Received Citations As a Main SEO Factor of Google Scholar Results Ranking
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Machine Learning for Marketers
Machine Learning for Marketers A COMPREHENSIVE GUIDE TO MACHINE LEARNING CONTENTS pg 3 Introduction pg 4 CH 1 The Basics of Machine Learning pg 9 CH. 2 Supervised vs Unsupervised Learning and Other Essential Jargon pg 13 CH. 3 What Marketers can Accomplish with Machine Learning pg 18 CH. 4 Successful Machine Learning Use Cases pg 26 CH. 5 How Machine Learning Guides SEO pg 30 CH. 6 Chatbots: The Machine Learning you are Already Interacting with pg 36 CH. 7 How to Set Up a Chatbot pg 45 CH. 8 How Marketers Can Get Started with Machine Learning pg 58 CH. 9 Most Effective Machine Learning Models pg 65 CH. 10 How to Deploy Models Online pg 72 CH. 11 How Data Scientists Take Modeling to the Next Level pg 79 CH. 12 Common Problems with Machine Learning pg 84 CH. 13 Machine Learning Quick Start INTRODUCTION Machine learning is a term thrown around in technol- ogy circles with an ever-increasing intensity. Major technology companies have attached themselves to this buzzword to receive capital investments, and every major technology company is pushing its even shinier parentartificial intelligence (AI). The reality is that Machine Learning as a concept is as days that only lives and breathes data science? We cre- old as computing itself. As early as 1950, Alan Turing was ated this guide for the marketers among us whom we asking the question, “Can computers think?” In 1969, know and love by giving them simpler tools that don’t Arthur Samuel helped define machine learning specifi- require coding for machine learning. -
Analysis of the Youtube Channel Recommendation Network
CS 224W Project Milestone Analysis of the YouTube Channel Recommendation Network Ian Torres [itorres] Jacob Conrad Trinidad [j3nidad] December 8th, 2015 I. Introduction With over a billion users, YouTube is one of the largest online communities on the world wide web. For a user to upload a video on YouTube, they can create a channel. These channels serve as the home page for that account, displaying the account's name, description, and public videos that have been up- loaded to YouTube. In addition to this content, channels can recommend other channels. This can be done in two ways: the user can choose to feature a channel or YouTube can recommend a channel whose content is similar to the current channel. YouTube features both of these types of recommendations in separate sidebars on the user's channel. We are interested analyzing in the structure of this potential network. We have crawled the YouTube site and obtained a dataset totaling 228575 distinct user channels, 400249 user recommendations, and 400249 YouTube recommendations. In this paper, we present a systematic and in-depth analysis on the structure of this network. With this data, we have created detailed visualizations, analyzed different centrality measures on the network, compared their community structures, and performed motif analysis. II. Literature Review As YouTube has been rising in popularity since its creation in 2005, there has been research on the topic of YouTube and discovering the structure behind its network. Thus, there exists much research analyzing YouTube as a social network. Cheng looks at videos as nodes and recommendations to other videos as links [1]. -
Human Computation Luis Von Ahn
Human Computation Luis von Ahn CMU-CS-05-193 December 7, 2005 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Thesis Committee: Manuel Blum, Chair Takeo Kanade Michael Reiter Josh Benaloh, Microsoft Research Jitendra Malik, University of California, Berkeley Copyright © 2005 by Luis von Ahn This work was partially supported by the National Science Foundation (NSF) grants CCR-0122581 and CCR-0085982 (The Aladdin Center), by a Microsoft Research Fellowship, and by generous gifts from Google, Inc. The views and conclusions contained in this document are those of the author and should not be interpreted as representing official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity. Keywords: CAPTCHA, the ESP Game, Peekaboom, Verbosity, Phetch, human computation, automated Turing tests, games with a purpose. 2 Abstract Tasks like image recognition are trivial for humans, but continue to challenge even the most sophisticated computer programs. This thesis introduces a paradigm for utilizing human processing power to solve problems that computers cannot yet solve. Traditional approaches to solving such problems focus on improving software. I advocate a novel approach: constructively channel human brainpower using computer games. For example, the ESP Game, introduced in this thesis, is an enjoyable online game — many people play over 40 hours a week — and when people play, they help label images on the Web with descriptive keywords. These keywords can be used to significantly improve the accuracy of image search. People play the game not because they want to help, but because they enjoy it. I introduce three other examples of “games with a purpose”: Peekaboom, which helps determine the location of objects in images, Phetch, which collects paragraph descriptions of arbitrary images to help accessibility of the Web, and Verbosity, which collects “common-sense” knowledge. -
