CompSci 1: Overview CS0. Collaborative Filtering: A Tutorial. Collaborative Filtering. Everyday Examples of Collaborative Filtering...
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1 CompSci 1: Overview CS0 Collaborative Filtering: A Tutorial! Audioscrobbler and last.fm! Collaborative filtering! What is a neighbor?! What is the network? Drawn from tutorial by William W. Cohen Center for Automated Learning and Discovery Carnegie Mellon University HarambeNet Workshop HarambeNet Workshop Collaborative Filtering Everyday Examples of Collaborative Filtering! Goal: predict the utility of an item to a particular user based on a database of user profiles! User profiles contain user preference information! Preference may be explicit or implicit Explicit means that a user votes explicitly on some scale Implicit means that the system interprets user behavior or selections to impute a vote! Problems! Missing data: voting is neither complete nor uniform! Preferences may change over time! Interface issues HarambeNet Workshop HarambeNet Workshop
2 Everyday Examples of Collaborative Filtering Rate it? The Dark Star's crew is on a 20-year mission..but unlike Star Trek the nerves of this crew are frayed to the point of psychosis. Their captain has been killed by a radiation leak that also destroyed their toilet paper. "Don't give me any of that 'Intelligent Life' stuff," says Commander Doolittle when presented with the possibility of alien life. "Find me something I can blow up. HarambeNet Workshop HarambeNet Workshop Everyday Examples of Collaborative Filtering HarambeNet Workshop HarambeNet Workshop
3 Google s PageRank a b c HarambeNet Workshop HarambeNet Workshop a b c pdq pdq.. Inlinks are good (recommendations) Inlinks from a good are better than inlinks from a bad but inlinks from s with many outlinks are not as good Good and bad are relative. Google s PageRank Google s PageRank (Brin & Page, Imagine a pagehopper that always either Imagine a pagehopper that always either a b c follows a random link, or jumps to random page a b c follows a random link, or jumps to random page a b c pdq pdq.. a b c pdq pdq.. PageRank ranks pages by the amount of time the pagehopper spends on a page: or, if there were many pagehoppers, PageRank is the expected crowd size HarambeNet Workshop HarambeNet Workshop
4 Everyday Examples of Collaborative Filtering BellCore s MovieRecommender! Bestseller lists! Top 40 music lists! The recent returns shelf at the library! Unmarked but well-used paths thru the woods! The printer room at work! Many logs! Read any good books lately?!.! Common insight: personal tastes are correlated:! If Alice and Bob both like X and Alice likes Y then Bob is more likely to like Y! especially (perhaps) if Bob knows Alice! Participants sent to videos@bellcore.com! System replied with a list of 500 movies to rate New participant P sends in rated movies via ! System compares ratings for P to ratings of (a random sample of) previous users! Most similar users are used to predict scores for unrated movies! Empirical Analysis of Predictive Algorithms for Collaborative Filtering Breese, Heckerman, Kadie, UAI98! System returns recommendations in an message. HarambeNet Workshop HarambeNet Workshop BellCore s MovieRecommender MovieRecommender Goals! Recommending And Evaluating Choices In A Virtual Community Of Use. Will Hill, Larry Stead, Mark Rosenstein and George Furnas, Bellcore; CHI 1995 By virtual community we mean "a group of people who share characteristics and interact in essence or effect only". In other words, people in a Virtual Community influence each other as though they interacted but they do not interact. Thus we ask: "Is it possible to arrange for people to share some of the personalized informational benefits of community involvement without the associated communications costs?" HarambeNet Workshop Recommendations should:! simultaneously ease and encourage rather than replace social processes.should make it easy to participate while leaving in hooks for people to pursue more personal relationships if they wish.! be for sets of people not just individualsmulti-person recommending is often important, for example, when two or more people want to choose a video to watch together.! be from people not a black box machine or so-called "agent".! tell how much confidence to place in them, in other words they should include indications of how accurate they are. HarambeNet Workshop
