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Recommender system introduction Recommender system introduction Presentation Transcript

  • No So Brief Introduction Case Studies. A Study of Recommender System. 12.05.2011 . . . . . . A Study of Recommender System
  • No So Brief Introduction Case Studies. Main Contents 1. No So Brief Introduction The categories of RS The Challenge Human Recommendation VS. Recommendation System 2. Case Studies Amazon YouTube . . . . . . A Study of Recommender System
  • No So Brief Introduction Case StudiesInforamtion Retrieval ( IR ) google, baiduInformation Filtering ( IF ) strings, facebook . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System From Web Portal to Search Engine and Personal. recommendation . . Deveplopment Trendency Web portal . . Tranditional website like . Yahoo! sina . Search Engine . sometimes hard to find what you want . . Recommendation . “Follow”model . “cold start” . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Yahoo! . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Google . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. YouTube . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Twitter . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. What is Recommender System . Definition of Recommender System and More examples . Recommender System form a specific type of information filtering system technique that attempts to recommend information items (movies, TV show, music, news, web pages, research papers etc.) or social elements (e.g. people, events or groups) that are likely to be of interest to the user. . . Famous RS website . news.qq.com www.YouTube.com douban.fm www.netflix.com www.taobao.com . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. The Main Job of Recommender System Top-N problem . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. An example : Amazon.cn . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Research Area 1. Recommendation technique and recommendation algorithm . 2 Real-time research ( users and items are changing ) 3. Recommendation quality . 4 Hybrid data and hybrid technique integration . 5 Data Mining in RS ( association rule DM , Bayesian category ) 6. User privacy protection . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Main Contents 1. No So Brief Introduction The categories of RS The Challenge Human Recommendation VS. Recommendation System 2. Case Studies Amazon YouTube . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Recommendation Algorithm . Main RS Algorithm . . 1 Content-based recommendation . 2 Collaborative filtering recommendation user-based CF recommendation ( user CF ) item-based CF recommendation ( item CF ) 3. Knowledge-based recommendation . 4 association-based recommendation . hybrid recommendation 5 . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Association Rule-Based Recommendation ( ARBR ) . Target: item6 . f .rom 1-item set to k-items set . item5 Procedure . . 1 For new user u set a item set Userb item4 . 2 Find association-rule R for u u Usera 3. All items right of Ru ⇒ item set item3 . 4 Delete items have been bought . item2 . Disadvantage item1 . H . ard to find association rules . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Association Rule-Based Recommendation ( ARBR ) . Target: item6 . f .rom 1-item set to k-items set . item5 Procedure . . 1 For new user u set a item set Userb item4 . 2 Find association-rule R for u u Usera 3. All items right of Ru ⇒ item set item3 . 4 Delete items have been bought . item2 . Disadvantage item1 . H . ard to find association rules . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Content-Based Recommendation ( CBR ) . 1. Extract item i ’s attribute features . Content(i) = (Wi1 , Wi2 , · · · · · · , Wik ) . . 2. Compute user c ’s bias . ContenBaseProfile( c ) = (Wc1 , Wc2 , · · · · · · , Wck ) Wcj means the important of key word kj to user c. . . 3. Matching Key words, filtering low relative items . U( c, i ) = score( ContenBaseProfile( c ) , Content( i ) ) hard to extract features from videos and sound. . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Collaborative Filtering Recommendation ( CFR ) . process . } { U = u1 , u2 , · · · , um predict score Get score matrix → CF → I = i1 , i2 , · · · , in top-N R . . flowsheet . . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Memory-basedUser-based Collaborative Filtering . The Process of User-CF . Scan all users, find similar interest users, use their scores to predict the scores of user u. . 