Pagerank Best Practices Neus Ferré Aug08
Search Engine Optimisation. PageRank best Practices Graduand: Supervisor: Neus Ferré Viñes Florian Heinemann [email protected] [email protected] UPC Barcelona RWTH Aachen RWTH Aachen Business Sciences for Engineers Business Sciences for Engineers and Natural Scientists and Natural Scientists July 2008 Abstract Since the explosion of the Internet age the need of search online information has grown as well at the light velocity. As a consequent, new marketing disciplines arise in the digital world. This thesis describes, in the search engine marketing framework, how the ranking in the search engine results page (SERP) can be influenced. Wikipedia describes search engine marketing or SEM as a form of Internet marketing that seeks to promote websites by increasing their visibility in search engine result pages (SERPs). Therefore, the importance of being searchable and visible to the users reveal needs of improvement for the website designers. Different factors are used to produce search rankings. One of them is PageRank. The present thesis focuses on how PageRank of Google makes use of the linking structure of the Web in order to maximise relevance of the results in a web search. PageRank used to be the jigsaw of webmasters because of the secrecy it used to have. The formula that lies behind PageRank enabled the founders of Google to convert a PhD into one of the most successful companies ever. The uniqueness of PageRank in contrast to other Web Search Engines consist in providing the user with the greatest relevance of the results for a specific query, thus providing the most satisfactory user experience. -
The Machine Learning Journey with Google
The Machine Learning Journey with Google Google Cloud Professional Services The information, scoping, and pricing data in this presentation is for evaluation/discussion purposes only and is non-binding. For reference purposes, Google's standard terms and conditions for professional services are located at: https://enterprise.google.com/terms/professional-services.html. 1 What is machine learning? 2 Why all the attention now? Topics How Google can support you inyour 3 journey to ML 4 Where to from here? © 2019 Google LLC. All rights reserved. What is machine0 learning? 1 Machine learning is... a branch of artificial intelligence a way to solve problems without explicitly codifying the solution a way to build systems that improve themselves over time © 2019 Google LLC. All rights reserved. Key trends in artificial intelligence and machine learning #1 #2 #3 #4 Democratization AI and ML will be core Specialized hardware Automation of ML of AI and ML competencies of for deep learning (e.g., MIT’s Data enterprises (CPUs → GPUs → TPUs) Science Machine & Google’s AutoML) #5 #6 #7 Commoditization of Cloud as the platform ML set to transform deep learning for AI and ML banking and (e.g., TensorFlow) financial services © 2019 Google LLC. All rights reserved. Use of machine learning is rapidly accelerating Used across products © 2019 Google LLC. All rights reserved. Google Translate © 2019 Google LLC. All rights reserved. Why all the attention0 now? 2 Machine learning allows us to solve problems without codifying the solution. © 2019 Google LLC. All rights reserved. San Francisco New York © 2019 Google LLC. All rights reserved. -
The Pagerank Algorithm and Application on Searching of Academic Papers
The PageRank algorithm and application on searching of academic papers Ping Yeh Google, Inc. 2009/12/9 Department of Physics, NTU Disclaimer (legal) The content of this talk is the speaker's personal opinion and is not the opinion or policy of his employer. Disclaimer (content) You will not hear physics. You will not see differential equations. You will: ● get a review of PageRank, the algorithm used in Google's web search. It has been applied to evaluate journal status and influence of nodes in a graph by researchers, ● see some linear algebra and Markov chains associated with it, and ● see some results of applying it to journal status. Outline Introduction Google and Google search PageRank algorithm for ranking web pages Using MapReduce to calculate PageRank for billions of pages Impact factor of journals and PageRank Conclusion Google The name: homophone to the word “Googol” which means 10100. The company: ● founded by Larry Page and Sergey Brin in 1998, ● ~20,000 employees as of 2009, ● spread in 68 offices around the world (23 in N. America, 3 in Latin America, 14 in Asia Pacific, 23 in Europe, 5 in Middle East and Africa). The mission: “to organize the world's information and make it universally accessible and useful.” Google Services Sky YouTube iGoogle web search talk book search Chrome calendar scholar translate blogger.com Android product news search maps picasaweb video groups Gmail desktop reader Earth Photo by mr.hero on panoramio (http://www.panoramio.com/photo/1127015) 6 Google Search http://www.google.com/ or http://www.google.com.tw/ The abundance problem Quote Langville and Meyer's nice book “Google's PageRank and beyond: the science of search engine rankings”: The men in Jorge Luis Borges’ 1941 short story, “The Library of Babel”, which describes an imaginary, infinite library. -
