MPF-based Hybrid Recommendations for Mobile Commerce

摘要

Typical collaborative filtering may suffer from the so-called sparsity problem, which means there may not be sufficient similar users because the user-item rating matrix is sparse. In this paper, we propose a mobile phone feature-based (MPF) hybrid method to resolve the sparsity issue of the typical CF method in mobile environments. We use the features of mobile phones to identify users' characteristics and then cluster users into groups with similar interests. Our experiment results show that the proposed hybrid method performs better than other recommendation methods.