1 Establish a m × n matrix 2. Search neighbors using similarity algorithm . 3 Recommendation generation . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. similarity algorithm . Cosine Function . ∑n ⃗ ·⃗ a b Rai Rbi Sim(Ua , Ub ) = cos(⃗ , ⃗ = a b) = √∑ i=1 √∑ ||⃗ || · ||⃗ a b|| n 2 n 2 . i=1 Rai i=1 Rai . Pearson Function . ∑ (RUa ,s − mUa )(RUb ,s − mUb ) s∈Sa,b Sim(Ua , Ub ) = √ ∑ ∑ (RUa ,s − mUa )2 (RUb ,s − mUb )2 s∈Sa,b s∈Sa,b . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Item-based Collaborative Filtering . The Process of Item-CF . . 1 Compute similarity among items by using similarity algorithm . 2 Find several most similar items neighbors of target item . By weighting items’ scores as the target item’s predicted value 3 . . Item prediction by similar items’ scores . . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. How we predict the Pu,i (user u’s score for item) ∑ ∑ j∈NBS sim(i,j)·Ru,j Pu,i = j∈NBS (|sim(i,j)|) NBS: neighbors set of item i, Ru,j : score of user for item j . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. User-based CF and Item-based CF . User-based CF . . Item-based CF . 1 User-item matrix . . 1 User-item matrix . 2 Find similar users . 2 Find similar items 3. Compute the similarity of 3. Compute the similarity of two users’ items two items for all users . 4 Recommend similar users’ 4. Recommend items which choices have high-similarity 5. Asking friends for a 5. People who buy x also buy y recommendation . 6 People like similar stuff . 6 User like items of user who which they like before share similar interests . . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. User-based CF and Item-based CF . User-based CF . . Item-based CF . 1 User-item matrix . . 1 User-item matrix . 2 Find similar users . 2 Find similar items 3. Compute the similarity of 3. Compute the similarity of two users’ items two items for all users . 4 Recommend similar users’ 4. Recommend items which choices have high-similarity 5. Asking friends for a 5. People who buy x also buy y recommendation . 6 People like similar stuff . 6 User like items of user who which they like before share similar interests . . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. User-based CF and Item-based CF . User-based CF . . Item-based CF . 1 User-item matrix . . 1 User-item matrix . 2 Find similar users . 2 Find similar items 3. Compute the similarity of 3. Compute the similarity of two users’ items two items for all users . 4 Recommend similar users’ 4. Recommend items which choices have high-similarity 5. Asking friends for a 5. People who buy x also buy y recommendation . 6 People like similar stuff . 6 User like items of user who which they like before share similar interests . . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. User-based CF and Item-based CF . User-based CF . . Item-based CF . 1 User-item matrix . . 1 User-item matrix . 2 Find similar users . 2 Find similar items 3. Compute the similarity of 3. Compute the similarity of two users’ items two items for all users . 4 Recommend similar users’ 4. Recommend items which choices have high-similarity 5. Asking friends for a 5. People who buy x also buy y recommendation . 6 People like similar stuff . 6 User like items of user who which they like before share similar interests . . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. User-based CF and Item-based CF . User-based CF . . Item-based CF . 1 User-item matrix . . 1 User-item matrix . 2 Find similar users . 2 Find similar items 3. Compute the similarity of 3. Compute the similarity of two users’ items two items for all users . 4 Recommend similar users’ 4. Recommend items which choices have high-similarity 5. Asking friends for a 5. People who buy x also buy y recommendation . 6 People like similar stuff . 6 User like items of user who which they like before share similar interests . . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. User-based CF and Item-based CF . User-based CF . . Item-based CF . 1 User-item matrix . . 1 User-item matrix . 2 Find similar users . 2 Find similar items 3. Compute the similarity of 3. Compute the similarity of two users’ items two items for all users . 4 Recommend similar users’ 4. Recommend items which choices have high-similarity 5. Asking friends for a 5. People who buy x also buy y recommendation . 6 People like similar stuff . 6 User like items of user who which they like before share similar interests . . