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Recommender Systems

Recommender Systems. Martin Ester Simon Fraser University School of Computing Science CMPT 884 Spring 2009. Recommender Systems. Outline Introduction motivation, applications, issues Collaborative filtering user-based, item-based, challenges

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Recommender Systems

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  1. Recommender Systems Martin Ester Simon Fraser University School of Computing Science CMPT 884 Spring 2009 CMPT 884, SFU, Martin Ester, 1-09

  2. Recommender Systems • Outline • Introduction motivation, applications, issues • Collaborative filtering user-based, item-based, challenges • Trust-based recommendation deterministic, random walks, challenges • Model-based recommendation •  [Konstan 2008] [Cohen 2002] CMPT 884, SFU, Martin Ester, 1-09

  3. Recommender Systems • Introduction • search engine users just type in a few keywords • search engine overwhelms user with a flood of results • ranking mechanism based on similarity between query keywords and web pages and on prestige of pages • search engine‘s answers do not take into account user feedback and users‘ preferences •  Information needs more complex than keywords or topics: quality and taste CMPT 884, SFU, Martin Ester, 1-09

  4. Recommender Systems • Introduction • Users are not willing to spend a lot of time to specify their personal information needs • Recommender systems automatically identify relevant information or products relevant for a given user, learning from available data • Data can be transactions of all users / customers of a website or profile of an individual user  users who bought this book also bought . . . (Amazon.com) CMPT 884, SFU, Martin Ester, 1-09

  5. Recommender Systems • Personalization Level • • Generic • everyone receives same recommendations • • Demographic • matches a demographic group • Personalized matches an individual, everybody gets different recommendations • • Ephemeral matches current activity • • Persistent • matches long-term interests CMPT 884, SFU, Martin Ester, 1-09

  6. Recommender Systems • Types of Systems • Filtering interfaces • E-mail filters, clipping services • Recommendation interfaces • suggestion lists, “top-n,” offers and promotions • Prediction interfaces evaluate candidates, predicted ratings CMPT 884, SFU, Martin Ester, 1-09

  7. Recommender Systems • Collaborative Filtering • Main idea • users rate items • users are correlated with other users personal predictions for unrated items • Nearest-Neighbor Approach • find people with history of agreement aggregate their ratings to predict rating of user assume stable tastes •  employs data about the target user and other users CMPT 884, SFU, Martin Ester, 1-09

  8. Target user Recommender Systems Aggregator Prediction CMPT 884, SFU, Martin Ester, 1-09

  9. Recommender Systems • Collaborative Filtering CMPT 884, SFU, Martin Ester, 1-09

  10. Recommender Systems • Collaborative Filtering • Recommendation task 1 • Predicting the rating on a target item for a given user Predicting John’s rating on Star Wars Movie movie1 ?? Recommender CMPT 884, SFU, Martin Ester, 1-09

  11. Recommender Systems • Collaborative Filtering • Recommendation task 2 • Recommending a list of items to a given user Recommending a list of movies to John for watching List of Top Movies ?? Recommender CMPT 884, SFU, Martin Ester, 1-09

  12. Recommender Systems • Applications • Movie recommendations • Book recommendations • Recommendation of friends CMPT 884, SFU, Martin Ester, 1-09

  13. Recommender Systems • Privacy and Trustworthiness • • Who knows what about me? • – personal information revealed • – identity • • Is the recommendation honest? • – biases built-in by operator e.g. want to sell „old hats“ or prefers ads with higher bids • • Vulnerability to external manipulation (fraud) - insert fraudulent user profiles which rate my product highly CMPT 884, SFU, Martin Ester, 1-09

  14. Collaborative Filtering Rating Matrix • Introduction Items Users Ratings Similar user What is Joe’s rating of Blimp and of RockyXV? CMPT 884, SFU, Martin Ester, 1-09

  15. Collaborative Filtering • Example CMPT 884, SFU, Martin Ester, 1-09

  16. Collaborative Filtering • Definitions • vi,j: vote of user i on item j • Ii = items for which user i has voted • mean vote of user i is • predicted vote for active usera on target itemj is weighted sum of votes on j by n “similar” users normalizer weights of n similar users CMPT 884, SFU, Martin Ester, 1-09

  17. Collaborative Filtering • Definitions • K-nearest neighbor • Pearson correlation coefficient • Cosine distance CMPT 884, SFU, Martin Ester, 1-09

  18. Collaborative Filtering • Evaluation[Herlocker 2004] • split users into train/test sets • for each user a in the test set: - split a’s votes into observed (I) and to-predict (P) - measure average absolute deviation between predicted and actual votes in P - alternatively, measure the squared deviation predicted and actual votes in P • average error measure over all test users MAE or RMSE CMPT 884, SFU, Martin Ester, 1-09