The Pagerank Algorithm Is One Way of Ranking the Nodes in a Graph by Importance
The PageRank 13 Algorithm Lab Objective: Many real-world systemsthe internet, transportation grids, social media, and so oncan be represented as graphs (networks). The PageRank algorithm is one way of ranking the nodes in a graph by importance. Though it is a relatively simple algorithm, the idea gave birth to the Google search engine in 1998 and has shaped much of the information age since then. In this lab we implement the PageRank algorithm with a few dierent approaches, then use it to rank the nodes of a few dierent networks. The PageRank Model The internet is a collection of webpages, each of which may have a hyperlink to any other page. One possible model for a set of n webpages is a directed graph, where each node represents a page and node j points to node i if page j links to page i. The corresponding adjacency matrix A satises Aij = 1 if node j links to node i and Aij = 0 otherwise. b c abcd a 2 0 0 0 0 3 b 6 1 0 1 0 7 A = 6 7 c 4 1 0 0 1 5 d 1 0 1 0 a d Figure 13.1: A directed unweighted graph with four nodes, together with its adjacency matrix. Note that the column for node b is all zeros, indicating that b is a sinka node that doesn't point to any other node. If n users start on random pages in the network and click on a link every 5 minutes, which page in the network will have the most views after an hour? Which will have the fewest? The goal of the PageRank algorithm is to solve this problem in general, therefore determining how important each webpage is. -
SEO - What Are the BIG Things to Get Right? Keyword Research – You Must Focus on What People Search for - Not What You Want Them to Search For
Search Engine Optimization SEO - What are the BIG things to get right? Keyword Research – you must focus on what people search for - not what you want them to search for. Content – Extremely important - #2. Page Title Tags – Clearly a strong contributor to good rankings - #3 Usability – Obviously search engine robots do not know colors or identify a ‘call to action’ statement (which are both very important to visitors), but they do see broken links, HTML coding errors, contextual links between pages and download times. Links – Numbers are good; but Quality, Authoritative, Relevant links on Keyword-Rich anchor text will provide you the best improvement in your SERP rankings. #1 Keyword Research: The foundation of a SEO campaign is accurate keyword research. When doing keyword research: Speak the user's language! People search for terms like "cheap airline tickets," not "value-priced travel experience." Often, a boring keyword is a known keyword. Supplement brand names with generic terms. Avoid "politically correct" terminology. (example: use ‘blind users’ not ‘visually challenged users’). Favor legacy terms over made-up words, marketese and internal vocabulary. Plural verses singular form of words – search it yourself! Keywords: Use keyword search results to select multi-word keyword phrases for the site. Formulate Meta Keyword tag to focus on these keywords. Google currently ignores the tag as does Bing, however Yahoo still does index the keyword tag despite some press indicating otherwise. I believe it still is important to include one. Why – what better place to document your work and it might just come back into vogue someday. List keywords in order of importance for that given page. -
Personalized Pagerank Estimation and Search: a Bidirectional Approach
Personalized PageRank Estimation and Search: A Bidirectional Approach Peter Lofgren Siddhartha Banerjee Ashish Goel Department of CS School of ORIE Department of MS&E Stanford University Cornell University Stanford University [email protected] [email protected] [email protected] ABSTRACT 1. INTRODUCTION We present new algorithms for Personalized PageRank es- On social networks, personalization is necessary for re- timation and Personalized PageRank search. First, for the turning relevant results for a query. For example, if a user problem of estimating Personalized PageRank (PPR) from searches for a common name like John on a social network a source distribution to a target node, we present a new like Facebook, the results should depend on who is doing the bidirectional estimator with simple yet strong guarantees on search and who their friends are. A good personalized model correctness and performance, and 3x to 8x speedup over ex- for measuring the importance of a node t to a searcher s is isting estimators in experiments on a diverse set of networks. Personalized PageRank πs(t)[20, 13, 12] { this motivates a Moreover, it has a clean algebraic structure which enables natural Personalized PageRank Search Problem: Given it to be used as a primitive for the Personalized PageRank • a network with nodes V (each associated with a set of Search problem: Given a network like Facebook, a query keywords) and edges E (possibly weighted and directed), like \people named John," and a searching user, return the • a keyword inducing a set of targets: top nodes in the network ranked by PPR from the perspec- T = ft 2 V : t is relevant to the keywordg tive of the searching user. -
