Random Partition Factorization Machines for Context-Aware Recommendations

Abstract

Fusing hierarchical information implied into contexts can significantly improve predictive accuracy in recommender systems. We propose a Random Partition Factorization Machines (RPFM) by adopting random decision trees to split the contexts hierarchically to better capture the local complex interplay. The intuition here is that local homogeneous contexts tend to generate similar ratings. During prediction, our method goes through from the root to the leaves and borrows from predictions at higher level when there is sparseness at lower level. Other than estimation accuracy of ratings, RPFM also reduces the over-fitting by building an ensemble model on multiple decision trees. We test RPFM on three different benchmark contextual datasets. Experimental results demonstrate that RPFM outperforms state-of-the-art context-aware recommendation methods.

This work is supported by National Basic Research Program of China (973) (No. 2014CB340403, No. 2012CB316205), National High Technology Research and Development Program of China (863) (No. 2014AA015204) and NSFC under the grant No. 61272137, 61033010, 61202114 and NSSFC (No. 12&ZD220), and the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (15XNH113, 15XNLQ06). It was partially done when the authors worked in SA Center for Big Data Research in RUC. This Center is funded by a Chinese National 111 Project Attracting.