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Model-based Collaborative Filtering and other RS . Model-based CF RS More users and items, sparser matrix and worse recommendation quality Model from training users’ history, when new user comes, use this model to predict and recommend . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Model-based Collaborative Filtering and other RS . Model-based CF RS More users and items, sparser matrix and worse recommendation quality Model from training users’ history, when new user comes, use this model to predict and recommend Demographic-based Recommendation 基于用户统计信息 Categorize users based on personal attributes ( ASL,... ) and make recommendation based on demographic classes . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Model-based Collaborative Filtering and other RS . Model-based CF RS More users and items, sparser matrix and worse recommendation quality Model from training users’ history, when new user comes, use this model to predict and recommend Demographic-based Recommendation 基于用户统计信息 Categorize users based on personal attributes ( ASL,... ) and make recommendation based on demographic classes Utility-based Recommendation 基于效用 . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Model-based Collaborative Filtering and other RS . Model-based CF RS More users and items, sparser matrix and worse recommendation quality Model from training users’ history, when new user comes, use this model to predict and recommend Demographic-based Recommendation 基于用户统计信息 Categorize users based on personal attributes ( ASL,... ) and make recommendation based on demographic classes Utility-based Recommendation 基于效用 Knowledge-based Recommendation Inference technique 推理技术 . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Model-based Collaborative Filtering and other RS . Model-based CF RS More users and items, sparser matrix and worse recommendation quality Model from training users’ history, when new user comes, use this model to predict and recommend Demographic-based Recommendation 基于用户统计信息 Categorize users based on personal attributes ( ASL,... ) and make recommendation based on demographic classes Utility-based Recommendation 基于效用 Knowledge-based Recommendation Inference technique 推理技术 Hybrid Recommendation . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. A Example of Demographic-based Recommendation . Refrence: 动态推荐系统关键技术研究.xlvector . Male users is more than female users in IMDB No Cold Start Problem: the recommendation is rough . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. The Comparison of Main Recommendation System . RS Advantage Disadvantage 关联规则推荐 发现新兴趣点,不 关联规则难以获 需要领域知识 取,个性化程度低 基于内容推荐 推荐结果简单,不 新用户问题,需要 需要领域知识 足够对象构造分类 器 协同过滤推荐 跨类兴趣推荐,自 “cold start”,稀疏 动化程度高,处理 性问题,需大量历 非机构化对象 史数据 基于用户信息 发现新兴趣点,不 获得人口统计信息 需领域知识 难度大 基于知识推荐 把用户的需求映射 知识难以获得,推 到产品上,考虑非 荐是静态的 . 产品属性 . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Main Contents 1. No So Brief Introduction The categories of RS The Challenge Human Recommendation VS. Recommendation System 2. Case Studies Amazon YouTube . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. 5 Problems of Recommender Systems (1st) . Lack of Data . firstly needs item data,then capture and analyze user data excellent recommendations are those with lots of consumer user data. Such as Amazon Google . . Changing Data . too many product attributes in fashion and each attribute has a different level of importance at different times for the same consumer the trends are always changing . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. 5 Problems of Recommender Systems (2nd) . Changing User Preference . books for myself or a birthday present for my sister . . Unpredictable Items . the user reaction to items tends to be diverse and unpredictable . . This Stuff is Complex . takes a lot of variables to do even the simplest recommendations Netflix looking for a 10% improvement on their algorithm . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Recommendation In Reality ( 1st ) . Real Recommendation System must satisfy following demands . RS must fullfill users’ demands The data generated is beneficial for RS’s development RS must fullfill owner’s demands . . Data Recommender Data Customer Owner Rec. system Data . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Recommendation In Reality ( 2nd ) . What users want ? . Good Recommendation from Recommender System . . How do we judge a RS ? . accuracy — recommend items that users like coverage — long tail effect (personalization) diversity — recommend kinds of items novelty and serendipity — user experience is important . . What owner will consider ? . long tail effect sale specific goods — (Promotion,unsaleable goods) . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Recommendation In Reality ( 3rd ) . Society bias and user bias shifting . . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Recommendation In Reality ( 4th ) . Item bias shifting . . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Recommendation In Reality ( 5th ) . Cold Start is a problem, But we can: . Hybrid RS combining association-basedcontent-baseddemographic-based RS Social Recommendation (Ask friends for recommendations) Amazon makes Social Recommendation using Facebook Connector ( API ) . . What we know more ? . If all users rely on a Recommender System, the recommendation algorithm’s accuracy will be 100% Even recommendation algorithm doesn’t improve, the accuracy will rise. . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Main Contents 1. No So Brief Introduction The categories of RS The Challenge Human Recommendation VS. Recommendation System 2. Case Studies Amazon YouTube . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Reference Comparing Recommendations Made by Online Systems and Friends . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Paper’s Study Goal,Study design,Study method . What we know after reading this paper . Goal:paper compares RS by online RS and friends. book RS: Amazon, RatingZone, Sleeper movie RS: Amazon, MovieCritic, Reel.com . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Paper’s Study Goal,Study design,Study method . What we know after reading this paper . Goal:paper compares RS by online RS and friends. book RS: Amazon, RatingZone, Sleeper movie RS: Amazon, MovieCritic, Reel.com Definition: good R: interest the user useful R: user’s interest, no experienced before trust-generating R: positive experiences previously . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Paper’s Study Goal,Study design,Study method . What we know after reading this paper . Goal:paper compares RS by online RS and friends. book RS: Amazon, RatingZone, Sleeper movie RS: Amazon, MovieCritic, Reel.com Definition: good R: interest the user useful R: user’s interest, no experienced before trust-generating R: positive experiences previously Method: participants:19 people,20 to 35 years,6 males 13 females procedure:complete registration; rated items on each RS in order to get recommendations; review list of recommendations; complete satisfaction and usability questionaire. three frends recommend 3 books and movies which the user haven’t discussed with friends before. . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Conclusion . 1. User prefer recommendation made by their friends to those made by the set of online RS. . 2 User expressed a high level of overall satisfaction with online RS. 3. Design recommendations for RS: user don’t mind rating more items initially to receive quality recommendation Allow users to provide initial rating on a continuous rather than binary choice scale provide enough information about the recommended item for user to make a decision provide easy ways to generate new recommendation sets Interface matters, mostly when it gets in the way . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Conclusion . 1. User prefer recommendation made by their friends to those made by the set of online RS. . 2 User expressed a high level of overall satisfaction with online RS. 3. Design recommendations for RS: user don’t mind rating more items initially to receive quality recommendation Allow users to provide initial rating on a continuous rather than binary choice scale provide enough information about the recommended item for user to make a decision provide easy ways to generate new recommendation sets Interface matters, mostly when it gets in the way . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Conclusion . 1. User prefer recommendation made by their friends to those made by the set of online RS. . 2 User expressed a high level of overall satisfaction with online RS. 3. Design recommendations for RS: user don’t mind rating more items initially to receive quality recommendation Allow users to provide initial rating on a continuous rather than binary choice scale provide enough information about the recommended item for user to make a decision provide easy ways to generate new recommendation sets Interface matters, mostly when it gets in the way . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Conclusion . 