Profiles in Innovation: Artificial Intelligence
EQUITY RESEARCH | November 14, 2016 Artificial intelligence is the apex technology of the information era. In the latest in our Profiles in Innovation Heath P. Terry, CFA series, we examine how (212) 357-1849 advances in machine [email protected] learning and deep learning Goldman, Sachs & Co. have combined with more Jesse Hulsing powerful computing and an (415) 249-7464 ever-expanding pool of data [email protected] to bring AI within reach for Goldman, Sachs & Co. companies across Mark Grant industries. The development (212) 357-4475 [email protected] of AI-as-a-service has the Goldman, Sachs & Co. potential to open new markets and disrupt the Daniel Powell (917) 343-4120 playing field in cloud [email protected] computing. We believe the Goldman, Sachs & Co. ability to leverage AI will Piyush Mubayi become a defining attribute (852) 2978-1677 of competitive advantage [email protected] for companies in coming Goldman Sachs (Asia) L.L.C. years and will usher in a Waqar Syed resurgence in productivity. (212) 357-1804 [email protected] Goldman, Sachs & Co. PROFILESIN INNOVATION Artificial Intelligence AI, Machine Learning and Data Fuel the Future of Productivity Goldman Sachs does and seeks to do business with companies covered in its research reports. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. Investors should consider this report as only a single factor in making their investment decision. For Reg AC certification and other important disclosures, see the Disclosure Appendix, or go to www.gs.com/research/hedge.html. -
Large-Scale Deep Learning with Tensorflow
Large-Scale Deep Learning With TensorFlow Jeff Dean Google Brain team g.co/brain In collaboration with many other people at Google What is the Google Brain Team? ● Research team focused on long term artificial intelligence research ○ Mix of computer systems and machine learning research expertise ○ Pure ML research, and research in context of emerging ML application areas: ■ robotics, language understanding, healthcare, ... g.co/brain We Disseminate Our Work in Many Ways ● By publishing our work ○ See papers at research.google.com/pubs/BrainTeam.html ● By releasing TensorFlow, our core machine learning research system, as an open-source project ● By releasing implementations of our research models in TensorFlow ● By collaborating with product teams at Google to get our research into real products What Do We Really Want? ● Build artificial intelligence algorithms and systems that learn from experience ● Use those to solve difficult problems that benefit humanity What do I mean by understanding? What do I mean by understanding? What do I mean by understanding? What do I mean by understanding? Query [ car parts for sale ] What do I mean by understanding? Query [ car parts for sale ] Document 1 … car parking available for a small fee. … parts of our floor model inventory for sale. Document 2 Selling all kinds of automobile and pickup truck parts, engines, and transmissions. Example Needs of the Future ● Which of these eye images shows symptoms of diabetic retinopathy? ● Find me all rooftops in North America ● Describe this video in Spanish -
Big Data Systems Big Data Parallelism
Big Data Systems Big Data Parallelism • Huge data set • crawled documents, web request logs, etc. • Natural parallelism: • can work on different parts of data independently • image processing, grep, indexing, many more Challenges • Parallelize applicaFon • Where to place input and output data? • Where to place computaFon? • How to communicate data? How to manage threads? How to avoid network boJlenecks? • Balance computaFons • Handle failures of nodes during computaFon • Scheduling several applicaFons who want to share infrastructure Goal of MapReduce • To solve these distribuFon/fault-tolerance issues once in a reusable library • To shield the programmer from having to re-solve them for each program • To obtain adequate throughput and scalability • To provide the programmer with a conceptual framework for designing their parallel program Map Reduce • Overview: • ParFFon large data set into M splits • Run map on each parFFon, which produces R local parFFons; using a parFFon funcFon R • Hidden intermediate shuffle phase • Run reduce on each intermediate parFFon, which produces R output files Details • Input values: set of key-value pairs • Job will read chunks of key-value pairs • “key-value” pairs a good enough abstracFon • Map(key, value): • System will execute this funcFon on each key-value pair • Generate a set of intermediate key-value pairs • Reduce(key, values): • Intermediate key-value pairs are sorted • Reduce funcFon is executed on these intermediate key- values Count words in web-pages Map(key, value) { // key is url // value is