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Leveraging Multiple Relations for Fashion Trend Forecasting Based on Social Media Yujuan Ding*, Yunshan Ma*, Lizi Liao, Wai Keung Wong, and Tat-Seng Chua

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1 Leveraging Multiple Relations for Fashion Trend Forecasting Based on Social Media Yujuan Ding*, Yunshan Ma*, Lizi Liao, Wai Keung Wong, and Tat-Seng Chua Abstract—Fashion trend forecasting is of great research signif- User groups icance in providing useful suggestions for both fashion companies and fashion lovers. Although various studies have been devoted to tackling this challenging task, they only studied limited fashion New York Female New York Female 18-25 years old New York Female 25-40 years old … elements with highly seasonal or simple patterns, which could Fashion elements 10 upper body clothes (sweater, shirt, …) [New York Female 25-40 years old hardly reveal the real complex fashion trends. Moreover, the 4 lower body clothes (pants, skirt, …) Trend Relations 3 full body clothes (dress, jumpsuit, …) @ Dress] 6 outwear (jacket, suit, …) [New York Female 18-25 years old mainstream solutions for this task are still statistical-based and 4 shoes (pumps, flat shoes, …) @ Dress] 2 sneakers (lifestyle, performance) Group Affiliation solely focus on time-series data modeling, which limit the forecast 4 sandals (flat, heeled, …) 4 boots (ankle, knee high, …) [New York Female [New York Female 12 shoulder bag totes (crossbody, bucket, …) @ Pencil dress] accuracy. Towards insightful fashion trend forecasting, previous 4 clutch wallet (clutch, punch, …) @ Pencil dress] 17 colors (white, red, …) Element Affiliation 13 patterns (stripes, text, …) work [1] proposed to analyze more fine-grained fashion elements 24 necklines (v neck, turtleneck, … ) 6 upper-body lengths (crop, below knee, …) [New York Female Relation enhanced which can informatively reveal fashion trends. Specifically, it 6 lower-body lengths (extra short, full, …) 6 dress shapes (pencil, a line, …) @ Dress] content information 4 sleeve lengths (short, sleeveless, …) focused on detailed fashion element trend forecasting for specific 10 sleeve styles (flared, puff, …) 3 pants fit types (skinny, wide leg, …) Historical fashion trend Fashion trend to predict user groups based on social media data. In addition, it proposed 3 coat styles (straight, a line, …) Fashion trend 4 sweater styles (ruffle, plain, …) Forecasting a neural network-based method, namely KERN, to address the 3 rise types (high, low, …) 3 denim wash colors (light, dark, …) model 5 closure types (button, zip, …) problem of fashion trend modeling and forecasting. In this work, 10 shoe types (loafer, boat, …) to extend the previous work [1], we propose an improved model …… named Relation Enhanced Attention Recurrent (REAR) network. Compared to KERN, the REAR model leverages not only the Fig. 1. The fashion trend forecasting task aims to forecast the trends relations among fashion elements, but also those among user of meaningful fashion elements for specific user groups. Multiple relations (group and fashion element affiliations) among the fashion trend time series groups, thus capturing more types of correlations among various are leveraged to enhance the forecasting model. fashion trends. To further improve the performance of long-range trend forecasting, the REAR method devises a sliding temporal attention mechanism, which is able to capture temporal patterns on future horizons better. Extensive experiments and more They need a fashion guidance to develop good taste and catch analysis have been conducted on the FIT [1] and GeoStyle [2] up with trend [3], [4]. Additionally, for the fashion indus- datasets to evaluate the performance of REAR. Experimental and try, valid forecasting enables fashion companies to establish analytical results demonstrate the effectiveness of the proposed marketing strategies wisely and continuously anticipate and REAR model in fashion trend forecasting, which also show the improvement of REAR compared to the KERN. fulfill their consumers’ wants and needs. Traditionally, fashion experts need to travel and conduct surveys to obtain people’s Index Terms—Fashion Trend Forecasting; Time Series Fore- real fashion tastes based on local culture and tradition, which casting; Fashion Analysis; Social Media usually affects the world’s fashion trends [5]. However, such approaches are inefficient, expensive, and highly dependent I. INTRODUCTION on the experts’ background, which usually introduces bias ASHION trend forecasting, aiming to master the change- to the forecasting results [6]. Meanwhile, the huge progress ability in fashion, is of great significance and matters as in Internet, Big Data, and Artificial Intelligence provides us F with an alternative way to tackle this challenging problem: arXiv:2105.03299v2 [cs.LG] 11 May 2021 