Group latent factor model for recommendation with multiple user behaviors

Recently, some recommendation methods try to relieve the data sparsity problem of Collaborative Filtering by exploiting data from users' multiple types of behaviors. However, most of the exist methods mainly consider to model the correlation between different behaviors and ignore the heterogeneity of them, which may make improper information transferred and harm the recommendation results. To address this problem, we propose a novel recommendation model, named Group Latent Factor Model (GLFM), which attempts to learn a factorization of latent factor space into subspaces that are shared across multiple behaviors and subspaces that are specific to each type of behaviors. Thus, the correlation and heterogeneity of multiple behaviors can be modeled by these shared and specific latent factors. Experiments on the real-world dataset demonstrate that our model can integrate users' multiple types of behaviors into recommendation better.