Leveraging Blogging Activity on to Infer Demographics and Interests of Users for Advertising Purposes

Mihajlo Grbovic, Vladan Radosavljevic, Nemanja Djuric Narayan Bhamidipati, Ananth Nagarajan Yahoo Labs {mihajlo, vladan, nemanja}@yahoo-inc.com 701 First Ave, Sunnyvale, CA, USA

ABSTRACT 1. INTRODUCTION As one of the leading platforms for creative content, Tumblr In recent years, online social networks have evolved to be- offers advertisers a unique way of creating brand identity. come an important part of life for online users of all demo- Advertisers can tell their story through images, animation, graphic and socio-economic backgrounds. They allow users text, music, video and more, and they can promote that to easily stay in touch with their friends and family, discuss content by sponsoring it to appear as an advertisement in everyday events, or share their interests with other users the streams of Tumblr users. In this paper, we present a with the click of a button. Tumblr is one such social net- framework that enabled one of the key targeted advertis- work, representing one of the most popular and fastest grow- ing components for Tumblr, specifically, gender and interest ing networks on the web. Hundreds of millions of people targeting. We describe the main challenges involved in the around the world come every month to Tumblr to find, fol- development of the framework, which include the creation low, and share what they love. The Tumblr network is a gold of a ground truth for training gender prediction models, as mine of content, comprising of 200 million on different well as mapping Tumblr content to an interest taxonomy. topics such as travel, sports, and music, where 85 million For purposes of inferring user interests, we propose a novel user posts are published on a daily basis. This wealth of semi-supervised neural language model for categorization of user-generated data opens a great opportunity for advertis- Tumblr content (i.e., post tags and post keywords). The ers, allowing them to promote their products through high- model was trained on a large-scale data set consisting of 6.8 quality targeting campaigns to both visitors and blog billion user posts, with a very limited amount of categorized owners [16]. keywords, and was shown to have superior performance over The standard, prevalent form of advertising on Tumblr is the baseline models. We successfully deployed gender and through sponsored posts that appear alongside regular posts interest targeting capability in Yahoo production systems, in the user’s dashboard, the central page for a Tumblr user, delivering inference for users that covers more than 90% of displaying the newest posts of followed blogs in the form activities on Tumblr. Online performance results of a stream. This form of advertising, in which advertise- indicate advantages of the proposed approach, where we ob- ments resemble native content in the stream, is often referred served a 20% increase in user engagement with sponsored to as native advertising. Native advertisements are usually posts in comparison to untargeted campaigns. aesthetically beautiful and highly engaging, which typically makes them more enjoyable than regular display ads [4]. Tumblr launched its native advertising product in May of Categories and Subject Descriptors 2012. Since then, the number of advertisers (or brands) on H.2.8 [Database applications]: Data Mining the platform has grown steadily and reached a milestone of 100 advertisers in April of 2013. Moreover, most of the biggest global brands have used Tumblr to advertise and Keywords sponsored posts have generated billions of paid ad impres- sions since the launch of the Tumblr advertising product1. data mining; computational advertising; audience modeling; In this paper, we further enhance ad targeting that Tumblr algorithms offers, allowing advertisers to specify new demographic or interest categories. This improved targeting provides adver- Permission to make digital or hard copies of all or part of this work for personal or tisers with the control and flexibility to find the audience classroom use is granted without fee provided that copies are not made or distributed they most want to reach. for profit or commercial advantage and that copies bear this notice and the full cita- Copyright c 2016 held by author(s)/owner(s); copying permitted Building of interest targeting products on social and mi- tion on the first page. Copyrights for components of this work owned by others than only for private and academic purposes. croblogging platforms is an important research topic, dis- ACMPublished must be as honored. part of Abstracting the #Microposts2016 with credit is permitted. Workshop To copy proceedings, otherwise, or re- publish,available to post online on servers as CEUR or to redistribute Vol-1691 to (http://ceur-ws.org/Vol-1691 lists, requires prior specific permission) cussed previously by several researchers [11]. However, due and/or a fee. Request permissions from [email protected]. to its distinct characteristics, Tumblr poses novel challenges, KDD’15,#Microposts2016,August 10-13, Apr 2015, 11th, Sydney, 2016, NSW, Montréal, Australia. Canada. c 2015 ACM. ISBN 978-1-4503-3664-2/15/08 ...$15.00. 1 DOI: http://dx.doi.org/10.1145/2766XXX.XXXXXXX. www.comscore.com, accessed June 2015

#Microposts2016 6th Workshop on Making Sense of Microposts @WWW2016 · · · Figure 1: Examples of blog title (first line, bigger font) and blog description (bottom line, smaller font) which we explain in detail in this paper. In particular, the and follow, like and reblog actions. This data is publicly content and language used on Tumblr have distinct charac- available through Tumblr Firehose data source3. Other user teristics that needed to be accounted for during the mod- activities, such as user searches on Tumblr, which blogs they eling. Users often use tags to summarize the text in their visited and where they clicked, are considered to be sensitive posts. However, the language styles used in the tags and data and were not used in any way for the development of post text are different (e.g., the “hp” and the word the ad targeting models. “hp” have different meanings when they appear in the posts, “Harry Potter” and “HP company”, respectively). More- 2. RELATED WORK over, unlike the popular social platform , which Personalization is defined as“the ability to proactively tai- contains a large amount of social interactions but a limited lor products and product purchasing experiences to tastes of amount of content, or the platform , individual consumers based upon their personal and prefer- which contains an intermediate amount of social interac- ence information” [7], and it has become an important topic tion and content, Tumblr represents a unique combination in recent years. Personalization of online content for individ- of a rich and diverse content platform and a dynamic so- ual users may lead to improved user experience and directly cial network. To make use of this vast advertising potential, translate into financial gains for online businesses [14]. In we propose to classify user-generated Tumblr content into a addition, personalization fosters a stronger bond between standard multi-level general-interest taxonomy 2 that adver- customers and companies, and can help in increasing user tisers commonly use for defining their targeting campaigns, loyalty and retention [2]. For these reasons it has been rec- opening doors to high-quality audience segmentation and ognized as a strategic goal and is the focus of significant modeling for purposes of ad targeting. However, inferring research efforts of major internet companies [8, 12]. categories of user posts is a challenging task, given the huge We consider personalization through the domain of ad tar- quantities of unlabeled data being posted every day and the geting [9], where the task is to find the best matching ads very limited amount of labeled data, typically obtained by to be displayed for each individual user. This improves the human editorial efforts. To this end, we propose a novel user’s online experience (as only relevant and interesting ads semi-supervised neural language model, capable of jointly are shown) and can lead to increased revenue for the adver- learning embeddings of post keywords, post tags and cate- tisers (as users are more likely to click on the ad and make a gory representations in the same feature space. The neural purchase). Due to its large impact and many open research model was trained on a large-scale data set comprising of questions, targeted advertising has garnered significant in- 6.8 billion posts, with only a fraction of categorized content. terest from the machine learning community, as witnessed Targeting pipelines described in this paper are being used by a large number of recent workshops4. and publications to show ads to millions of users daily, and have substantially [5, 11]. improved Tumblr’s business metrics following the launch. One of the basic approaches in ad targeting is to target On our to developing targeting capabilities for Tum- users with ads based on their demographics, such as age or blr, we first created user profiles, based on users’ Tumblr gender. Historically, this approach has proven to work better activities that include publishing blog posts, following other than targeting random users. However, while for some prod- blogs, liking posts, and other. Lastly, we aimed at build- ucts this type of targeting may be sufficient (e.g., women’s ing and delivering both demographic and interest predictive makeup, women’s clothing, man’s razors, man’s clothing), models based on the created profiles. for others it is not effective enough and a more involved We note that the privacy of our users is of critical impor- profiling of users is required. A popular method in today’s tance. Therefore, we were constrained in regards to what ad targeting that addresses this issue is known as interest data we can use. Specifically, user profiles were created targeting, in which users are assigned interest categories, solely from data which users share publicly with others, in- such as “sports” or “travel”, based on their historical be- cluding contents of blog posts, blog titles and descriptions, havior [1]. Typically, a taxonomy is used to decide on the 2http://www.iab.net/QAGInitiative/overview/ 3gnip.com/sources/tumblr taxonomy 4www.targetad-workshop.net

