Nearest biclusters collaborative filtering framework with fusion

Collaborative filtering is one of the widely used recommendation technique. It provides automated and personalized suggestions to consumers for selecting variety of products by examining their preferences. However, sparsity is one of the major weaknesses of this prosperous approach. This problem inherently occurs in the system due to ever increasing number of users and items. This affects the performance of a recommender system as the accuracy of prediction decreases. Thus, there is a need for a technique that can perform efficiently under sparse environment and this work proposes one such technique. The memory based CF techniques can be user-based or item based. In both cases, the user-item rating matrix can provide only partial information to predict unknown ratings. This is due to the sparsity inherent to rating data. Hence, we propose to fuse the item-based CF and user-based CF. Subsequently, Neighborhood formation is a crucial step in Collaborative filtering technique. Therefore, this paper adopts the biclustering approach for neighborhood formation. This approach, allows a degree of overlap between biclusters (i.e. a user or item is included in more than one clusters). Therefore, a new similarity measure is proposed that obtains a bicluster that has strong partial similarity with an active users' preferences. Experimental results demonstrate that proposed approach generates better accuracy of rate prediction compared to the tradition item-based, user-based and some state of the art approaches.