  19. Collaborative Filtering • Evaluation • There is a trade-off between precision and recall • Measure also the recall / coverage, i.e. the percentage of (a,i) pairs for which method • can make a recommendation • F-measure considers both precision and recall Max squared error CMPT 884, SFU, Martin Ester, 1-09

  20. Collaborative Filtering • Evaluation • so far, only comparison against ground truth • in industry, want to measure the business profit • user surveys • in an online system • measure click through rates measure add-on sales CMPT 884, SFU, Martin Ester, 1-09

  21. Collaborative Filtering • Challenges • user item rating matrix is very sparsetypically 99% of the entries unknown  dimensionality reduction  item-item based CF • cannot make (accurate) recommendations for cold start users users who have recently joined the system and have rated only very few items (typically, 50% of users)  trust-based recommendation CMPT 884, SFU, Martin Ester, 1-09

  22. Collaborative Filtering • Challenges • the larger the user community - the more variance among the ratings - the more the ratings converge to the mean value  cluster users and use only the corresponding cluster to make a recommendation • cannot compute the confidence of a recommendation system does not know its limits probabilistic methods • vulnerable to fraud copy a user profile and become the most similar user  trust-based recommendation CMPT 884, SFU, Martin Ester, 1-09

  23. Collaborative Filtering • Challenges • need to explain recommendations • how to reward serendipity in the evaluation? recommendations should not all be of the same kind • how to evaluate a set of recommendations? • how to produce the best sequence of recommendations? CMPT 884, SFU, Martin Ester, 1-09

  24. Collaborative Filtering CMPT 884, SFU, Martin Ester, 1-09

  25. Collaborative Filtering  leads to a denser rating, lower-dimensional matrix  can alternatively use Singular Value Decomposition (SVD) or Latent Semantic Indexing (LSI) CMPT 884, SFU, Martin Ester, 1-09

  26. Collaborative Filtering • Item-Item Collaborative Filtering [Sarwar et al 2001] • Many applications have many more users (customers) • than items (products) • • Many customers have no similar customers • • Most products have similar products • Make recommendation by considering ratings of active user for similar products CMPT 884, SFU, Martin Ester, 1-09

  27. Collaborative Filtering Item-Item Collaborative Filtering ? Aggregator Prediction CMPT 884, SFU, Martin Ester, 1-09

  28. Collaborative Filtering Explanations • Simple visual representations of neighbors ratings • Statement of strong previous performance “MovieLens has predicted correctly 80% of the time for you” CMPT 884, SFU, Martin Ester, 1-09

  29. Collaborative Filtering • Explanations • • Complex representations are not accepted by users, e.g. • more than one dimension • any use of statistical • terminology such as correlation, variance, etc. CMPT 884, SFU, Martin Ester, 1-09

  30. Trust-based Recommendation • Introduction • •Users tend to trust ratings given by their trusted friends • Trust is propagated in the social network • Trust is transitive (to a certain degree) and asymmetric • Use neighborhood of (directly or indirectly) trusted friends to find reliable ratings and make a recommendation • Can make recommendations for cold start users as long as they are somehow connected to the network • More robust to fraud CMPT 884, SFU, Martin Ester, 1-09

  31. Trust-based Recommendation Introduction CMPT 884, SFU, Martin Ester, 1-09

  32. Trust-based Recommendation • Definitions • •ri,j: rating of user i for item j • Trust network: • graph G = (U,T) where U is a set of nodes (users) and T is a set of edges (trust relationships) • Edges can be weighted, but typically they are not • Trust relationships can be explicitly stated by users (e.g., Epinions.com) or be implicitly derived from observed interactions between users (e.g., MSN network) CMPT 884, SFU, Martin Ester, 1-09

  33. Trust-based Recommendation • Definitions • • for users i and j which are connected via T, the indirect trust between i and j is defined via some trust model, based on the direct trust values • raters: all users that have rated target item i • trusted raters: all raters that are trusted by active user u(to a certain degree) CMPT 884, SFU, Martin Ester, 1-09

  34. Trust-based Recommendation • Definitions • • • and f is a function comuting the trust model • recommendation by aggregating the ratings of k trusted raters u CMPT 884, SFU, Martin Ester, 1-09

  35. Trust-based Recommendation • Issues • How to compute the indirect trust? • How many of the trusted raters to consider? • Which ones? • If using too few, the prediction is not based on a significant number or rates. If using too many, these raters may only be weakly trusted. • In a large trust network, need to consider also the efficiency of exploring the trust network. CMPT 884, SFU, Martin Ester, 1-09