3 #Microposts2016 6th Workshop on Making Sense of Microposts @WWW2016 · · · title

body

tags

like

reblog

Figure 3: Example of Tumblr blog post

Figure 2: Distribution of Tumblr post types created by a registered user is considered his or her primary blog. In addition, a very small portion of users maintain targeting categories, and a model is learned to categorize one or more secondary blogs. A Tumblr user is uniquely de- user activities and estimate their interest in each category. scribed by the blog ID of the primary blog, and throughout Interest targeting is known to build good brand awareness the paper we will use “blog” and “user” interchangeably. with relevant audience, which has already shown interest in Common user activities of Tumblr users include the fol- the corresponding category. In this paper we follow this in- lowing: 1) creating a post on one’s blog; 2) sharing a post terest targeting approach. Alternatively, advertisers may be created by another blog, called reblogging (a reblogged post interested going a step forward and optimizing for intent, will appear on the user’s blog); 3) liking a post by another typically done by assigning categories to actual ads, and blog; and 4) following another blog. Similar to Twitter, the training a machine learning model to estimate the probabil- follow connections at Tumblr are uni-directional. However, ity of an ad click in that category [17]. For each ad category unlike Twitter, users can create longer and richer content in a separate predictive model is trained, and evaluated on the the form of several post types, such as text, photo, quote, entire user population, with N users with the highest score link, chat, audio and video. The most popular types of blog selected for ad exposure. posts are photo posts and text posts, and, based on the To the best of our knowledge, the Tumblr analysis published in [18], together they cover more than has been considered by only a few scientific studies. In [3, 92% of all posts on Tumblr (see Figure 2). Any post type 15], the authors discuss the problem of blog recommenda- can be annotated with words starting with # that concisely tion, while in [6] they explore Tumblr social norms. How- describe the post and allow for easier browsing and search- ever, our work is the first paper that addresses ad targeting ing (called tags). Additional metadata that describes a post on Tumblr. includes photo captions in photo posts, post titles in text posts, and artists names in audio posts. An example post is shown in Figure 3. Tags, such as #gadgets or #tech, are 3. WHAT IS TUMBLR? displayed bellow the photo caption, while the buttons for re- 5 Tumblr is one of the most popular social blogging plat- blog and like actions are located in the bottom right corner. forms, where users can create and share posts with the fol- Lastly, each user has a dashboard (i.e., a feed of blog posts 6 lowers of their blogs. According to data from January 2015 , published by followed users, which is ordered in time), with there is a total of 221.6 million blogs on Tumblr, which more recent posts appearing at the top. jointly produced over 102.7 billion blog posts. With a large number of users signing up every day, Tumblr is currently 3.2 Advertising at Tumblr the fastest growing social platform7. Advertising on Tumblr is implemented through the mech- 3.1 User activities on Tumblr anism of sponsored (or promoted) posts shown in a user’s dashboard. This is similar to how advertising works on Twit- To register for a Tumblr account, a valid e-mail address is ter and Facebook. A sponsored post can be a video, an im- required, along with a primary username (which will become age, or simply a textual post containing an advertising mes- a part of the blog URL) and a confirmation of age. A Tumblr sage. In Figure 4, we show an example of a sponsored post blog resembles a webpage, with a profile picture, blog title and how it appears on web and mobile dashboards. Sim- and blog description appearing at the top (see Figure 1), ilarly to organic (or non-promoted) posts, sponsored posts followed by a stream of blog posts bellow. The first blog can propagate from user to user in the network by means of 5www.tumblr.com reblogs, and users can also “like” the promoted post. Both 6www.tumblr.com/about likes and reblogs can be seen as an explicit form of accep- 7http://t.co/3txHFRJreJ tance or endorsement of the advertising message. Moreover,

4 #Microposts2016 6th Workshop on Making Sense of Microposts @WWW2016 · · · BEAUTIFUL SPONSORED POSTS THAT ARE NATIVE, MOBILE (& WEB), AND EFFECTIVE Table 1: User data extracted from Tumblr Firehose YOUR CONTENT TAKES CENTER STAGE Declared Content Actions

• Interest, geo, gender targeting blog title post tags reblog blog description photo captions like • Choose from any format: GIFs, Video, still images text post title follow etc text post body audio post artists • Sold on a CPE basis so all you pay for is engagement

• Full reporting on earned impact generated by the user’s first name, age, and even declared interests (e.g., reblogging, liking, and organic discovery statements such as “fashion addict” or “I love football”).

• Sponsored Video Posts available with full video analytics and sold on a Cost Per View 4.2 Data Processing In order to improve the representation of user profiles, we • Paid syndication to Yahoo available to increase the propose to extract keywords from available blog informa- reach of your Sponsored Posts tion. This requires a certain amount of data preparation and processing. Given the extracted blog data, including the title, content and tags, we first removed all the html tags, followed by the removal of stopwords and the forma- tion of bigrams. It is common for certain words to appear Figure 4: Example of Tumblr sponsored post together more often than some others (e.g., words “credit” and “card”), and we aim to capture those bigrams and use them in keyword-based user profiles. To detect bigrams, we just like any other posts, sponsored posts are supplemented use a procedure that counts the unigram and bigram ap- with notes on who liked and reblogged the post. pearances, and for each combination of words wi and wj it Interestingly, while user-generated, organic posts are re- calculates the following score:

blogged on average 14 times, sponsored posts are reblogged count(wi, wj ) on average 10,000 times8. We have observed that 40% of score(wi, wj ) = . (4.1) count(wi)count(wj ) engagements with sponsored posts are reblogs, likes, or fol- lows. What is more, every fourth reblog of a sponsored Finally, bigrams with a score above a certain threshold were post results in 6 downstream reblogs from followers, leading extracted from the text in blog contents. Tags, on the other to content longevity, and one third of reblogs of sponsored hand, naturally form n-grams, and we extracted them in posts are present for 30 days or more after the initial post. their original form. 4.3 User profiles 4. TUMBLR DATA Available data sources were used to create user profiles. In In this section, we describe the data sources (user ac- particular, we extracted three distinct groups of user-related tivities and post contents) utilized to create user profiles. data: 1) declared; 2) content of posts; and 3) actions. The In particular, user activities included actions such as posts, specific components included in each of the data groups are likes, follows and reblogs, while post contents included tags, listed in Table 1. From each group we extracted features to the title and body for text posts, artist names from audio represent the users as described below. posts, as well as tags and captions for photo posts. Declared data consists of information which a user pro- vided during sign-up, including keywords from the blog title 4.1 Data sources and blog description, where keywords were extracted using Once signed in onto Tumblr, a user can follow other users’ the method in Section 4.2. To create user profiles we kept blogs. The follow action is one-directional as it does not re- the most frequent keywords from blog titles and descrip- quire the follow back. For the purpose of this study, we tions, after removing stopwords such as “a”, “the”, “where”, collected a sub-graph which contained 96.9 million unique “in”. We counted the keyword frequency in a user’s blog title nodes (i.e., users), 5.1 billion edges (i.e., follows), out of and description, and stored the count along with the time which 36.4 million are bi-directional (18.2 million pairs of stamp of the latest log-in as a part of the user profile. users that follow each other). The data set included more Content features were formed from the textual contents than 26.1 billion activities on Tumblr. As discussed ear- of posts which a user either created or reblogged. The main lier, an activity log is available through a called content feature types included: 1) post tags; 2) keywords Firehose. from the post title and body; 3) keywords from photo post To create user profiles for targeting, textual contents of all captions; and 4) artist names from audio posts. Tags in posts were collected, including photo captions, tags, titles posts were not tokenized, instead they were used in the form and bodies. In addition, every time a user performs a post they appear, e.g., tag “food for a vegan” was one keyword. or reblog activity, Firehose lists the user’s blog title and On the other hand, in order to extract keywords from text blog description, which we also used to represent a user. appearing in text post content, we again used the method As we can see in Figure 1, a blog title and description often from Section 4.2. We kept only the most frequently occur- provide useful information with respect to targeting, such as ring keywords, excluding stopwords. In addition, we used the most popular artist names as keywords. In this way, 8http://yhoo.it/1vFfIAc we collected several millions of distinct keywords that were

5 #Microposts2016 6th Workshop on Making Sense of Microposts @WWW2016 · · · tags within a single post tags within a single post

