towards trust-aware recommender systems

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  • 1.Introduction Recommender systems Related workA recommender system for Twitter Experiments Conclusions Towards trust-aware recommender systemsAlberto Lumbreras Ricard Gavald (Advisor)July 2012

2. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions1. Introduction2. Recommender systems3. Related work4. A recommender system for Twitter5. Experiments6. Conclusions 3. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions1. Introduction2. Recommender systems3. Related work4. A recommender system for Twitter5. Experiments6. Conclusions 4. IntroductionRecommender systems Related work A recommender system for Twitter Experiments ConclusionsIntroduction The paradox of choice: "more is less" Recommender systems to reduce information overload Social networks information to enhance recommendations 5. IntroductionRecommender systems Related work A recommender system for Twitter Experiments ConclusionsAims of the Thesis Recommend tweets to users based on theirsocial network information Studying the concept of trust in Twitter Can trust improve tweet recommendations? What other techniques are useful? 6. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions1. Introduction2. Recommender systems3. Related work4. A recommender system for Twitter5. Experiments6. Conclusions 7. IntroductionRecommender systems Related work A recommender system for Twitter Experiments ConclusionsRecommender systems Collaborative Filtering: Look at "similar users" (similar ratings) Average their ratings weighted by closeness. Sparsity Cold-start problem Content Based: Based on items similarity / machine learning Features extracted automatically or by experts Cold-start problem What features are relevant? Trust-aware: Use trust to enhance recommendation methods What is trust? How to compute and propagate trust? 8. IntroductionRecommender systemsRelated workA recommender system for Twitter Experiments Conclusions Computing Trust Direct trust computation: Explicit: Users annotations Implicit: Inferred from users behavior and/or interactions Trust propagation: Algorithms t network properties (decay, trust horizon,...) Network as Markov Chain: PageRank, EigenTrust... ... Trust-aware recommendations: Trust + Collaborative Filtering Trust + Content Based Filtering ... 9. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions1. Introduction2. Recommender systems3. Related work4. A recommender system for Twitter5. Experiments6. Conclusions 10. IntroductionRecommender systems Related workA recommender system for Twitter Experiments ConclusionsRelated work TidalTrust: Movie recommendation. Customized algorithm for trust propagation Direct trust explicitly annotated by users (0-10 range) Pro: Do not normalize trust Con: Requires annotated direct trust EigenTrust: Reputation in P2P networks Direct trust inferred from proportion of successful downloads Trust propagation by Random Walk Distributed computation Pro: Simple algorithm Con: Do not explicitly consider network properties 11. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions1. Introduction2. Recommender systems3. Related work4. A recommender system for Twitter5. Experiments6. Conclusions 12. IntroductionRecommender systems Related work A recommender system for Twitter Experiments ConclusionsGoals and challenges GOAL: Recommend tweets to users based on their social network information CHALLENGES: In GETTING the data: APIs limits Implement a crawler Crawling criteria In ANALYZING the data: No explicit feedback mechanism Textual items of 140 characters Items volatility (hours or days) Very high sparsity (items rated (retweets) by few users) 13. IntroductionRecommender systems Related work A recommender system for Twitter Experiments Conclusions Contributions Trust metric (for social networks) Trust-aware crawler (for social networks) Recommender system prototype Analysis of trust properties in Twitter 14. IntroductionRecommender systems Related work A recommender system for Twitter Experiments ConclusionsComputing trust in TwitterInteractions in Twitter tweet: @xamat: Markov Random Fields for #recsys: http://t.co/zHggOl2r mention: @bob: Nice tutorial @xamat! @bob: Not a very good tutorial @xamat... trust or distrust (assumption: mostly trust) retweet (forward) : @charles: RT "@xamat: Markov Random Fields for #recsys: http://t.co/zHggOl2r" @charles: So bad! RT "@xamat: Markov Random Fields for #recsys:http://t.co/zHggOl2r" trust or distrust (observation: mostly trust) favorite: store a friends tweet observation: mostly trust follow/unfollow : users subscribe/unsubscribe to other users publications 15. IntroductionRecommender systems Related work A recommender system for Twitter ExperimentsConclusionsComputing trust in Twitter Direct trust Mentions and