5 BellCore s MovieRecommender! Participants sent to videos@bellcore.com! System replied with a list of 500 movies to rate on a 1-10 scale (250 random, 250 popular)! Only subset need to be rated! New participant P sends in rated movies via ! System compares ratings for P to ratings of (a random sample of) previous users! Most similar users are used to predict scores for unrated movies (more later)! System returns recommendations in an message. Suggested Videos for: John A. Jamus. Your must-see list with predicted ratings: 7.0 "Alien (1979)" 6.5 "Blade Runner" 6.2 "Close Encounters Of The Third Kind (1977)" Your video categories with average ratings: 6.7 "Action/Adventure" 6.5 "Science Fiction/Fantasy" 6.3 "Children/Family" 6.0 "Mystery/Suspense" 5.9 "Comedy" 5.8 "Drama" HarambeNet Workshop HarambeNet Workshop The viewing patterns of 243 viewers were consulted. Patterns of 7 viewers were found to be most similar. Correlation with target viewer: 0.59 viewer-130 (unlisted@merl.com) 0.55 bullert,jane r (bullert@cc.bellcore.com) 0.51 jan_arst (jan_arst@khdld.decnet.philips.nl) 0.46 Ken Cross (moose@denali.ee.cornell.edu) 0.42 rskt (rskt@cc.bellcore.com) 0.41 kkgg (kkgg@athena.mit.edu) 0.41 bnn (bnn@cc.bellcore.com) By category, their joint ratings recommend: Action/Adventure: "Excalibur" 8.0, 4 viewers "Apocalypse Now" 7.2, 4 viewers "Platoon" 8.3, 3 viewers Science Fiction/Fantasy: "Total Recall" 7.2, 5 viewers Children/Family: "Wizard Of Oz, The" 8.5, 4 viewers "Mary Poppins" 7.7, 3 viewers Mystery/Suspense: "Silence Of The Lambs, The" 9.3, 3 viewers Comedy: "National Lampoon's Animal House" 7.5, 4 viewers "Driving Miss Daisy" 7.5, 4 viewers "Hannah and Her Sisters" 8.0, 3 viewers Drama: "It's A Wonderful Life" 8.0, 5 viewers "Dead Poets Society" 7.0, 5 viewers "Rain Man" 7.5, 4 viewers Correlation of predicted ratings with your actual ratings is: 0.64 This number measures ability to evaluate movies accurately for you means low ability means very good ability means fair ability. HarambeNet Workshop BellCore s MovieRecommender! Evaluation:! Withhold 10% of the ratings of each user to use as a test set! Measure correlation between predicted ratings and actual ratings for test-set movie/user pairs HarambeNet Workshop
6 Another key observation: rated movies tend to have positive ratings: i.e., people rate what they watch, and watch what they like Question: Can observation replace explicit rating? HarambeNet Workshop HarambeNet Workshop HarambeNet Workshop HarambeNet Workshop
7 HarambeNet Workshop HarambeNet Workshop HarambeNet Workshop HarambeNet Workshop
8 TrustMail (back) HarambeNet Workshop HarambeNet Workshop Filtering! Boykin and Roychowdhury (2004) use social networks derived from folders to classify messages as spam or not spam! 50% of messages can be classified How does CF actually work? HarambeNet Workshop HarambeNet Workshop
9 Memory-Based Methods (Breese et al, UAI98)! v i,j = vote of user i on item j! I i = items for which user i has voted! Mean vote for i is Computing the weight! K-nearest neighbor # 1 if i $ neighbors( a) w( a, i) = "! 0 else! Pearson correlation coefficient (Resnick 94, Grouplens):! Predicted vote for active user a is weighted sum! Cosine distance (from IR) normalizer weights of n similar users HarambeNet Workshop HarambeNet Workshop Calculating the weight Visualizing Cosine Distance! Cosine with inverse user frequency f i = log(n/n j ), where n is number of users, n j is number of users voting for item j v( a, j) similarity of doc a to doc b = sim( a, b) = "! 2 word i r " v ( a, j') Let A =<, v( a, j), > j' r r r A A Let A' = r = 2 A! v ( a, j') word 1 j' word 2 v( b, j) " j' 2 v ( b, j') = A'! B' doc c doc d doc a word j doc b HarambeNet Workshop word n HarambeNet Workshop
10 Visualizing Cosine Distance distance from user a to user i = Visualizing Cosine Distance Approximating Matrix Multiplication for Pattern Recognition Tasks, Cohen & Lewis, SODA 97 explores connection between cosine distance/inner product and random walks item 1 item 2 item 1 item 2 user a item j user i user a item j user i Suppose user-item links were probabilities of following a link item n Then w(a,i) is probability of a and i meeting Suppose user-item links were probabilities of following a link item n Then w(a,i) is probability of a and i meeting HarambeNet Workshop HarambeNet Workshop Model-based methods! Really what we want is the expected value of the user s vote! Cluster Models Users belong to certain classes in C with common tastes Naive Bayes Formulation Calculate Pr(v i C=c) from training set! Bayesian Network Models Other issues, not addressed much! Combining and weighting different types of information sources! How much is a page link worth vs a link in a newsgroup?! Spamming how to prevent vendors from biasing results?! Efficiency issues how to handle a large community?! What do we measure when we evaluate CF?! Predicting actual rating may be useless!! Example: music recommendations: Beatles, Eric Clapton, Stones, Elton John, Led Zep, the Who,! What s useful and new? for this need model of user s prior knowledge, not just his tastes. Subjectively better recs result from poor distance metrics HarambeNet Workshop HarambeNet Workshop
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