1. Collaborative Recommendation via Adaptive Association Rule Mining KDD-2000 Workshop on Web Mining for E-Commerce (WebKDD-2000) Weiyang Lin Sergio A. Alvarez Carolina Ruiz Microsoft WebTV Wellesley College Worcester Polytechnic Institute

2. 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. 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. 4 Recommendation via Association Rules Represent Ratings Data as Transactions User Associations rule: [user 1 likes] and [user 2 likes]  [user target likes] view article as market basket containing users who like article Article Associations rule: [article 1 liked] and [article 2 liked] and [article 3 liked]  [article target 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. 5 minSupport minConfidence Desired number of rules New Adaptive-Support Algorithm for Rule Mining Given: transaction dataset target item (user or article) desired range for 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 minimum confidence constraint rules in S have higher support than rules not in S that satisfy above constraints

6. 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. 7 Visual C ++ on a 463 MHz Pentium PC with 128 MB RAM Dense ratings dataset used for this experiment Recommendation Performance

8. 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. 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