retweets as a sign of trust Trust tij as proportion of interactions of user i with user j. (m) (rt) wNij+ (1 w )Nij tij =(1) Ni Temporal decay of interactions:Ni (t) = Ni (t) + (1 )Ni (t 1)(2) 16. IntroductionRecommender systemsRelated workA recommender system for Twitter Experiments Conclusions Computing trust in TwitterTrust propagation through random walk Step 1: With direct trusts, build transition probabilities matrix P 0 0.20.80 0 0 0.5 0.5 P= 0 00 1 0.30.30.30 17. IntroductionRecommender systemsRelated work A recommender system for Twitter ExperimentsConclusions Computing trust in TwitterTrust propagation through random walk Step 2: Propagate trust 1T3 = (1 P +2P2+3P3) (3) 3 walk 1 step walk 2 steps walk 3 stepss1Ts =n P n (4)s n=1P: direct trust matrix (transition matrix)s: trust horizonn : path length penalization n Note: Assumes transitivity 18. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Architecture 19. Introduction Recommender systems Related workA recommender system for Twitter Experiments ConclusionsCrawlerAlgorithm 1: Crawling cyclewhile True doS SeedUsers() TopTrustedUsers()foreach s S dostatuses GetLastUpdates(s)UpdateInteracctionsMatrix(s, statuses)endTruncateInteractionsMatrix() /*remove unsignicant users*/UpdateTrustMatrix()UpdateTopTrustedUsers()end 20. IntroductionRecommender systems Related work A recommender system for Twitter Experiments ConclusionsRecommender Optional: Tweet expansion. Optional: bag-of-words or tfPOS /tfNEG ratio. Tweet candidates from top-trusted neighborhood Learn to predict retweets. 21. Introduction Recommender systems Related workA recommender system for Twitter Experiments Conclusions RecommenderQuery (tweet) expansion Query expansion: Bag of words probably not enough. Too few word coincidences between tweets If expanding the query (i.e: synonyms) more chances to get coincidences Query expansion proved useful in some scenarios (i.e: for QA systems with search engines) Tweet expansion: Query Bing with tweets Get rst 200 results (summaries) Add summaries words to tweet 22. IntroductionRecommender systems Related work A recommender system for Twitter Experiments ConclusionsRecommenderScoring: Trust-aware + Content-based Features: has_url [True, False] bag-of-words or tf ratio trust [0,1] Label: retweet [True, False] 23. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions1. Introduction2. Recommender systems3. Related work4. A recommender system for Twitter5. Experiments6. Conclusions 24. IntroductionRecommender systems Related workA recommender system for Twitter Experiments Conclusions Dataset 20 target users 6 month crawling Spanish, Catalan, English 314 instances/user (50% retweets, 50% non-retweets) 70% training, 30% test. Oine testing (a posteriori prediction of retweets) 25. IntroductionRecommender systems Related work A recommender system for Twitter Experiments ConclusionsTransitivity tests Normalize trust rankings [0-10] : Disagreement about ranking of common neighbors. If transitivity: the higher trust on a user, the smaller between their ratings 26. IntroductionRecommender systems Related workA recommender system for Twitter Experiments ConclusionsTransitivity tests Interactions Question: are interactions of Twitter users transitive?Table: Relation of interactions rank and deltaRanking [0-10] [0 1)1.36 [1 2)0.75 [2 3)1.14 [3 4)0.83 [4 5)0.78 [5 6)0.52 [6 7)1.06 [7 8)0.53 [8 9)0.57[9 10)0.17 Agreement about common neighbors (no matter the ) ... but we do not see transitivity. 27. IntroductionRecommender systems Related workA recommender system for Twitter Experiments ConclusionsTransitivity tests Trust dierent path decays (n )Table: Relation of trust rank and deltaRanking (0-10) No decayLinear decay Exp.decay (0-1) 1.140.90 0.87 (1-2) 1.101.22 1.09 (2-3) 0.961.05 1.05 (3-4) 1.071.18 1.20 (4-5) 1.090.81 1.01 (5-6) 1.131.11 0.92 (6-7) 0.911.08 1.16 (7-8) 0.901.08 0.99 (8-9)1.31.34 1.34(9-10) 1.261.11 1.16 we do not see transitivity 28. Introduction Recommender systems Related workA recommender system for Twitter Experiments Conclusions Trust-aware recommendations Benchmarkingtrust slightly improves recommendations Classif.ExpandEncode Acc.RecallPrecisionF1 AUCbow 50.76 59.4352.0755.51 50.93 yes tf 52.38 58.9952.5255.57 52.58 NBbow 48.79 53.0147.7650.25 49.88no Trust tf 51.97 50.4950.6050.44 51.51bow 45.84 61.0030.2240.42 50.53 yes tf 47.63 51.3646.9549.06 47.94SVMbow 46.25 68.4232.4744.04 50.00no tf 47.79 46.3848.4447.39 47.32Averages48.93 56.1345.1349.08 50.09bow 47.02 57.5848.9052.89 49.56 yes tf 51.13 49.5654.8152.05 52.22 NBbow 49.28 47.9850.7649.33 50.54 No trustno tf 46.12 45.1845.8445.51 46.28bow 45.22 55.8327.0536.44 50.06 yes tf 49.23 49.3651.4759.39 49.80SVMbow 43.40 34.8717.5923.38 50.22no tf 47.11 41.5548.8744.91 47.32Averages47.31 47.7343.

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