tj-n … tj-1 tj+1 … tj+n tj-n … tj-1 tj+1 … tj+n

Projecon Projecon

tj tj c1 … ck j-th tag j-th tag j-th tag categories

Figure 5: Unsupervised skip-gram model Figure 6: Semi-supervised skip-gram model

ing platform for native advertising9. The GIT is carefully used to obtain rich representation of user profiles. To il- derived based on Interactive Advertising Bureau (IAB) tax- lustrate content keyword extraction from our dataset, con- onomy recommendations, in order to meet advertiser needs sider that user u at time stamp t used tag #hp five times i and protect Yahoo’s interests. The GIT has a two-level hi- and tag #nba eight times, keyword football two times in erarchical structure, such that advertisers can adjust the post titles and posted an audio post with a song from artist audience reach by utilizing broader or narrower interest cat- Shakira ten times, then the resulting user profile would be: egories. The top level of the taxonomy contains 23 nodes u = tag : #hp, t : 5, #nba, t : 8 , title : football, t : i (e.g., “Automotive”, “Business”, “Pets”, “Travel”), while the 2 , artist{ : shakira,{ t : 10 . } { second level contains 130 nodes which represent more precise }Action{ features include}} follows, likes, and reblogs. If interests (e.g., “Automotive/SUV”, “Automotive/Luxury”, user u follows user u at time stamp t, we create an indi- i j “Pets/Dogs”). cator feature follows : j, t : 1 and add it to the ui’s user { } profile. Similarly, if user ui likes or reblogs user uj ’s post, we create a feature that keeps record of the number of likes 5.2 Proposed semi-supervised classification and reblogs n, as likes : j, t : m , reblogs : j, t : n , In this section, we present a novel classification approach respectively, and update the{ user profile} accordingly.{ } based on the recently proposed skip-gram model [13], which The described approach resulted in user profiles for a total is used to categorize keywords into the GIT taxonomy. For of 81.8 million users. The total number of unique features conciseness, we describe the proposed model on the assump- was 1.4 million, and an average user had 379.9 non-zero fea- tion that it is applied to tag categorization. However, we tures. Most of the features described above are represented used the same methodology for categorization of keywords as either binary indicators or counts of occurrences. originating from blog titles, descriptions and text, audio and image posts. We consider the task of tag classification, where the goal 5. INTEREST PREDICTION is to classify tags into a pre-defined taxonomy of interest categories. In order to address this problem, we propose The goal of our work is to infer user demographics (de- to learn tag representation in a low-dimensional space using scribed in the following section) and identify user groups neural language models that are applied to historical Tum- with interests in certain topics, such as music, travel, cook- blr posts. Let us assume that we are given N posts. In the ing or books, in order to allow advertisers to target seg- post logs found in Firehose, every post p is recorded along mented Tumblr audiences. As the topics may be defined at i with the tags t , j = 1...M. We collected data in the form various levels of granularity, to avoid sparsity problems while ij p = t , j = 1...M , where M represents the number of still providing useful and actionable interest categories, user i ij i i tags in{ the i-th post.} Given the data set = N p , the interests are often classified into a pre-determined hierarchi- i=1 i objective is to find a representation of tagsD in which∪ seman- cal interest taxonomy that the advertisers commonly use. tically similar tags are nearby in the representation space. However, to be able to create effective user interest classi- For this purpose, we extend ideas originating from recently fiers, one requires a sufficient amount of labeled data. Yet, proposed language models, as described in the remainder of for the problem of the scale of Tumblr interest prediction, this section. this can be a daunting task for human editors. For that The skip-gram (SG) model involves learning represen- reason we propose a novel semi-supervised classification ap- tations of tags in a low-dimensional space from post logs in proach, based on the recently proposed word2vec model [13], an unsupervised fashion, by using the notion of a blog post which efficiently and seamlessly makes use of large amounts as a “sentence” and the tags within the post as “words”, of unlabeled and a limited amount of editorially labeled data borrowing the terminology from the Natural Language Pro- for learning effective content classifiers. cessing (NLP) domain (see Figure 5). Tag representations using the skip-gram model [13] are learned by maximizing 5.1 User interest taxonomy the objective function over the entire set of blog posts, We decided to classify keywords into the General Inter- D est Taxonomy (GIT), used by the Yahoo Gemini advertis- 9gemini.yahoo.com

6 #Microposts2016 6th Workshop on Making Sense of Microposts @WWW2016 · · ·

Table 2: Examples of categorized tags anastasiabeverlyhills green_ dress makeuplover maccosmetics dahliawolf fashionable motivescosmetics face_ makeup blackdress makeup_ brushes black_ lipstick wedding_ dress

makeup_ blog smokeyeye smokey_eyes lip_balm little_black_dress heels bustier chic

red_ lipstick embellished Tag Category maquiagem haircare eye_ shadow bblogger mesh beautiful_ eye_ shadow bath_ body_ products younique beauty_ guru cocktail_dress natural_ makeup eye_ brows summer_dress maybelline lip_care beautyblog cateye lip_treatments bridal_ gown eye_ beauty prom_ dress instabeauty smokey_eye smokey jumpsuit music Arts and Entertainment/Music makeup_ ideas baby_ lips lime_crime white_ dress black_ heels backless_ dress bride

marykay pout stilettos smoky sheer sugarpill nails nars palettes cosmeticsmotives nail_ polish beautyguru lace_dress fashion Style and Fashion lip lips sundress rhinestones

bronzerbeauty white_ lace strapless ruffle beaded cut_out eye_ makeup fotd benefit beading bridal

sleek pretty_ eye_ shadow smashbox cat_eyes eye guru makeupartist dark_ lips lashes flower_ print sparkle eyes eau_de_perfume prom

swatches corset long_prom_dresses skincare sequin_dress casual red_ lips

song Arts and Entertainment/Music hairstyle makeupforeverlip_makeup polka_ dot minidressrufflesblue_ dress lipgloss cutout tarte lace kat_ von_ d mascara long_skirt

foundation tulle red_ lip makeupaddict clothes rimmel party_ dress beautyblogger redlips modcloth

mua powder peplum art Arts and Entertainment blush naked3 evening_ dress elegance bourjois weddingdress fashion_ dress eyeshadow meggy_ grace nail_ varnish motdcosmetology cateyes open_back palette lip_treatment revlon beautiful_ dress sparkly yellow_ dress haul mask disney Arts and Entertainment/Movies purple_ dress liner formal blouse

eotd avon bag lips_makeup

brows maxi skin_care pink_ dress bigeyesmakeup drugstore greeneyes glitter nail_ care instamakeup vegasnay pinklips contacts mac redlipstick makeover partydress lorac make_ up straps ilovemakeup backlessdresses lash babylips sequin style Style and Fashion lorealmac_ cosmetics bbloggers eye_ lashes makeup_ artist fahion pearls

facepaint lipbalm makeup_ tips long_dress elegant eye_ liner

lipstick eyeliner brown_ eyes makeup_ brands sexy_eye_shadow frilly hair gown flowy contour beautytips pink_ lips clutch lancome sheer_dress bridesmaid_ dresses

glam beauty_ products gowns photography Hobbies/Photography pink_ lipstick cocktail_dresses lip_gloss_makeup beatface lip_stick floral_ dress lipsticks sephora covergirl rubber eyebrow illamasqua brushes shimmer bridesmaid nyx beauty_ blog ipsy urbandecay chiffon skirtdressy couture eyebrows lip_gloss maquillaje makeupjunkie black_ dress teen wolf Arts and Entertainment/TV cosmetic birchbox best_ makeup bodycon beauty_ blogger gloss eye_ shadows sequins red_ dress too_ faced concealer limecrime veil stylish ball_ gown wedding_ gown

moisturizing_lipstick cute_dress winged_ eyeliner cat_eye pleats peter_ pan_ collar

meggy_ grace_ darcyeyemakeup maxi_ dress evening_ gown food Food and Drink eyelashes fashion_and_dressing_right wheretoget highlighter makeup_ tutorial prom_ dresses sexy_dress wildoesthings short_dress makeupoftheday vintage_ dress beauty tips wedding gowns defined as Figure 7: Nearest neighbors of tags: a) #makeup; b) #dress

kg_cooks matchaholic reese039s ajcinhochiminh oatmeal_with_a_fork lovely_recipes sweets_and_desserts dessertcafe homemadepancakes fruitandnut blogkulinarny pecan_nuts omonomnom cherry_chocolate quick_snacksdonutsssss dessert_blog motherfucking_cheesecake sweets_for_youraspberry_macarons applepopbakery amazingcupcakes pumpkin_donuts soforkingyummy tart_tatin bakedwondersemporium abc039s_the_chew bakingwithtanyaburr i_really_want_some_right_now vegan_muffin cinnamon_crunch kakaroonsandco sweetesttooth culturecuisine fruit_jam potato_bake thesweetstuff dairy_free_chocolate erminjonathanfood avocado_pudding when_i_couldnt_taste_i_would_put_sprinkles_on_basically_everything_to_make_the_textures_more_interesting_and_bearable marshmallow_fondant buttermilk_waffles cavegirlcuisinecom gluten_free_banana_bread breadfernbakery epicuriosity kias_one_true_love pumpkin_cinnamon_rolls carrot_cake holiday_candy oldrecipe my_dream_breakfast mochi_donuts chocochipcookies pinkfrosting garlic_breadsticks cadberry confectioners cookieandcream red_velvet_brownies cinnamon_raisin designercake cake_rolls ihgfoodie baking_substitutions cake_icing_recipe candy_every_day things_i_need_to_cook unsweetened_coconut marmellade valpovelveticecream breaktfast mint_syrup sweetslover naturalyeast orangecheesecake iced_gems giant_cake canadian_recipes sugar_cone naughtyfood guglhupf breakfadt lemon_extract hazlenuts vegan_cupcakes_take_over_the_world oatmealchocolatechip cookingwithsogodlike puffed_rice soft_fruit more_food_porn pankake caramel_cake_recipe instacolumbus butter_icing blendsbyorly kid_friendly_recipe original_glazed fuckingrecipe non_dairy_milk sweettoothsunday confectioners_sugar crocker_confections ilovegreatcoffee sandwiches_recipes cinnamon_latte strawberris diary_of_a_stress_eater im_so_hongrymississippi_mud_pie cream_pan dessertagram vegan_pinterest peanut_butter_oreos nom_food peppermint_patties wippedcream cranberry_pie meredith_video_studios_carson_s_kitchen_826 nutella_sandwich charleighsveganchallenge 84th_and_3rd its_my_birthday_ill_queue_if_i_want_to nommmmmmm hocus_pocus_buns food_rec eclaires dark_chocolate_cake tinkas_cafe national_chocolate_chip_cookie_day realmenbakemomskitchen homemadecheesecake cookie_exchange vegan_parfait cheesecakelove love_baking mangococonut italian_bakery ilove2bake4you cakeroll dark_chocolate_bar the_candid_appetite taken_with_t2i = log (t t ). 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foodofat dessertrecipes cutiepiefoods caramelized_banana officesnacks chocolate_chip_cupcakes shutterbean bacon_cupcakes pretzel_rolls foodie_eating_eat

banoffiepie pb_chocolate chocolate_mousse_pie cirque_du_plum no−knead_bread mini_desserts kit_kat_cake fig_cake dessertrecipe

