Collaborative Filtering: A Tutorial William W. Cohen Center as long as Automated Learni

Collaborative Filtering: A Tutorial William W. Cohen Center as long as Automated Learni www.phwiki.com

Collaborative Filtering: A Tutorial William W. Cohen Center as long as Automated Learni

Latina, Tina, Morning On-Air Personality has reference to this Academic Journal, PHwiki organized this Journal Collaborative Filtering: A Tutorial William W. Cohen Center as long as Automated Learning in addition to Discovery Carnegie Mellon University Everyday Examples of Collaborative Filtering

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Everyday Examples of Collaborative Filtering Everyday Examples of Collaborative Filtering

Google’s PageRank web site xxx web site yyyy web site a b c d e f g web site pdq pdq web site yyyy web site a b c d e f g web site xxx Inlinks are “good” (recommendations) Inlinks from a “good” site are better than inlinks from a “bad” site but inlinks from sites with many outlinks are not as “good” “Good” in addition to “bad” are relative. web site xxx Google’s PageRank web site xxx web site yyyy web site a b c d e f g web site pdq pdq web site yyyy web site a b c d e f g web site xxx Imagine a “pagehopper” that always either follows a r in addition to om link, or jumps to r in addition to om page

Google’s PageRank (Brin & Page, http://www-db.stan as long as d.edu/~backrub/google.html) web site xxx web site yyyy web site a b c d e f g web site pdq pdq web site yyyy web site a b c d e f g web site xxx Imagine a “pagehopper” that always either follows a r in addition to om link, or jumps to r in addition to om page 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” Everyday Examples of Collaborative Filtering 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 weblogs “Read any good books lately” . Common insight: personal tastes are correlated: If Alice in addition to Bob both like X in addition to Alice likes Y then Bob is more likely to like Y especially (perhaps) if Bob knows Alice Outline Non-systematic survey of some CF systems CF as basis as long as a virtual community memory-based recommendation algorithms visualizing user-user via item distances CF versus content filtering Algorithms as long as CF CF with different inputs true ratings assumed/implicit ratings Conclusions/Summary

BellCore’s MovieRecommender Recommending And Evaluating Choices In A Virtual Community Of Use. Will Hill, Larry Stead, Mark Rosenstein in addition to George Furnas, Bellcore; CHI 1995 By virtual community we mean “a group of people who share characteristics in addition to 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 as long as people to share some of the personalized in as long as mational benefits of community involvement without the associated communications costs” MovieRecommender Goals Recommendations should: simultaneously ease in addition to encourage rather than replace social processes .should make it easy to participate while leaving in hooks as long as people to pursue more personal relationships if they wish. be as long as sets of people not just individuals multi-person recommending is often important, as long as 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. BellCore’s MovieRecommender Participants sent email to videos@bellcore.com System replied with a list of 500 movies to rate on a 1-10 scale (250 r in addition to om, 250 popular) Only subset need to be rated New participant P sends in rated movies via email System compares ratings as long as P to ratings of (a r in addition to om sample of) previous users Most similar users are used to predict scores as long as unrated movies (more later) System returns recommendations in an email message.

Suggested Videos as long as : 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” 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 in addition to 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 as long as you. 0.15 means low ability. 0.85 means very good ability. 0.50 means fair ability. BellCore’s MovieRecommender Evaluation: Withhold 10% of the ratings of each user to use as a test set Measure correlation between predicted ratings in addition to actual ratings as long as test-set movie/user pairs

Another key observation: rated movies tend to have positive ratings: i.e., people rate what they watch, in addition to watch what they like Question: Can observation replace explicit rating BellCore’s MovieRecommender Participants sent email to videos@bellcore.com System replied with a list of 500 movies to rate New participant P sends in rated movies via email System compares ratings as long as P to ratings of (a r in addition to om sample of) previous users Most similar users are used to predict scores as long as unrated movies Empirical Analysis of Predictive Algorithms as long as Collaborative Filtering Breese, Heckerman, Kadie, UAI98 System returns recommendations in an email message.

Algorithms as long as Collaborative Filtering 1: Memory-Based Algorithms (Breese et al, UAI98) vi,j= vote of user i on item j Ii = items as long as which user i has voted Mean vote as long as i is Predicted vote as long as “active user” a is weighted sum weights of n similar users normalizer Algorithms as long as Collaborative Filtering 1: Memory-Based Algorithms (Breese et al, UAI98) K-nearest neighbor Pearson correlation coefficient (Resnick ’94, Grouplens): Cosine distance (from IR) Algorithms as long as Collaborative Filtering 1: Memory-Based Algorithms (Breese et al, UAI98) Cosine with “inverse user frequency” fi = log(n/nj), where n is number of users, nj is number of users voting as long as item j

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Algorithms as long as Collaborative Filtering 1: Memory-Based Algorithms (Breese et al, UAI98) Evaluation: split users into train/test sets as long as each user a in the test set: split a’s votes into observed (I) in addition to to-predict (P) measure average absolute deviation between predicted in addition to actual votes in P predict votes in P, in addition to as long as m a ranked list assume (a) utility of k-th item in list is max(va,j-d,0), where d is a “default vote” (b) probability of reaching rank k drops exponentially in k. Score a list by its expected utility Ra average Ra over all test users Algorithms as long as Collaborative Filtering 1: Memory-Based Algorithms (Breese et al, UAI98) soccer score golf score Why are these numbers worse Visualizing Cosine Distance similarity of doc a to doc b = doc a doc b word 1 word 2 word j word n doc d doc c

Visualizing Cosine Distance distance from user a to user i = user a user i item 1 item 2 item j item n Suppose user-item links were probabilities of following a link Then w(a,i) is probability of a in addition to i “meeting” Visualizing Cosine Distance user a user i item 1 item 2 item j item n Suppose user-item links were probabilities of following a link Then w(a,i) is probability of a in addition to i “meeting” Approximating Matrix Multiplication as long as Pattern Recognition Tasks, Cohen & Lewis, SODA 97—explores connection between cosine distance/inner product in addition to r in addition to om walks Outline Non-systematic survey of some CF systems CF as basis as long as a virtual community memory-based recommendation algorithms visualizing user-user via item distances CF versus content filtering Algorithms as long as CF CF with different inputs true ratings assumed/implicit ratings

Other issues, not addressed much Combining in addition to weighting different types of in as long as mation sources How much is a web page link worth vs a link in a newsgroup Spamming—how to prevent vendors from biasing results Efficiency issues—how to h in addition to le 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 in addition to new as long as this need model of user’s prior knowledge, not just his tastes. Subjectively better recs result from “poor” distance metrics Final Comments CF is one of a h in addition to ful of learning-related tools that have had broadly visible impact: Google, TIVO, Amazon, personal radio stations, Critical tool as long as finding “consensus in as long as mation” present in a large community (or large corpus of web pages, or large DB of purchase records, .) Similar in some respects to Q/A with corpora Science is relatively-well established in certain narrow directions, on a few datasets Set of applications still being exp in addition to ed Some resources: http://www.sims.berkeley.edu/resources/collab/ http://www.cs.umn.edu/Research/GroupLens/ http://www.cis.upenn.edu/~ungar/CF/

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