Exploiting Incidence Relation Between Subgroups for Improving Clustering-Based Recommendation Model

Matrix factorization (MF) has been attracted much attention in recommender systems due to its extensibility and high accuracy. Recently, some clustering-based MF recommendation methods have been proposed in succession to capture the associations between related users (items). However, these methods only use the subgroup data to build local models, so they will suffer the over-fitting problem caused by insufficient data in the process of training. In this paper, we analyse the incidence relation between subgroups of users (items) and then propose two single improved clustering-based MF models. Through exploiting these relations between subgroups, the local model in each subgroup can obtain global information from other subgroups, which can mitigate the over-fitting problem. Above all, we generate an ensemble model by combining the two single models for capturing associations between users and associations between items at the same time. Experimental results on different scales of MovieLens datasets demonstrate that our method outperforms state-of-the-art clustering-based recommendation methods, especially on sparse datasets.