much as it ever did. Although rapid technological change has infiltrated every aspect of modern life, people’s wants to con- automatic forecasting of fashion trends based on fashion vey a sense of self through their appearance have not changed. data [7]. In fact, fashion trend forecasting or analysis has attracted This research is supported by the National Research Foundation, Singapore some research interests in the computing field, but is still at under its International Research Centres in Singapore Funding Initiative. Any its nascent stage. One popular data source for fashion trend opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National analysis is the purchase record on either online or offline Research Foundation, Singapore. We also appreciate the fashion recognition retailing platform [8]. However, purchase records reflect peo- API service provided by Visenze. ple’s buying decisions which are influenced by many factors Y. Ding is with the College of Computer Science and Software Engi- neering, Shen zhen University, Shenzhen, 518060, China (e-mail: dingyu- such as retailers’ promotions, which hardly reveal real fashion [email protected]) preferences of users, neither the real fashion trend. Social W. K. Wong is with the Hong Kong Polytechnic University, Hong Kong media records the daily life of people from all over the world SAR, China, he is also with the Laboratory for Artificial Intelligence in Design, Hong Kong SAR, China ( [email protected]). and has turned into a platform for more and more users to Y. Ma, L. Liao, and T. Chua are with National University of Singapore, show their fashion tastes and opinions. It thus offers a natural Singapore (e-mail: [email protected], [email protected], and dc- platform for research on fashion trend analysis [9]. Moreover, [email protected]) The corresponding author is W. K. Wong data from social media is massive, diverse, highly related * The two authors contribute equally. to fashion trend and with long time-span, which makes it possible and applicable for insightful large-scale fashion trend step as it incorporates both historical information and future analysis. One recent state-of-the-art study of fashion trends contextual information. With above technical improvement, a based on social media [2] extracted fashion attributes from novel method Relation Enhanced Attention Recurrent network social media images with a CNN model and investigated the (REAR) is proposed in this paper. trends of specific attributes in certain cities. Compared with This paper is an extension of [1], we summarize the other works which target on implicit fashion styles by visual contributions of it from two aspects as follows: clustering [10], [11], the fashion trends demonstrated in Mall’s 1) we propose a novel relation-enhanced attention recurrent work were more specific. However, the fashion attributes it network (REAR) method for tackling the fashion trend fore- investigated to reveal specific fashion trends were coarse- casting task. Technically, we improve the KERN [1] from two grained and of less significance, sometimes showing seasonal perspectives. First, we leverage more external fashion trend patterns only, for example, wearing jacket. Moreover, the relations. On top of the fashion element relations, the user attributes of users were not explored sufficiently. It involved group relations are also leveraged to bring more association location information of users while overlooked other basic of fashion trend signals. Both relations are incorporated by attributes such as age ad genders. the message passing modules. Second, to further enhance Ma et al. made an improvement later by investigating the performance of long-horizon forecasting, we devise a fashion trends of specific fashion elements for various user sliding temporal attention mechanism to effectively capture groups. They focused on more fine-grained fashion elements, the temporal patterns on future horizons. incorporated more user profile information, and worked on 2) Extensive experiments are conducted on the FIT and the longer fashion trends. To handle the challenging task, a GeoStyle datasets [2] to evaluate the effectiveness of the model named KERN that based on an LSTM encoder-decoder proposed REAR method. Specifically, REAR shows superior framework was proposed. The novel model leveraged the performance in fashion trend forecasting in terms of overall internal and external knowledge to utilize the correlations forecasting accuracy compared to all other competitive base- among specific fashion trend signals to enhance the trend lines, including the KERN. The technical parts designed in forecasting. REAR are evaluated to be effective via ablation studies. Some In this paper, we extend the work of [1], improve the technical details that were same as KERN but not investigated
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