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

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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]

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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

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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

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minSupport

minConfidenceDesired 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

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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

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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

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Visual C++ on a 463 MHz Pentium PC with 128 MB RAM

Dense ratings dataset used for this experiment

Recommendation Performance

User Assoc Article Assoc CombinedAccuracy 0.72 0.611 0.717Precision 0.751 0.754 0.745Recall 0.584 0.226 0.582

Avg Runtime 14.2s 0.06s 5.2s

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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

Correlation InfoGain/ANN SVD/ANNaccuracy 0.644 0.67 0.679

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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