1. User prefer recommendation made by their friends to those made by the set of online RS. . 2 User expressed a high level of overall satisfaction with online RS. 3. Design recommendations for RS: user don’t mind rating more items initially to receive quality recommendation Allow users to provide initial rating on a continuous rather than binary choice scale provide enough information about the recommended item for user to make a decision provide easy ways to generate new recommendation sets Interface matters, mostly when it gets in the way . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Conclusion . 1. User prefer recommendation made by their friends to those made by the set of online RS. . 2 User expressed a high level of overall satisfaction with online RS. 3. Design recommendations for RS: user don’t mind rating more items initially to receive quality recommendation Allow users to provide initial rating on a continuous rather than binary choice scale provide enough information about the recommended item for user to make a decision provide easy ways to generate new recommendation sets Interface matters, mostly when it gets in the way . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Conclusion . 1. User prefer recommendation made by their friends to those made by the set of online RS. . 2 User expressed a high level of overall satisfaction with online RS. 3. Design recommendations for RS: user don’t mind rating more items initially to receive quality recommendation Allow users to provide initial rating on a continuous rather than binary choice scale provide enough information about the recommended item for user to make a decision provide easy ways to generate new recommendation sets Interface matters, mostly when it gets in the way . . . . . . . A Study of Recommender System
  • The categories of RS No So Brief Introduction The Challenge Case Studies Human Recommendation VS. Recommendation System. Conclusion . 1. User prefer recommendation made by their friends to those made by the set of online RS. . 2 User expressed a high level of overall satisfaction with online RS. 3. Design recommendations for RS: user don’t mind rating more items initially to receive quality recommendation Allow users to provide initial rating on a continuous rather than binary choice scale provide enough information about the recommended item for user to make a decision provide easy ways to generate new recommendation sets Interface matters, mostly when it gets in the way . . . . . . . A Study of Recommender System
  • No So Brief Introduction Amazon Case Studies YouTube. Main Contents 1. No So Brief Introduction The categories of RS The Challenge Human Recommendation VS. Recommendation System 2. Case Studies Amazon YouTube . . . . . . A Study of Recommender System
  • No So Brief Introduction Amazon Case Studies YouTube. Reference Amazon.com Recommendations Item-to-Item Collaborative Filtering . . . . . . A Study of Recommender System
  • No So Brief Introduction Amazon Case Studies YouTube. Amazon’s Demand and Solution . Demand . Amazon has more than 29 million customers and several million catalog items Amazon use recommendation algorithms to personalize the online store for each customer in real time. . . Solution . Existing algorithms were evaluated over small data sets. . Reduce M by randomly sampling the customers or discarding 1 customers with few purchases (M:the number of customers) . 2 Reduce N by discarding very popular or unpopular items (N:the number of items) . 3 Dimensionality reduction techniques such as clustering and principal component analysis can reduce M or N . . . . . . . A Study of Recommender System
  • No So Brief Introduction Amazon Case Studies YouTube The Amazon Item-to-Item Collaborative Filtering. Algorithm . For each item in product catalog, I1 For each customer C who purchased I1 For each item I2 purchased by customer C Record that a customer purchased I1 and I2 For each item I2 . Compute the similarity between I1 and I2 . Algorithm Complexity . Worst case: O(N2 M) In practice: O(NM) , ’cause customers have few purchases Sampling customers who purchase best-selling titles reduces runtime even further . . . . . . . A Study of Recommender System
  • No So Brief Introduction Amazon Case Studies YouTube. Main Contents 1. No So Brief Introduction The categories of RS The Challenge Human Recommendation VS. Recommendation System 2. Case Studies Amazon YouTube . . . . . . A Study of Recommender System