flourless_cake cake_sweet (t t ) = , (5.2) yuuummy happy_bread halfbakedharvest bakerella smittenkitchen foodofme j+m j iisjongeats cup_of_cake caramelcheesecake bellesucre korean_cake reicpes ice_cream_waffle cheessecake mug_cake_recipe double_chocolate_chip_cookies xxx−food−xxx moistchocolatecake muse_cravings chocolate_crinkle_cookies chocolaate damndelicious recioe sometimes_im_a_food_blog recieps

secret_lovely_things_shhh cakes_and_pies cookie_bites pastry_porn jymmy P best_cupcakes chocolate_flakes vegan_caramel

moaintlfoodfest chocolae blueburry paul_bakery peanutbutter_cookies delicioud choc_chip_cookie my_favorite_cake deliciously_beautiful sucre_glace T nomsies tiny_pies silk_pie nutellaicecream toffee_sauce triple_chocolate_brownies patiserrie

wafer_cookies zimtsterne homemade_donuts greek_yougurt red_velvet_bars just−bake icecreamcakes peanut_buttercup banoffe choco_sauce nutellahotchocolate whipped_ricotta amcbites coffee caramel_syrup cinabon q_rico tasty_treats_tuesday callmecupcake smore_cupcakes affagato livefooddie ccandtreats chcolate dessert_drinks terminatefood rainbow_recipe bakingporn chocolateguru reblog_recipe donutsglazed lotolles_favorite_food sweet_baking swift_food party_treats siftandwhisk jibbynco resees reicpe macarontower french_madeleines chokolad vinalla

mochilato starbucks_cupcakes

food_i_need_to_make graham_crust novelty_cakes other_desserts icyedits trufflelata frosted_cookies cheesecake_bites foodbully meals_to_make bakerita

food_i_want_in_my_life theme_indulge cupcake_jemma sweet_desserts vegan_rolls pinchofyum sprinkle_porn bunsinmyoven hot_fudge_sauce miomii chcoolate chocolate_custard nobakecheesecake stickytoffee peanutbuttericecream | there_are_hungry_ghosts ricoa oreo food_that_looks_like_other_food avanillarue blackerry cheesecake_recipes chocalte strawberry_cream_cake pecan_bars efood banana_bites salted_caramel_apple_snickers_cake strawberry_muffin muddie_buddies poachedpears panacota caramel_slice kaspas parsley_sage_and_sweet lemon_bread exp(v v ) plated_desserts pudding_recipe swiss_meringue skillet_brownie donuts_rezept > 0 vegan_valentine chocolate−covered_strawberries homura_dont_look pvcreamery french_fancies pop_cakes whitechoc milkfroth white_chocolat yummy_cookies t t beurre_sale pinata_cake foodie_diary chocoalate frugal_antics dainty_pastries glica sprinkles t=1 gemmas_baking_blog cookie_dough_dip chocolate_chip_cookie_cake desserycreme_filled j snickers_cheesecake cheesecake_cupcakes buiscit olla_podrida foodpornarmy cakes_pies mont_blanc_cake misc_sweets ice_creamvanilla resipe brownie_ice_creamweekendbaking revelbars ice_box_cake white_choc heart_sprinkles mug_brownies marshmallow_cream seasonal_or_holiday spudnuts cupncake breakfast_dessert strawberry_food side_dish_recipe chocolate_filled ferrero_rochers berakfast alice_medrich baked_doughnuts i_want_this_in_my_belly perfect_snack

bavariancream savoiardi bakegoods chocolatecake lifes_simple_measures chocolate_chip_brownies nessykitchen dessertlove

dessert_tag chesscake food_port mini_apple_pies red_velvet_cheesecake_brownies lucythebaker creme_brulee_cheesecake nathomemade chocolatelava ice_cream_cookie dressert

scotcharoos 85degree tejalskitchen kremal fudge_bars matcha_cookies lucamontersino thepassionateculina classic_pastry sugarandcinnamon oc_food foodport cookie_dough_bars food_food_porn dessertpics eggnog_french_toast mangomousse funfetti_cupcakes coconut_bars cinder_toffee citrus_salad dark_chocolate_cookies gram_cracker potdecreme happy_donuts_day food_i_like_to_look_at redcake warm_bread voiceacting_crud sugahrainbows r_breakfastmscupcake foodie_funday_sunday raspberrycake swirl_bread apple_cupcakes food_are bakewithyeemi shortbread_cookie caramel_cupcakes uchicafe creamnco lemonpoppyseed snackrecipe organicpie moose_treats cinnamon_scroll pralin cramel loafcake salted_carmelhot_chokolate cakevanilla sweet_snacks whitechocolateganache rice_chex loudstellar bacon_cookies fudge_sauce creative_cakelove_pancakes ice_cream_floats apple_pie_filling summer_pie swiss_meringue_buttercream cjocolate cold_desserts pretty_desserts behind_the_sandwich chocolatetart nbaking bananacupcakes addapinch cake_cookies chocolate_pound_cake gourmandeinthekitchen browies vegan_cookie_dough cucakes cookie_pie sonjas chocolate_covered_potato_chips refcake malt_balls dessets bakery_quality salted_almond cookie_dough_recipe sweet_salty cinnamon_roll_recipe chocolate_orange_cake cakw chapcakes shameless_addict carmensbest oreo_butter reipe blueberry_bread deliciious carissa_feed_me oat_bars sticky_date theseasidebaker chocolate_banana_bread peanut_butter_brownie cake_merchant shortbreads top_with_cinnamon baking_advice gustogelato why_not_food moltenchocolatecake lemon_poppy_seed_muffins cinnamon_donuts healthified biscuit_cake no_bake_recipes hot_cocoas dukindonuts pareve

toping glazure

vsdough pretzelbread layer_cakes baking_with_peeta nom_mom chai_ice_cream getdrop brownie_bars bakingdom patisserierusselia food_ive_made yummy_yums veggieful peanut_butter_recipe cake_doughnuts heath_bars bananachocolate mug_cookie eurekamt wjlte vanilla_cheesecake cakes_and_slices chocolate_eclairs icecreamsandwhich luxbite flax_egg saveyellowduck nitrolab choc−chip deliceous ice_cream_taco nutella_to_go choco_cookies desseet 0 easy_pie frosted_cake

P chocchip where v and v are the input and output vector representa- meltyblend yummy_looking_stuff malted no_churn kitchen_trials t foodrecipeshq halakaon chaykof coldfood mybakes oreodonut t mint_chocolate_chips mamiaoyagi jilanis cakedecorater 7_layer mini_oreos jam_and_cream purple_oven cake_blogs kitchendivas foodrp0001 clafouti desssert yestoyum bake bakingwithbooze dessertforlunch ice_creamv − shortbread stickydatepudding chocolate_cup caskes easter_recipe cheese_cream chocolate_popcorn eatngreet girl_cakes no autumn_desserts dessert cinnamon_streusel lavender_cupcakes alpen_gold cookie_dough_truffles plated_dessert sprinkle_bakes laurenslatestnjamii spring_does_recipes foodpon omnomm unbleachedwhiteflour candy_recipes cookies_recipe greedywolf cookie_shot cherrycake brownie_a_la_mode cookie_a_day chocolate_log_blog happilybalanced popsicle_recipes maplebar culinaryconcoctionsbypeabody sodkie_kakao recees lasagna_cups kaysfoodfotos raspberry_and_chocolate fudgy_brownies corn_cake douhgnut american_cookies quick_easy chocolateshavings dessert_lunch dssert cookies_dough milk_with_chocolate chocolate_layer_cake yumm_food fave_candy yums_and_loves fod_porn bakedgoodness for_my_butler_cafe foods_kawaii chococo healthy_ish breakfasr homemademacarons apple_cinnamon_pancakes creme_egg_browniesr_entreecakiessnickers_cupcakes stuffed_cookies salted_caramel_milkshake heaveninajar twix_bars churn nutella_frosting dessertpic nobakecookies vanilla_creme grilled_banana salted_caramel_popcorn tassies uitimate_food_porn cheesecake_dip wsprinklesmarathon_food verbthefood mcennedy peartart mango_mousse

