Recommender Systems: User Experience and System Issues. About me. Scope of Recommenders. A Quick Introduction. Wide Range of Algorithms

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1 Recommender Systems: User Experience and System ssues Joseph A. Konstan University of Minnesota About me Professor of Computer Science & Engineering, Univ. of Minnesota Ph.D. (1993) from U.C. Berkeley GU toolkit architecture Teaching nterests: HC, GU Tools Research nterests: General HC, and... Collaborative nformation Filtering Multimedia Authoring and Systems Visualization and nformation Management Medical/Health Applications and their Delivery UNVERSTY OF MNNESOTA A Quick ntroduction What are recommender systems? Tools to help identify worthwhile stuff Filtering interfaces filters, clipping services Recommendation interfaces Suggestion lists, top-n, offers and promotions Prediction interfaces Evaluate candidates, predicted ratings Scope of Recommenders Purely Editorial Recommenders Content Filtering Recommenders Collaborative Filtering Recommenders Hybrid Recommenders Wide Range of Algorithms Simple Keyword Vector Matches Pure Nearest-Neighbor Collaborative Filtering Machine Learning on Content or Ratings Classic Collaborative Filtering MovieLens* K-nearest neighbor algorithm Model-free, memory-based implementation ntuitive application, supports typical interfaces *Note newest releases use updated architecture/algorithm 1

2 CF Classic Submit Ratings ratings C.F. Engine C.F. Engine Ratings Correlations Ratings Correlations Store Ratings Compute Correlations C.F. Engine C.F. Engine ratings pairwise corr. Ratings Correlations Ratings Correlations Request Recommendations dentify Neighbors C.F. Engine request C.F. Engine find good Ratings Correlations Ratings Correlations Neighborhood 2

3 Select tems; Predict Ratings Understanding the Computation Ratings C.F. Engine Correlations predictions recommendations Neighborhood Hoop Dreams Star Wars Pretty Woman Titanic Blimp Rocky XV Joe D A B D?? John A F D F Susan A A A A A A Pat D A C Jean A C A C A Ben F A F Nathan D A A Understanding the Computation Understanding the Computation Hoop Dreams Star Wars Pretty Woman Titanic Blimp Rocky XV Joe D A B D?? John A F D F Susan A A A A A A Pat D A C Jean A C A C A Ben F A F Nathan D A A Hoop Dreams Star Wars Pretty Woman Titanic Blimp Rocky XV Joe D A B D?? John A F D F Susan A A A A A A Pat D A C Jean A C A C A Ben F A F Nathan D A A Understanding the Computation Understanding the Computation Hoop Dreams Star Wars Pretty Woman Titanic Blimp Rocky XV Joe D A B D?? John A F D F Susan A A A A A A Pat D A C Jean A C A C A Ben F A F Nathan D A A Hoop Dreams Star Wars Pretty Woman Titanic Blimp Rocky XV Joe D A B D?? John A F D F Susan A A A A A A Pat D A C Jean A C A C A Ben F A F Nathan D A A 3

4 Understanding the Computation Understanding the Computation Hoop Dreams Star Wars Pretty Woman Titanic Blimp Rocky XV Joe D A B D?? John A F D F Susan A A A A A A Pat D A C Jean A C A C A Ben F A F Nathan D A A Hoop Dreams Star Wars Pretty Woman Titanic Blimp Rocky XV Joe D A B D?? John A F D F Susan A A A A A A Pat D A C Jean A C A C A Ben F A F Nathan D A A MovieLens ML-home Freely accessible at: ML-comedy ML-clist 4

5 ML-rate ML-search ML-slist ML-buddies Talk Roadmap Collaborative Filtering Algorithms ntroduction Algorithms Research Overview nfluencing Users Recommending Research Papers Rethinking Recommendation Non-Personalized Summary Statistics K-Nearest Neighbor user-user item-item Dimensionality Reduction LS PLS Factor Analysis Content + Collaborative Filtering Burke s Survey of Hybrids Graph Techniques Horting Clustering Classifier Learning Naïve Bayes Bayesian Belief Networks Rule-induction 5

6 Zagat Guide Detail Collaborative Filtering Algorithms Non-Personalized Summary Statistics K-Nearest Neighbor user-user item-item Dimensionality Reduction LS PLS Factor Analysis Content + Collaborative Filtering Burke s Survey of Hybrids Graph Techniques Horting Clustering Classifier Learning Naïve Bayes Bayesian Belief Networks Rule-induction tem-tem Collaborative Filtering tem-tem Collaborative Filtering B. Sarwar et al. tem-based collaborative filtering recommendation algorithms. Proc. WWW tem-tem Collaborative Filtering s i,j =? tem Similarities i j n-1 n R R - R u m-1 m R R R R R - Used for similarity computation 6

