Collaborative Recommender Systems

1. Collaborative Recommendation via Adaptive Association Rule MiningKDD-2000 Workshop on Web Mining for E-Commerce (WebKDD-2000) Weiyang Lin Sergio A. Alvarez Carolina Ruiz Microsoft WebTVWellesley CollegeWorcester Polytechnic Institute

2. Collaborative Recommender Systems • Recommend articles to target user based on similarity between past behaviors of target user and other users • Some Approaches • Correlation-based Methods [Resnick, et al. 94] • Bayesian Classifier and Bayesian Network Model [Breese, Heckerman, Kadie 98] • Neural Network Paired with SVD/InfoGain [Billsus, Pazzani 98] • Association Rules[Fu et al., IUI-2000]

3. Contributions of our Work • New adaptive-support algorithm for association rule mining • Association rule based collaborative recommendation • Does not rely on pairwise user similarity • Allows article as well as user associations • Efficient user-specific mining process • Produces high quality recommendations

4. Recommendation via Association Rules • Represent Ratings Data as Transactions • User Associations • rule: [user1 likes] and [user2 likes]  [usertarget likes] • view article as market basket containing users who like article • Article Associations • rule: [article1 liked] and [article2 liked] and [article3 liked]  [articletarget liked] • view user as market basket containing articles liked by user • Recommendation strategy • rank mined rules using confidence and support • recommend articles backed by top rules

5. Given: transaction dataset target item (user or article) desired rangefor number of rules specified minimum confidence Find: set S of association rules for target item such that number of rules in S is in given range rules in S satisfy minimumconfidence constraint rules in S have higher support than rules not in S that satisfy above constraints New Adaptive-Support Algorithm for Rule Mining Desired number of rules minConfidence minSupport

6. Experimental Evaluation • Data: EachMovie dataset (DEC) • ratings from 72,916 users for 1,628 movies • scale: (0.0, 0.2, 0.4, 0.6, 0.8, 1.0) • fraction of liked movies: 0.45 for threshold value 0.7 • 1000-2000 users in collaborative group • first set: 1000 users who rated over 100 movies • second set: 2000 randomly chosen users • Performance measures • recall, precision, accuracy • 4-fold cross-validation

7. Recommendation Performance • Visual C++ on a 463 MHz Pentium PC with 128 MB RAM • Dense ratings dataset used for this experiment

8. Comparison • [Billsus & Pazzani, ICML 98] • Collaborative user group: 2000 • Target users: 20 • Training movies: 50 • Our Approach • Matched experimental setup as closely as possible • Accuracy: 0.682

9. Conclusions and Future Work • New approach to recommender systems based on association rule mining • Does not rely on pairwise user similarity • Allows article as well as user associations • Efficient adaptive-support rule mining algorithm • Recommendation quality comparable to state-of-the-art techniques • Future Work • Further experimental evaluation • Content-based recommendation • New application domains