− chocolate_raspberry_cake homemadefrosting fudge_friday brownie cake_store best_brownies peanutbutterblossoms chocolate_shell misc_dinner cheese_tart pumpkin_2014 chispas_de_chocolate hummingbirdhigh main_food mias_domain fodporn stuffed_strawberries ice_cream_pie dogoodwithfood christmas_tree_cupcakes youwillbehungry eggnog_pancakes chocolatefoood carb_heaven wagasgi tworedbowls

pumpkindonut no tions of tag t of user-specified dimensionality d, n defines the hluizstyle chocolate_peanut_butter_chip_recipe cherry_pie_filling yum_d seven_layer_bars venus_is_mecaramelsyrup food_porn_pics sweet_cocoa strawberried waffle_ice_cream loaf_cakedeseert chocolate_barks peanutbutterchocolatechip fp695 sweet_pretzels food_delicious_ymmm bicuit smores_cupcake food_tut cheese_cakeplain tim_vidra no_bake_cheesecake krespykreme espresso_powder inatartshell food_forn major_cravings_now cake_bites layered_dessert walffles savory_dish happybaker bread_experience jernejkitchen darkchocolatechips chocolate_doughnut sweetie−piesyummyblog joyofbaking cheryls artisanbreads rose_frosting chocolate_ice−cream chocolate_wafer veggie_desserts carrot_cupcakes crema_batida cooking_queue cream_cheese_brownies food_pon snickerdoodle_cookiescookie_bowl chocolate_cupschocozona everybody_loves_to_eat nutella_stuffed bleuchocolat cakec lagodons_shop fiorgelato bake_later to_cook_list ice_cream_cookies parfe chocolate maple_syrup_cake sortedeyecandy green_tea_flavor quickbread red_bean_ice_cream mousefoodbakes slutty_brownie saintbait omnomom baked_onion french_crullers donut_muffinspumpkin_chocolate_chip non−recipe autumn_flavors baked_cheesecake sidecar_doughnuts evil_ice_cream mybaking cookieporn oreo_fudge malted_chocolate reipes custards jar_cake cookie_dough_cupcakes im_getting_fat_just_looking_at_this 6787199279 red_velvet_recipes no_churn_ice_cream dessert_gif turtlepie focaccia_bread shugarysweets ichigo_mochi worthgettingfat c1food cake_truffles yorgut other_sweets semisweet_chocolate candied_fruit checkerboard_cake strawberry_donuts xxxfoodxxx peach_crumble delicipus choclate_cake chocolate_sticks mini_bolo dulce_de_leche_brownies caramel_recipes chocolate_gelato pate_de_fruit choco_cake apple_galette buttercream_roses savory_bread waffleslayer bananaloaf chocolateaddiction valentinescake double_chocolate_cookies deliicous misc_desserts bakeitrain candy_melts butterfly_cakes jcocebu dark_chocolate_chip food_donuts whipped_coconut_cream dessert_roll multiple_recipes kitchen_shit choco_pops length of the context for tag sequences, and T is the number sugar_donuts fall_desserts pumpkin_scone cook_later bakng berrry mounds_bar chocolate_scones summer_cake butterstudio crunch_cake chocolatem mms_cookies chocolate_roll lick_the_spoon katy_cooksapple_pancake recipe_food love_to_bake cookieschocolatefoodcom pink_foods recipes_i_need_to_try chocolate_rolls maple_glaze mini_nutella foodasm cookie_batter victoria_sponge_cake lauren_bakes chocolate_drinks chocolated ice_cream_topping coffe_cake om_noms_favorites caramel_apple_pie igsnack icecream_cones caramel_brownie cookeis autumn_cookies ice_cream_recipes holiday_cake chocolate_hazelnut_spread lumberjacobmartin3 gimme_some_oven coconut_chocolate recipechocolate sweetgasm warm_dessert eatuntildieweekend_baking cake_batter_truffles fay_da_bakery breafkast starbucks_ice_cream makaroniki happy_marshmallow_day crepe_cakes sweets_recipe banana_chocolate strawberry_sauce butterscotch_sauce cinnamon_chips deliciouss zucre chelseasmessyapron yuuuummy gotta_make_these roses_cookies chocogasm fruit_sorbet food_decorating love_cupcakes choco_chip_cookies thingsicooked flourless_cookies tiramisu_cheesecake ferero thecookiewriter chiffon_cake wltr cookiesganache cranberry_cake world_nutella_day mint_cookies dark_food jamoca cake_doughnut nama_chocolate mm_s rainbow_jimmies cherry_crumble decadent_dessert mini_dessertiksf coffee_cookies smores_cheesecake stressbaking

oreo_crust chocolate_pecan_pie cookie_cakes delsih pecan_cookies soft_bakedcookiesbars morellis ice_cream_chocolate white_chocolate_ganache chovolate efooddiary chocolatemudcake gofr ice_cream_sammich oreo_mini cake_bar delcicious loveandoliveoilpecan_tart sakura_cake cookies_brownies yogurt_pops chocolate_chunk_cookies cakeblog foodporb peanut_butter_bars chad_robertson chocolate_crepe summer_baking reciperunner oreo_recipes pastries_recipe with_nuts i_should_make_this_sometime icingcookies pretzel_dogs fuwa_edit crumb_topping checkerboard_cookies bombolini sundea receip ambitious_kitchen kugelhopf browines chocolate_chunks kittykattcooks theyseemerollinandbakin urbantaphouse cupcake_blog green_frosting averiecooks seeded_bread nutella_bread basbousa caramel_chocolate fruit_crumble chocolate_pastry coconut_cupcakes japanesecheesecake cateringdesserts of unique tags in the vocabulary. From equation (5.2) we see cupcake_decorations oh_my_gosh_yum ask_about_our_vegan_options peerie_shop_cafe kolace chocolatebrownies benandjerries handpie salted_caramel ice_cream_cookie_sandwich cukays pretty_dessert

to_eat_list vanilla_paste fudge_recipes recioes recipes_included bear_cookies

ebelskivers chocolate_fondant olive_oil_cake green_tea_cookies choceur yumn these_were_delicious scotcheroosmacaron_recipe sweet_eats lauravitale baking_from_scratch cinnamon_cookies cheesecake_bar pastry_recipes coffee_cakes red_velvet_donut foodsnaps mint_chip_ice_cream chocloate lime_cake cacao_nib rynagomakan baked_foods turtle_cookies chocolate_chip_cake ickfd sweetreat heath_bar petite_fours banana_cookies cakeinacup chip_cookies rice_bubbles chocofudge crinkle_cookies best_desserts vegan_frosting stuffed_bread oreo_frosting frosting white_chocolate_cheesecake atsunrice tonsraku cake_chocolate triffles doughuts chocololate donadussifoodfood_making oreo_ice_cream_sandwich granitas choco_cream sweets mabels_favorite_foods raw_cookies

tortina picnmix matchacake mini_chocolate_chip_cookies faydalicious donutsp cheese_cakep chocolaye work_snacks pumpkin_spice_smoothie i_love_matcha

chocolate_desserts mentally_obese cake cookiesandcups meringue_pie and_into_my_belly chocolatebuttons cake_foodsmores_pie crystallized_ginger chocolatemouse

chocate simplecookingchannel roclets lemon_macarons ideasinfood cream_coffee blueberry_buckle doublechoc buttericing smore_cookies devil_food sprinklesbakes jamroll thehungryhood chocolate_mousse_cake things_i_bake best_chocolate_in_the_world baking bicuits chocolate_chip_recipe truffle_cake browine snickers_bars breakfast_bake red_cupcake sick_eats iambaker cheesecakestuffedstrawberries things_at_the_cafe apinchadash vegan_whipped_cream br31 chocolate_bundt_cake pieinajar strabwerry chef_delvi jelly_worms snickers_ice_cream vegetarian_dessert bakefromscratch mypastries sunshinekitchen

recepi almondcheesecakecookies apple_pie_cookies brownie_cake cheftiffy peanut_butter_moussecafeuk colorful_cupcakes tripledarkchocolate castor_sugar malt_powder caramel_frosting cakeball screwpine bunt_cake soybean_milk food_photograpy vegan_dessert_recipes peanutbutterily kare_bakes pokecake fruitpie oatmealcookie girlyfood

mars_kitchen choclate_chip_cookies craivng cereal_treats dulche_de_leche chocolate_covered_fruit pecan_cake peacan small_cake hintofvanilla italian_cake icecake my_bakes fudgeebar cake_inspirations boilingbad pbs−food muffin_recipe chocolate_fingers chocolate_mouse cinnamon_recipes caramel_cookies chioccolate pretzel_recipe chocolate_hazelnut that tags that co-occur often and tags with similar contexts hot_chcolate doughnuts marvelous_sustenance when_im_bored_i_reblog_food pumpkin_chocolate_chip_muffins moelleux_au_chocolat

abrowntable vegan_popsicles coffee_flavor doughnut_recipes cakes_pies_etc chocolate_dessert chocchipcookies my_cookery desserts individual_desserts ricotta_cheesecake jar_cakes oh_my_god_this_looks_delicious strawberry_recipe tomato_tart gnum cookies_p stout_cake kitkat_cake themoonblushbaker chocolateonchocolate frosted_animal_cookies fancy_dessert fruit_desserts pretzel_roll passion4baking ymmmy cake_bars

oreo_dessert chipcoco_upload black_icecream chocoate cafeblue bake_goods vanillafrosting lemon_glaze kitchen_dabbling dessert_in_a_jar cremecheese yomaro islandcreamery bakings rilakkuma_cake chocolate_roulade raspberry_recipes assemblycoffee food_for_entertaining whippingcream leanne_feeds_herself sticky_rolls cold_treatpeanut_butter_cupcakes recipegirl pumpkin_brownies moltenlavacake feedingmagpies chocolatedrizzle fooods canbake cook_bake_eat oreo_truffles 2_egg_whites deliious chococolate vgan krispie_treats poshenglamgoodies jam_cookies i_am_in_with_donuts foodorexia deliciouslyyum food−por−favor avocado_chocolate_pudding caramel_tart

cakes_decorations tiramisu recipeb donut_cake banana_foster original_cupcakes sweetplantations

chocolatebark bake_list ohtheplaces donugh mini_cheesecake millionaireshortbread iceing cookierecipe banana_chocolate_chip caliebakery iloveonfood sse14 ice_creamconecakepic breakfast_bread maracons cokies tv_gbbo mint_recipes pops_recipes i_want_to_try_these_so_bad chocolateroll easy_cake gateuxchocolate_torte

bakers_royale bread_buns chocoholic food_in_a_cup brownies eat_brekkie find_recipe traybake savory_pies