  36. Trust-based Recommendation • TidalTrust [Golbeck 2005] • • most accurate information will come from the highest trusted neighbors • in principle, each node should consider only its neighbors with highest trust rating • but different nodes have different max trust among their neighbors, which would lead to different levels of trust in different parts of the network • max: largest trust value such that a path can be found from source to sink with all tij >= max • define indirect trust recursively CMPT 884, SFU, Martin Ester, 1-09

  37. Trust-based Recommendation • MoleTrust [Massa et al 2007] • • trust model similar to TidalTrust • major difference in the set of trusted raters considered • both, TidalTrust and MoleTrust perform a breadth-first search of the trust network • TidalTrust considers all raters at the minimum depth (shortest path distance from the active user) • MoleTrust considers all raters up to a specified maximum depth CMPT 884, SFU, Martin Ester, 1-09

  38. Trust-based Recommendation • Discussion • • TidalTrust is likely to find only very few raters • MoleTrust may consider too many raters • TidalTrust ignores the actual ratings and their distribution • MoleTrust even ignores the actual distribution of the raters maximum depth independent of a and i CMPT 884, SFU, Martin Ester, 1-09

  39. Trust-based Recommendation • Random Walks [Andersen et al 2008] • •perform a random walk in the trust network starting from user a • if current user u has rating for item i, return it • otherwise, choose a trusted neighbor v randomly with probability proportional to tu,v and go to v • terminate as soon as rating found or some specified maxdepth reached • repeat random walks until the average aggregated rating converges • use the aggregated rating as recommendation •  termination depends on distribution of raters and ratings CMPT 884, SFU, Martin Ester, 1-09

  40. Trust-based Recommendation • Experimental Evaluation • •Epinions dataset products rated on a scale of [1. . 5] explicit trust network (binary) epinions.com • Distinguish cold start users and all users • Comparison of various CF and trust-based methods • Item based 0 / .4 / .8: considers only items with similarity at least 0 / .4 / .8 • Random Walk 1 / 6: considers trusted raters up to depth 1 / 6 CMPT 884, SFU, Martin Ester, 1-09

  41. Trust-based Recommendation Experimental Evaluation • all trust-based methods greatly improve the coverage of CF methods • they also have very competitive RMSE CMPT 884, SFU, Martin Ester, 1-09

  42. Trust-based Recommendation Experimental Evaluation • all methods perform much better on all users than on cold start users only • the gain of trust-based methods is not so significant CMPT 884, SFU, Martin Ester, 1-09

  43. Model-based Recommendation Introduction [Cohen 2002] • so far: memory-based methods CF, trust-based recommendation • no training of a model • model-based approaches to CF: • 1) CF as density estimation • 2) CF and content-based recommendation as classification CMPT 884, SFU, Martin Ester, 1-09

  44. Model-based Recommendation CF as Density Estimation [Horvitz et al 1998] • estimate Pr(Rij=k) for each user i, movie j, and rating k • use all available data to build model for this estimator CMPT 884, SFU, Martin Ester, 1-09

  45. Model-based Recommendation CF as Density Estimation • a simple model •  same model for all users CMPT 884, SFU, Martin Ester, 1-09

  46. Model-based Recommendation CF as Density Estimation • a more complex model group users into M “clusters”: c(1), ..., c(M) •  same model for all users within a group estimate by counts CMPT 884, SFU, Martin Ester, 1-09

  47. Model-based Recommendation CF as Density Estimation • group users into clusters using Expectation-Maximization: • - randomly initialize Pr(Rm,j=k) for each m • i.e., initialize the clusters differently somehow • - E-Step: estimate Pr(user i in cluster m) for each i,m • - M-Step: find maximum likelihood (ML) estimator for Rijwithin each cluster m • use ratio of #(users i in cluster m with rating Rij=k) to #(user i in cluster m ), weighted by Pr(i in m) from E-step • - repeat E-step, M-step until convergence CMPT 884, SFU, Martin Ester, 1-09

  48. Model-based Recommendation CF as Classification [Basu et al, 1998] • Classification task: map (user,movie) pair into {likes,dislikes} • Training data: known likes/dislikes, test data: active users • Features: anyproperties of user/movie pair CMPT 884, SFU, Martin Ester, 1-09

  49. Model-based Recommendation CF as Classification • e.g., moviesLikedByUser(Joe) = {Airplane,Matrix,...,Hidalgo} age(Joe)=27, income(Joe)=70k, genre(Matrix)=action, director(Matrix) = . . CMPT 884, SFU, Martin Ester, 1-09

  50. Model-based Recommendation CF as Classification genre={romance}, age=48, sex=male, income=81k, usersWhoLikedMovie={Carol}, moviesLikedByUser={Matrix,Airplane}, ... CMPT 884, SFU, Martin Ester, 1-09

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