7 1 2 u tem-tem Matrix Formulation R R R - R Target item i-1 i m-1 m u R R R - R s i,1 s i,3 s i,i-1 prediction s i,m tem-tem Discussion Good quality, in sparse situations Promising for incremental model building Small quality degradation Big performance gain m weighted sum regression-based 2nd 1st 4th 3rd 5th 5 closest neighbors Raw scores for prediction generation Approximation based on linear regression Collaborative Filtering Algorithms Dimensionality Reduction Non-Personalized Summary Statistics K-Nearest Neighbor user-user item-item Dimensionality Reduction LS PLS Factor Analysis Content + Collaborative Filtering Burke s Survey of Hybrids Graph Techniques Horting Clustering Classifier Learning Naïve Bayes Bayesian Belief Networks Rule-induction Latent Semantic ndexing Used by the R community Worked well with the vector space model Used Singular Value Decomposition (SVD) Main dea Term-document matching in feature space Captures latent association Reduced space is less-noisy B. Sarwar et al. ncremental SVD-Based Algorithms for Highly Scaleable Recommender Systems. Proc CCT SVD: Mathematical Background SVD for Collaborative Filtering R R k m X n = U U k m X rk S S k r k X rk V V k k r X n The reconstructed matrix R k = U k.s k.v k is the closest rank-k matrix to the original matrix R. m x n 1. Low dimensional representation O(m+n) storage requirement m x k k x n. 2. Direct Prediction 7

8 Singular Value Decomposition Collaborative Filtering Algorithms Reduce dimensionality of problem Results in small, fast model Richer Neighbor Network ncremental Update Folding in Model Update Non-Personalized Summary Statistics K-Nearest Neighbor user-user item-item Dimensionality Reduction LS PLS Factor Analysis Content + Collaborative Filtering Burke s Survey of Hybrids Graph Techniques Horting Clustering Classifier Learning Naïve Bayes Bayesian Belief Networks Rule-induction Talk Roadmap ntroduction Algorithms Research Overview nfluencing Users Recommending Research Papers Rethinking Recommendation Current and Recent Research User Experience mpact of Ratings on Users New User Orientation Confidence Displays nterface Design Human-Recommender nteraction Algorithmic and Systems ssues Beyond Accuracy: Metrics and Algorithms Buddies and Multi-User Recommendations nfluence and Shilling Eliciting Participation in On-Line Communities Reinventing Conversation User-Maintained Communities Extending Recommendation to New Domains Recommending Research Papers Talk Roadmap ntroduction Algorithms Research Overview nfluencing Users Recommending Research Papers Rethinking Recommendation D. Cosley et al. s Seeing Believing? How Recommender Systems nfluence Users' Opinions. Proc. CH Does Seeing Predictions Affect User Ratings? RERATE: Ask 212 users to rate 40 movies 10 with no shown prediction 30 with shown predictions (random order): 10 accurate, 10 up a star, 10 down a star Compare ratings to accurate predictions Prediction is user s original rating Hypothesis: users rate in the direction of the shown prediction 8

9 The Study Seeing Matters Ratings % 80% 60% 40% 20% 0% Not shown Prediction shown? Below At Above Show n Accuracy Matters Domino Effects? Ratings % 80% 60% 40% 20% 0% Down Accurate Up The power to manipulate? Below At Above Prediction manipulation Rated, Unrated, Doesn t Matter Recap of RERATE effects: Showing prediction changed 8% of ratings Altering shown prediction changed 12% Similar experiment, UNRATED movies 137 experimental users, 1599 ratings Showing prediction changed 8% of ratings Altering shown prediction changed 14% But Users Notice! Users are often insensitive UNRATED part 2: satisfaction survey Control group: only accurate predictions Experimental predictions accurate, useful? ML predictions overall accurate, useful? Manipulated preds less well liked Surprise: 24 bad = MovieLens worse! 9

10 Talk Roadmap ntroduction Algorithms Research Overview nfluencing Users Recommending Research Papers Rethinking Recommendation Recommending Research Papers Using Citation Webs For a full paper, we can recommend citations A paper rates the papers it cites Every paper has ratings in the system Other citation web mappings are possible, but many are have problems S. McNee et al. On the Recommending of Citations for Research Papers, in Proc. CSCW 2002 and R. Torres et al. Enhancing Digital Libraries with TechLens+, in Proc. JCDL