the_caked_crusader recipe_tag_chocolate kronut foooddddd yummysweet jezbakes

cookiebars donuts_make_me_go_nuts falldrink dragon_noms cookie_ice_cream oreo_bars savoury_pie fins_not_here choppingblocknyc chocolatecandies french_baking bakedecadence adams_best kitchenesse yummmi dough_balls resberry gallettasdecoradas

victoria_bc_hotel cake_mix_cookies chocolatcake salted_caramel_brownies mint_brownies 8fifteen vanillaflaca maple_bar buttermilk_recipes apple_cinnamon_rolls samoa_cookies multivarka ice_cream_pops thefoodshow rawtella cookie_sandwiches cinnimon_roll nussecken strawberry_glaze baked_food chocolate_chip_cookie_bars pankackes howtofood marhsmallows chocolatecream civico8 celebration_cake traceysculinaryadventures hazelnut_cake smores_brownies haselnut kirbiecravings manifest_vegan recipe_tag_cookies candymaking yum_omfg

no_bake_recipe deluxe_bakery mini_pie imahkopi lovetobake thanksgiving_desserts sliceofcake mugbrownie cedarridgecafe blacksambo pb_cookies need_to_cook sugar_free_recipe (i.e., with similar neighboring tags) will have similar vector winter_baking brekafast darkfood deilicious giantcookies caramela recipe_dessert recipe_archive chocolate_marshmallow pankcake cendiesseasons_and_suppers cheezecake pastisserie egg_muffin booja_booja

revelbar choux_creme cake_batter_ice_cream smores_cake pear_cake oreo_cheesecake_bars cold_dessertchococaramel biscoff_cookies jenisbook cheesecake_cookies craftandcake chcoclate vegan_treat white_chocolate_macadamia_nut

newyorkcheesecake caramel_recipe apple_cider_caramels luvju browniesc homemade_candy milk_bread living_the_vegan_life nomnomnom_b pumpkin_pie_recipe

kit_kay double_chocolate_banana_bread nhummy scons chocolate_caramel i_would_like_to_eat_this_now halloween_bark reciple choc_mint raspberry_coulis ivyplace cafefood pastries french_cruller our_recipes foodr lovely_dessert grandpas_dinner edible_perspective iceceam milkaoreo

cupackes apple_pie_bites browniecupcake chouxpastry recepe bakingsunday

cheesecream dessertfortwo cold_chocolate dessertfirstgirl bakingtips the_best_cookies sweetapolita chocolate_buttercream bronutsph dessert_nachos pumpkin_loaf oatmeal_bars cookie_dough_cheesecake open_face_sandwich jam_doughnuts brownieschocolate choclet peanut_putter mufins floral_cupcakes pie_pops avocado_ice_cream this_is_my_pornography moltenchocolate

foodpornphilippines multigrain_bread nanasgreentea boston_cream sweet_snack r_dessert baking_challenge chocoloungerd cupcake_bouquet pure_food_porn pumpkin_rolls cheesecake_pie donutsplain small_cakes lolascafe

around_the_kitchen mandazi cinamon_roll dessert_and_food foodsection hand_pie pastel_ice_cream chocolatefruit wimbly_lu scrumtious caramelslice food_sweets_sweet miniegg autolyse sweetdelight chocolate_bar_cake turtlecheesecake posypka chocolate_chip_cookie_dough_truffles chocolatetruffles butteredup pause_chocolats nutella_and_banana ferdakost helloemmasfood graham_cracker_crumbs chocolate_tart becky_cooks chocolatechipmuffins mocha_cake fructs

blueberry_recipes peanut_butter_frosting 35food lattte sonothealthy keeruzs_noms touslejours caramelbeurresale poached_pears gonna_cook_this cupcakes_recipe cake_vs_brownies cooking_with_friend chocolatebuttercream mad_marks delicious_looking_food oreo_ice_cream_cake chocolite icemilk i_eat_nutellavegan_banana_bread

thekitchenmccabe chocchips food_foodporn fatayer chocolate_chips picnic_recipes mini_cinnamon_rolls fopd prestat coconut_whipped_cream recipec chocolate_au_pain candy_bark cheesecake_brownie banoffi belgiumchocolategourmet littleandfriday totoro_cake milk_and_cookie_shots italian_dessert blondiebrownie

ferrero_rocher_cake vegan_chocolate_mousse chocolatebread easy_cookies browneyedbaker

cinnamon_raisin_bread sweet_pastries

buttermilk_pie vegan_brownie pumpkin_spice_cupcakes super_easy_mode strawberrries heart_shaped_waffles bakingideas adorable_foods peanut_butter_chocolate_chip_cookies omg_yummmm pumpkinicecream milshakes toddys berry_crisp life_love_and_sugar yuummy homemade_chocolate my_food_obsession waffle_sandwiches dont_blog_hungry food_by_d cookie_dough_frosting pecan_pie_bars holidaycupcake valentines_day_dessert cinamon_rolls strawberry_cheescake lolacafe asian_cake coconut_ice

vkny lactose_free_recipe lejoog i_wash_you_dry greenteachocolate lemondrizzlecake tahitianvanilla national_fudge_day microwave_mug_cake biscuits orange_the_fruit myfoodanddrink nutellaislife cookie_mug mollysgirls pumpkin_sugar_cookies sweetpizza kara_cooks halfeaten representations as learned by the word2vec model. fondant_au_chocolat dessert_photos saycheesecake molten_chocolate italian_meringue chocolete homemadepie thaisweets chocolate_macarons powered_sugar petit_cakes peanut_butter_cookie zebracake hardfoodporn butterscotch_chips peanut_butter_chip food_porn_recipes lemon_cream akonamayfoodblog candied_nuts diy_dessert caramel_ice_cream cocobruni edible_awesomeness pastry_cream gingerbread_cupcakes tiramisus deep_dish_cookie cakev pf365s baked_goodies ghost_cupcakes nutritious_he mini_doughnuts national_caramel_day lemon_cookie would_nom chocolate_covered_pretzel yuuuuuummmm easy_recipies strowberies pizookies family_frendly gooey_cookies smoreo food_porn_for_ts chocolate_fudge_cookies_recipe whatvegansbake pot_de_creme my_name_is_yeh preztel pasticcerie_brescia dippingdots dessertpizza ham_and_cheese_croissant choco_chips food_taimu sliced_almonds shortcrust_pastry chocolate_waffle caras_cravings doublecream datzdough macoron chocolate_chip_recipes cinnamon_scrolls gluten_free_donuts delicious_dessert dominque_ansel nhamii macaron_cake delicioius lemon_icing thee_simple_foodie cookiecups new_york_super_fudge_chunkcupcake_tower cakeandbakeshow girelle short_skit_long_jacket reblogged_recipes coconut_macaroon rice_krispy_squares wild_idea_buffalo_recipe_of_the_week pumpkin_desserts mybakingaddiction salted_caramel_cupcakes oreobrownies meltaways caramel cherpumple frappucchino blueberrryhandletheheat cookie_swap cookies_and_cream_cookies callmebettycrocker decadant chuao_coffee ideal_breakfast delightful_repast this_looks_delicious_omg yum_yumz fruit_crisp tall_cake annies_eats sweeys fruitdessert karmello polenta_cake no_bake_pie marshmallow_recipes craving_now banana_slices minipies cake_reference rosettecake sorghum_flour iced_doughnuts homemade_bagels pink_cookie halloween_donuts apple_cookiesnutella_french_toast fooderific something_swanky other_baking_stuff chewy_cookies chocolatewaffle leiansunofficialfoodblog lemon_poppyseed mrspenguin lemon_meringue_tart drizzled earl_grey_ice_cream pumpkin_spice_cake gensaneats sticky_toffee iced_coffees alanabakes sponge_cakes baking_tip delish−eats bakesession italianbakery heaven_in_a_box the_best_of_filipino_food low_faicebox_cookies cinnamon_ice_cream molten_cake hazelnut_chocolate veganmuffinsmy_time_in_the_kitchen date_syrup yummy_ass_food pink_oreos peanut_betterbrownes pocky_cake mint_and_chocolate holiday_sweets chocolateygoodness panna_cottas chocolatedonuts dacquoise valentines_cake bakelikeapro huckcafe garlic_parmesan_fries buttercream_cake nava_k mysokolad delicious_donuts chocolaterecipes pineappletart culinaryblog tunnel_cake marengs cookieshot goodnessgraceous faydabakery bakery brownie_cupcakes pastry_recipe choux_cream chicken_and_cheese vegan_baked_goods these_cookies_are_the_best the_hungry_diaries hot_dessert lemondrizzle fruittarts lemon_pudding smores_barcinnamon_roll_cake maple_frosting salted_caramel_ice_creamchoclate_chip food_gams vanilla_milk_chocolate dirt_cake oreo_pudding nomgasm

