Jaccard Coefficient-Based Bi-clustering and Fusion Recommender System for Solving Data Sparsity

Abstract

Recommender systems have been very common and useful nowadays, which recommend suitable items to users by predicting ratings for items. The most used collaborative filtering recommender system suffers from the sparsity issue due to insufficient data. To cope with this issue, we propose a Jaccard Coefficient-based Bi-clustering and Fusion (JC-BiFu) method for Recommender system. JC-BiFu uses density peak clustering for both users and items, and then makes estimations for missing values in the user-item rating matrix when finding the similar users. Finally, we utilize both users and items to generate the final predictions. Experimental analysis shows that our approach can improve the performance of user recommendations at the extreme levels of sparsity in user-item rating matrix.