Top-N Recommendation Based on Granular Association Rules

Recommender systems are popular in e-commerce as they provide users with items of interest. Existing top-K approaches mine the K strongest granular association rules for each user, and then recommend respective K types of items to her. Unfortunately, in practice, many users need only a list of N items that they would like. In this paper, we propose confidence-based and significance-based approaches exploiting granular association rules to improve the quality of top-N recommendation, especially for new users on new items. We employ the confidence measure and the significance measure respectively to select strong rules. The first approach tends to recommend popular items, while the second tends to recommend special ones to different users. We also consider granule selection, which is a core issue in granular computing. Experimental results on the well-known MovieLens dataset show that: 1) the confidence-based approach is more accurate to recommend items than the significance-based one; 2) the significance-based approach is more special to recommend items than the confidence-based one; 3) the appropriate setting of granules can help obtaining high recommending accuracy and significance.