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slice_of_cake caramelcookiecrunch tor for each node of the taxonomy, and leverage the tag con- the_brunette_baker time_40_minutes quickbites omg_i_want_to_make_this palmacupcake oat_cookies deepfrying estrels the_yummy_things flourlesschocolatecake vegan_mother_hubbard raw_desserts milkchoco bahaytinapie pacakes looks_heavenly j_kenji_lopez−alt heartcookie carrot_muffins oh_my_god_get_in_my_mouth food_por hazelnutspread chocolate_frosting arri_cooks flourfree strawberryyyy banana_nut almost_raw chocolate_smoothie sprinkle_bar holiday_snacks

royalpudding chelsea_buns dried_cherry guilt_free_dessert molten_lava_cake quick_bread food_i_cant_eat gelatobar bakedapples chocolate_coffee photograzing chocolatechip nycravings lemon_butter icing_cookies culinaryschooladventures easy_bread cherry_cupcakes peggy_porschen chocomallows cookiebrownie cookiesfordays rainbowdrops magic_shell giant_cookies raw_cheesecake vegan_pot_pie chocolatecupcake mganns_kitchen peanut_butter_jelly_sandwich purple_frosting cheeseburger_soup food_and_drink_edits cremadipistacchio fluffy_pancakes sprinkles_cupcake whitechocomocha snack_cake texts in blog posts to jointly learn tag vectors and category kakaoscsiga pumpkin_cream_cheese bestcupcakesever grandmas_cooking divine_food japanese_cheesecake coconut_milk_ice_cream therapybaking la_mia_vita_dolce recipe_dinner marcarons marshmallow_pops vectors in the same feature space. Given such a setup, after Figure 8: Nearest tags to “Food and Drink/Desserts” learning the representations, every tag from the vocabulary can be categorized by simply looking up the closest category vector in the joint embedding space. 5.2.1 Training Specifically, given the labeled tags, we extend to obtain D The models are optimized using stochastic gradient as- data set ss where categories are imputed into post “sen- D cent, suitable for large-scale problems. However, computa- tences” pi, where available. In particular, labeled tags are tion of gradients in (5.1) and (5.3) is proportional to the accompanied by assigned categories, and every time a vector vocabulary size T∇L, which may be computationally expensive of a labeled central tag tj is updated to predict the surround- in practical tasks seeing as how T could easily reach several ing tags, the vectors of categories assigned to tj are updated million tags. As an alternative, we used a negative sampling as well. More formally, assuming the central tag tj is la- approach [13], which significantly reduces the computational beled with Cj of C categories in total, ζj = c1, . . . , cC , { j } complexity. the semi-supervised skip-gram learns tag and category rep- The data set used during the model training comprised 6.8 resentations by maximizing the following objective function, billion posts that contained tags. To collect category labels for some of the tags, we sorted the tags in a decreasing order log P(tj+m tj )+ log P(tj+m c) . | | of popularity and the editors labeled the top ones with one or p ss t p n m n,m=0 c ζ ∈DX Xj ∈ − ≤ X≤ 6  X∈ j  more categories. Following this process, we obtained 8,400 (5.3) categorized tags. We show several examples in Table 2. The probability (t c) of observing tag t , given label P j+m j+m The models were trained using a machine with 96GB of c of the current tag t ,| is defined using the soft-max, j RAM memory and 24 cores. The dimensionality of the em- bedding space was set to d = 300, and the context neigh- exp(vc>vt0j+m ) P(tj+m c) = . (5.4) borhood size was set to n = 5. Finally, we used 10 neg- | T exp(v v ) t=1 c> t0 ative samples in each vector update for negative sampling. This procedure allows us toP seamlessly incorporate labeled Similarly to the approach in [13], most frequent tags were and unlabeled data, and learn tag and category vectors in sub-sampled during the training. the joint embedding space. Then, the classification of tags amounts to a simple nearest-neighbor search among the cat- 5.2.2 Inference egory vectors. In Figure 6 we show the graphical represen- When the vector representations of all tags are learned, tation of the semi-supervised skip-gram model. we can find similar tags for a given tag by straightforward

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User Inferred interest User profile user 1 Arts and Entertainment/Movies tag: spoilers:30, shrek:18, hercules:12, cinderella:3, hobbit:123, hulk:21, pokemon:7, thor:58, . . . disney:500,{ tarzan:8, marvel:385, wolverine:21, twilight:2, pixar:87, godzilla:1, x-men:53, . . . pocahontas:4, avengers:134 txt: aladdin:28, batman:10,} bambi:12, movies:100 desc:{ oscar:1, animation:12, comedy:1, movie:1, dvd:1} { } user 2 Style and Fashion tag: womensfashion:110, curls:6, fashiondiaries:133, redhair:2, menswear:125, chanel:4 . . . springfashion:50,{ style:132, streetstyle:132, hairstylist:134, dapper:3, mensfashion:124 txt: fashion:108 } { } user 3 Food and Drink tag: food:11, dessert:4 , soup:1, brunch:1, fruit:2, chicken:3, smoothie:1, cake:2, breakfast:2, . . . ginger:2,{ salad:5, avocado:1 txt: food:16, meals:6 } follows:{ user4542:1, user84852:1,} user9332:1, user4524424:1 { } user 4 Home and Garden tag: daisies:2, kitchen:20, chair:3, art:81, outdoor:20, chandelier:12, lamp:8, window:2, bath:1 . . . floral:17,{ home:3, wildflowers:1, flowers:102, interior:201, tree:1, flower:49, table:1, stairs:2, . . . bedroom:56, wood:2, bathroom:26 txt: garden:32, interior:17, home:41} { } user 5 Automotive/Motorcycles tag: cars:24, ride:9, vehicle:22, riding:8, road:18 txt:{bike:8, motorcycle:10, riding:5, ride:9, road:10,} vehicle:18, bikes:6, bicycle:2, scooter:1 { } extracted from an activity is of class k, and 0 otherwise. Table 6: A/B test results on 10% of user population In addition, we used the time stamp tact, representing the day in which the activity happened, to exponentially decay Campaign Control Targeted less recent activities to account for passing interest (we used Home and Garden +9.71% Style & Fashion − +42.53% α = 0.99 in our experiments). Note that the set i, in − addition to the user’s original content, also includedA posts Sports/Outdoor Sports +19.86% Arts & Enter./Television − +24.37% reblogged by user ui. − t Arts & Enter./Video Games +19.02% The value of ui,cat=k represents an exponentially time- Pets/Dogs − +27.21% decayed count of all the activities in the k-th category. To − effectively store the user profile for interest targeting, instead Arts & Entertainment 1 +9.08% Arts & Entertainment 2 − +6.54% of storing all possible activities and their time stamps, we − maintain a decayed sum of the activities and update the t ui,cat=k daily. Using this approach, we are able to qualify top K users in posts created by the b-th blog were liked ten times, then the t each category by sorting the interest score ui,cat=k. Depend- value was set to 10; if a user followed the b-th blog, then ing on the advertiser’s goals and the category, the choice of the value was set to 1), while the indicator function I( ) K varies from campaign to campaign. We note that a sin- returns 1 if the blog is of class k, and 0 otherwise. Similarly· gle user can be qualified into one or more interest categories to other activities, we applied the exponential decay to the (e.g., a user can be categorized in“Sport”,“Sport/Basketball”, sum, based on the time stamps of follow and like actions. and “Health and Nutrition/Vitamins” at the same time), We have observed that additional signals, in the form of and, when the system was deployed, each user was assigned follow and like engagement with frequent bloggers, increase to 13 categories on average. An example of user profiles our segment sizes, making it possible to efficiently target a qualified into certain categories is given in Table 5. greater number of users. 5.3.1 Leveraging the follower graph 5.4 Results To be able to target Tumblr users who do not create In order to evaluate the generated user interest segments, much content, but who actively follow and engage with other we performed online A/B testing and worked with several blogs, we leverage the follower graph to create additional advertisers who ran concurrent interest-targeted and untar- categorized features. Using equation 5.5 we can identify fre- geted campaigns. We tracked user engagement with their quent bloggers in certain interest categories by focusing on ads in terms of sponsored post likes, reblogs and follows, a small percentage of users with a maximum ui,cat=k. Fol- and show the results for 8 targeting campaigns in Table 6. lowing and liking posts created by social influencers in the We observed an average increase of 20% in user engagement k-th category serves as additional evidence of one’s interest (aggregate of 3 metrics) with sponsored posts in comparison in that category. to untargeted campaigns. This performance result repre- In each interest category, we label the 5% of users with sents a significant improvement over the baseline approach. the highest number of activities in that category as frequent bloggers. Next, we update the interest score of all users ui in the k-th category, in the following manner 6. GENDER PREDICTION In this section, we explain the details of our gender pre- t (t teng ) ui,cat=k+ = α − weng I(b is of class k), diction model, based on the user profiles described in the b eng b previous sections. We first describe the generation process X∈Fi X∈ (5.6) of a golden set of labeled users, which is used to train a where i is a set of all frequent blogs followed by user ui, predictive model that generalizes well on the remaining un- eng areF all engagements with the b-th blog, i.e. likes or labeled users. This is followed by the model’s description follow actions, along with their weights weng (e.g., if the and a discussion of the results.

