Random partition factorization machines for context-aware recommendations

Context plays an important role in helping users to make decisions. There are hierarchical structure between contexts and aggregation characteristics within the context in real scenarios. Exist works mainly focus on exploring the explicit hierarchy between contexts, while ignoring the aggregation characteristics within the context. In this work, we explore both of them so as to improve accuracy of prediction 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 over three different benchmark contextual datasets. Experimental results demonstrate that RPFM outperforms state-of-the-art context-aware recommendation methods.