Abstract
Collaborative filtering (CF) is one of the most successful and effective recommendation techniques for personalized information access. This method makes recommendations based on past transactions and feedback from users sharing similar interests. However, many commercial recommender systems are widely adopting the CF algorithms; these methods are required to have the ability to deal with sparsity in data and to scale with the increasing number of users and items. The proposed approach addresses the problems of sparsity and scalability by first clustering users based on their rating patterns and then inferring clusters (neighborhoods) by applying two knowledge-based techniques: rule-based reasoning (RBR) and case-based reasoning (CBR) individually. Further to improve accuracy of the system, HRC (hybridization of RBR and CBR) procedure is employed to generate an optimal neighborhood for an active user. The proposed three neighborhood generation procedures are then combined with CF to develop RBR/CF, CBR/CF, and HBR/CF schemes for recommendations. An empirical study reveals that the RBR/CF and CBR/CF perform better than other state-of-the-art CF algorithms, whereas HRC/CF clearly outperforms the rest of the schemes.