9 #Microposts2016 6th Workshop on Making Sense of Microposts @WWW2016 · · · Table 7: Matching names in blog description (* represents Table 8: Accuracy of gender model on hold-out set matched name; 564K female and 395K male users found) Gender Precision Recall regex count female 0.806 0.838 my name is * 783,564 male 0.794 0.689 my name’s * 291,811 me llamo * 47,663 the name’s * 38,065 Table 9: Editorial evaluation of random user predictions mi nombre es * 9,751 mi chiamo * 9,181 Class Prediction Correct Wrong Not sure mein name ist * 1,025 female 429 4 298 meu nome e * 512 male 144 5 127 mon nom est * 215 mio nome e * 185

function of input x, passed through a sigmoidal function, 1 6.1 Collecting ground-truth labels P(y = 1 x) = f(x, w) = , (6.1) | 1 + exp( xTw) In order to train the machine learning method for gender − prediction, in addition to user profiles, we also require la- and P(y = 0 x) = 1 P(y = 1 x). To estimate the parame- bels that present the ground truth (i.e., “male” or “female”). ters w, we minimize| − the following| loss function, However, Tumblr does not collect gender information when N users sign-up, leaving open the question of how to obtain 1 2 min yi f(xi, w) + λ w 1, (6.2) such data. w K N − k k ∈R i=1 To address this problem, we proposed to leverage highly X  informative blog description data in order to infer user gen- where hyper-parameter λ controls the `1-regularization, in- der information. In particular, very often users declare their troduced to induce sparsity in the parameter vector and re- name in the blog description, as illustrated in Figure 1. To duce the feature space to a subset of features that are the extract the users’ declared names, we used several regular most predictive. For data sets with a large number of fea- expression rules that we found to result in very high preci- tures, as we have in our use case, it is common that many sion. The obtained results from a large set of name-matching features are not useful for producing the desired learning regular expressions were editorially tested for quality. It was result. For this reason, the `1-regularization was a critical found that regular expressions reported in Table 7 yielded part of our training procedure. In addition, we experimen- the most reliable extracted names (valid names were ex- tally observed that the model generalizes better when we tracted in more than 95% of the cases). Next, in order trained an initial model with `1-regularization to find which to generate the gender ground truth, we used the US cen- features have non-zero weight, and then do another round 10 sus data of popular baby names from year 1880 to 2013 of training without `1-regularization, by only using features to create a “name gender” mapping. Seeing as how cer- with non-zero weights from the first round to learn a better tain names are given→ to both males and females, we used classifier. the empirical counts of babies with certain names from cen- Given a trained logistic regression model, the posterior sus data to generate the labels. More specifically, we used class probabilities are estimated as f(xi, w) [0, 1]. Then, ∈ male/female empirical ratios as soft labels, with 1 indicating the classification predictions are made by thresholding, as 100% confidence in a male and 0 indicating 100% confidence yˆi = sign(f(xi, w) θ), where threshold θ is set between 0 − in a female name. and 1 to ensure the desired precision and recall according to specific advertisers requirements. 6.2 Proposed approach 6.3 Results Let = (x , y ), i = 1, ..., N denote our gender data g i i To evaluate the accuracy of our gender prediction frame- set, whereD N{is the total number} of labeled users, x is the i work, we trained a logistic regression model on 70% of the K-dimensional user feature vector generated from the user golden set and tested on the remaining 30%. We used the profiles, and y [0, 1] is the user label (real-valued number, i Vowpal Wabbit [10] implementation on Hadoop to train the ranging from 0∈ to 1). The feature vectors were generated model. To illustrate the performance of our gender classi- from the user profiles described in Section 4.2, by disregard- fier, the performance results in terms of precision and recall ing time stamps (due to the fact that, unlike the users’ in- measures are presented in Table 8. The threshold value θ terests, their gender does not fluctuate), and directly using was set to a value which ensured precision of 0.8. the feature counts as values. To handle large counts, we In addition to evaluation on the hold-out set, we also edi- normalized the counts by applying log transformation: as- torially evaluated gender predictions on the unlabeled data suming that the count is x, we replace the count by the value set of user profiles. We randomly picked 1,007 gender pre- log(1 + x). dictions from the population of 64.1 million users and asked Our goal is to learn a gender-predictive model, f : x editors to visit their profiles and verify their gender. They y. As a classification model, we used logistic regression,→ were instructed to mark our predictions as “correct”,“incor- parameterized by weight vector w. We assume that the rect”, or “not sure”. The “not sure” grade is to be used when posterior gender probabilities can be estimated as a linear the visual inspection of a profile is inconclusive, as we found was often the case. The editorial judgment came back with 10www.ssa.gov/oact/babynames/limits.html 573 “correct” (429 females and 144 males), 9 “incorrect”, and

10 #Microposts2016 6th Workshop on Making Sense of Microposts @WWW2016 · · · 425 “not sure” grades (see Table 9). The fact that there are [4] F. Bonchi, R. Perego, F. Silvestri, H. Vahabi, and so many “not sure” grades indicates that in many cases it R. Venturini. Efficient query recommendations in the is hard to infer the gender even after manual efforts, fur- long tail via center-piece subgraphs. In Proceedings of ther indicating the benefits of the proposed approach and the 35th International ACM SIGIR Conference on its superior performance in comparison to humans. Finally, Research and Development in Information Retrieval, we retrained the model with 100% of the golden set and SIGIR ’12, pages 345–354, New York, NY, USA, 2012. deployed it in Yahoo production systems. ACM. A demonstration video of the most predictive tags [5] A. Z. Broder. Computational advertising and in each gender group is available online at https://www. recommender systems. In Proceedings of the ACM youtube.com/watch?v=jXGJ0TpOlhg. conference on Recommender systems, pages 1–2. ACM, 2008. 7. DEPLOYED SYSTEM [6] Y. Chang, L. Tang, Y. Inagaki, and Y. Liu. What is Due to the rapid growth of Tumblr and the large number tumblr: A statistical overview and comparison. ACM of activities generated by the existing users, we implemented SIGKDD Explorations Newsletter, 16(1):21–29, 2014. daily scoring of users in Yahoo production servers. We store [7] R. K. Chellappa and R. G. Sin. Personalization versus the activities, i.e. raw counts as well as decayed counts, privacy: An empirical examination of the online in Hive tables11 for efficient retrieval. The decayed counts consumer’s dilemma. Information Technology and used in interest prediction are updated on a daily basis by Management, 6(2-3):181–202, 2005. multiplying the old feature values by the decay factor α and [8] A. S. Das, M. Datar, A. Garg, and S. Rajaram. Google adding new activities. In order to infer the gender of new news personalization: Scalable online collaborative users we implemented daily scoring by leveraging MapRe- filtering. In WWW, pages 271–280. ACM, 2007. duce on Hadoop12. Both interest and gender models are [9] D. Essex. Matchmaker, matchmaker. Communications retrained on a regular basis. of the ACM, 52(5):16–17, 2009. After thorough editorial evaluation of the inferred gen- [10] J. Langford, L. Li, and T. Zhang. Sparse online der and interest targeting, both targeting frameworks were learning via truncated gradient. The Journal of 13 enabled through Gemini self-serve tool . Advertisers can Machine Learning Research, 10:777–801, 2009. choose to use gender and/or interest targeting with custom [11] A. Majumder and N. Shrivastava. Know your segment sizes, allowing for effective targeting campaigns. personalization: Learning topic level personalization in online services. In Proceedings of the 22nd 8. CONCLUSIONS International Conference on , pages We have presented the steps in the development of a large- 873–884, 2013. scale Tumblr gender and interest targeting framework, where [12] U. Manber, A. Patel, and J. Robison. Experience with we used historical Tumblr activities to create rich user pro- personalization on Yahoo! Communications of the files. We described the methodology, including a novel semi- ACM, 43(8):35, 2000. supervised neural language model, as well as the high-level [13] T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and implementation details behind the deployed system. Cur- J. Dean. Distributed representations of words and rently, our gender and interest predictions cover users that phrases and their compositionality. In NIPS, pages generate more than 90% of Tumblr’s daily activities, and 3111–3119, 2013. are heavily leveraged by advertisers. In our ongoing work, [14] D. Riecken. Personalized views of personalization. we are concentrating on creating custom keyword-targeted Communications of the ACM, 43(8):27–28, 2000. advertising segments, specifically tailored for a particular [15] D. Shin, S. Cetintas, and K.-C. Lee. Recommending advertiser, which include addressing of the problems of key- tumblr blogs to follow with inductive matrix word discovery and expansion. completion. In RecSys 14 Poster Proceedings, 2014. [16] T. Singh, L. Veron-Jackson, and J. Cullinane. 9. REFERENCES Blogging: A new play in your marketing game plan. [1] A. Ahmed, Y. Low, M. Aly, V. Josifovski, and A. J. Business Horizons, 51(4):281–292, 2008. Smola. Scalable distributed inference of dynamic user [17] S. K. Tyler, S. Pandey, E. Gabrilovich, and interests for behavioral targeting. In KDD, pages V. Josifovski. Retrieval models for audience selection 114–122, 2011. in display advertising. In Proceedings of the 20th ACM [2] J. Alba, J. Lynch, B. Weitz, C. Janiszewski, R. Lutz, international conference on Information and A. Sawyer, and S. Wood. Interactive home shopping: knowledge management, pages 593–598. ACM, 2011. consumer, retailer, and manufacturer incentives to [18] C. Yi, T. Lei, I. Yoshiyuki, and L. Yan. What is participate in electronic marketplaces. The Journal of tumblr: A statistical overview and comparison. Marketing, pages 38–53, 1997. arXiv:1403.5206v2, 2014. [3] N. Barbieri, F. Bonchi, and G. Manco. Who to follow and why: link prediction with explanations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1266–1275. ACM, 2014. 11https://hive.apache.org 12https://hadoop.apache.org 13https://gemini.yahoo.com

11 #Microposts2016 6th Workshop on Making Sense of Microposts @WWW2016 · · ·