11 Pure Experiment Results -- Online ndividual Recommendations Percentage Novel Relevant Co-citation tem-item User-user Graph Search Google Bayesian Pure Experiment Results -- Online Worst algorithm returned good results over 25% of the time 76% of users got at least one good recommendation Users happy with one good recommendation in list of five What s Next? Short-Term Efforts Task-specific recommendation Understanding personal bibliographies Privacy issues Longer-Term Efforts Toolkits to support librarians and other power users Exploring the shape of disciplines Rights issues Task-Specific Recommendations Many different user needs awareness in area of expertise find specific work in area of expertise explore peripheral or new area find people with relevant expertise reviewers, program committees, collaborators reading list for students, newcomers individuals or groups Different algorithms fulfill different needs Talk Roadmap ntroduction Algorithms Research Overview nfluencing Users Recommending Research Papers Rethinking Recommendation 11

12 Evaluating Recommendations Prediction Accuracy MAE, MSE, Decision-Support Accuracy Reversals, ROC Recommendation Quality Top-n measures tem-set Coverage From tems to Lists Do users really experience recommendations in isolation? J. Herlocker et al. Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on nformation Systems 22(1), Jan C. Ziegler et al. mproving Recommendation Lists through Topic Diversification., in Proc. WWW Amazon.com example Sauron Defeated By J.R.R. Tolkien, Amazon.com example Chris Tolkien, Editor The War of the Ring By J.R.R. Tolkien, Chris Tolkien, Editor Treason of sengard By J.R.R. Tolkien, Chris Tolkien, Editor Shaping of Middle Earth By J.R.R. Tolkien, Chris Tolkien, Editor Making Good Lists ndividually good recommendations do not equal a good recommendation list Other factors are important Diversity Affirmation Appropriateness Called the Portfolio Effect [ Ali and van Stam, 2004 ] Topic Diversification Re-order results in a rec list Add item with least similarity to all items already on list Weight with a diversification factor Ran experiments to test effects 12

13 Experimental Design Online Results Books from BookCrossing.com Algorithms tem-based CF User-based CF Experiments On-line user surveys 2125 users each saw one list of 10 recommendations Diversity is mportant User satisfaction more complicated than only accuracy List makeup is important to users 30% change enough to alter user opinion Change not equal across algorithms Human-Recommender nteraction Three premises: Users perceive recommendation quality in context; users evaluate lists Users develop opinions of recommenders based on interactions over time Users have an information need and come to a recommender as a part of their information seeking behavior S. McNee et al. Making Recommendations Better: An Analytic Model for Human-Recommender nteraction in Ext. Abs. CH 2006 HR Pillars and Aspects HR Process Model Makes HR Constructive Links Users/Tasks to Algorithms Need New Metrics 13

14 New Metrics Metric Experimental Design Benchmark a variety of algorithms Need several metrics inspired by different HR Aspects Examples: Ratability Boldness Adaptability ACM DL Dataset Thanks to ACM for cooperation! 24,000 papers Have citations, titles, authors, & abstracts High quality Algorithms User-based CF tem-based CF Naïve Bayes Classifier TF/DF Content-based Co-citation Local Graph Search Hybrid variants Ratability Probability a user will rate a given item Obviousness Based on current user model ndependent of liking the item Many possible implementations Naïve Bayes Classifier Mean Ratability Ratability Results Ratability -120 Local Graph Bayes tem, 50 nbrs TFDF User, 50 nbrs top-10 top-20 top-30 top-40 Boldness Boldness Results Measure of Extreme Predictions Only defined on explicit rating scale Choose extreme values Count appearance of extremes and normalize For example, MovieLens 0.5 to 5.0 star scale, half-star increments Choose 0.5 and 5.0 as extreme Ratio to Expected Boldness tem, 50 nbrs User, 30 nbrs top10 top20 top30 top40 topall 14

15 Adaptability Measure of how algorithm changes in response to changes in user model How do users grow in the system? Perturb a user model with a model from another random user 50% each See quality of new recommendation lists mean % adaptable Adaptability Results Adaptability, Even-Split Bayes tem, 50 nbrs Local Graph TFDF User, 50 nbrs top-10 top-20 top-30 top-40 Adaptability Results Adaptability, Even-Split Adaptability Results Adaptability, Even-Split mean % adaptable mean % adaptable item.10 item.30 item.50 item.100 item.200 item.300 user.10 user.30 user.50 user.100 user.200 user.300 item.10 item.30 item.50 item.100 item.200 item.300 user.10 user.30 user.50 user.100 user.200 user.300 top-10 top-20 top-30 top-40 top-10 top-20 top-30 top-40 Conclusions Acknowledgements From humble origins Substantial algorithmic research HC and online community research mportant applications Commercial deployment This work is being supported by grants from the National Science Foundation, and by grants from Net Perceptions, nc. Many people have contributed ideas, time, and energy to this project. 15

16 Recommender Systems: User Experience and System ssues Joseph A. Konstan University of Minnesota UNVERSTY OF MNNESOTA 16

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