  • No So Brief Introduction Amazon Case Studies YouTube. From ten pages to four pages . Reference papers . Shumeet Baluja etc.,2008,Video Suggestion and Discovery for YouTube: Taking Random Walks Through the View Graph. James Davidson etc.,2010,The YouTube Recommendation System. . . . . . . . A Study of Recommender System
  • No So Brief Introduction Amazon Case Studies YouTube. Taking Random Walks Through the View Graph (1st) . Random Walk in multi-demension . some walks take their steps at random times a fundamental topic in discussions of Markov processes . . . . . . . A Study of Recommender System
  • No So Brief Introduction Amazon Case Studies YouTube. Taking Random Walks Through the View Graph (2nd) . Co-view Graph . Each video is a vertex in the graph that is linked to other videos . . The Adsorption Algorithm . classify a node in a graph in terms of labels present on some of the other nodes. . u and v have a short path between them 1 . 2 u and v have several paths between them . 3 u and v have paths that avoid high-degree nodes . . . . . . . A Study of Recommender System
  • No So Brief Introduction Amazon Case Studies YouTube. Taking Random Walks Through the View Graph (3rd) . Adsorption via Averaging . Input: G = (V, E, w), L, VL repeat ∪˜ for each v ∈ V∑ V do: Let Lv = u w(u, v)Lu end-for Normalize Lv to have unit L1 norm until covergence Output: Distributions Lv |v ∈ V . . . . . . . A Study of Recommender System
  • No So Brief Introduction Amazon Case Studies YouTube. Taking Random Walks Through the View Graph (4th) . Adsorption via Random Walks . Input: G = ∪ E, w),L,VL , distinguished vertex v: (V, ˜ ˜ ∪ v Let G = (V V, E (v, ˜)|v ∈ VL , w). Define w(v, ˜) = 1 for all v ∈ VL v done := false vertex := v while ( not done ) do: vertex := pick-neighbor(v, E, w) ˜ if(neighbor ∈ V) done := true end-while u := vertex Output: label according to Lu . . . . . . . . A Study of Recommender System
  • No So Brief Introduction Amazon Case Studies YouTube. Taking Random Walks Through the View Graph (5th) . Adsorption via Linear Systems . Input: G= (V, E, w) Let n:=|V| Define the linear system of equations in n2 variables Xuv , for u, v ∈ V: ∑ v Xuv = 1 ∀u ∈ V; ∑ z:(z,u)∈E w(z, u)Xuv = Xuv ∀u, v ∈ V. incremental update to the label distributions or addition or deletion of nodes can be easily accommodated by quickly updating the information for the relevant neighborhood of the graph. . . . . . . . A Study of Recommender System
  • No So Brief Introduction Amazon Case Studies YouTube. The YouTube Video Recommendation System (1st) . Challenges . No or very poor metadata User interactions are relatively short and noisy . . System Design . recommend recent and fresh as well as diverse and relevant to user’s actions. generated by using a user’s personal activity. . . Input Data . Content data User activity data (explicit data , implicit data) . . . . . . . A Study of Recommender System
  • No So Brief Introduction Amazon Case Studies YouTube. The YouTube Video Recommendation System (2nd) . Related Videos . . 1 A given time period, we count for each pair of videos v ,v how i j often they were co-watched. . 2 We denote this co-visitation count by c j. i 3. We define related score of video vj to vi as : c r(vi , vj ) = f(vi ij j ) ,v . 4 f(v , v ) is a normalization function that denote the global i j popularity . 5 Pick the set of related videos R for a given seed video v as i i the top N candidate videos ranked by their scores r(vi , vj ). . . . . . . . A Study of Recommender System
  • No So Brief Introduction Amazon Case Studies YouTube. The YouTube Video Recommendation System (3rd) . Generating Recommendation Candidates . . 1 A given seed set S (e.g. the videos user watched) . 2 Each video v in the seed set consider its related videos R i i . 3 Denote the union of these related video sets as C : 1 ∪ C1 (S) = Ri vi ∈CS . Distance of n from any video in the seed set: 4 ∪ Cn (S) = Ri vi ∈Cn−1 . . . . . . . A Study of Recommender System
  • No So Brief Introduction Amazon Case Studies YouTube. The YouTube Video Recommendation System (4th) . Generating Recommendation Candidates . The final candidate set Cfinal : ∪ N Cfinal = ( Ci ) − S i=0 Due to the high branching factor of the related videos graph we found expanding over a small distance yielded broad and diverse recommendations even for users with a small seed set. . . Ranking . Using a linear combination of three kinds of signals( a.video quality b.user specifility c.diversification ),we generate a ranked list . of the candidate videos. . . . . . . A Study of Recommender System
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