Decomposed Collaborative Filtering: Modeling Explicit and Implicit Factors For Recommender Systems

Representation learning is the keystone for collaborative filtering. The learned representations should reflect both explicit factors that are revealed by extrinsic attributes such as movies' genres, books' authors, and implicit factors that are implicated in the collaborative signal. Existing methods fail to decompose these two types of factors, making it difficult to infer the deep motivations behind user behaviors, and thus suffer from sub-optimal solutions. In this paper, we propose Decomposed Collaborative Filtering (DCF) to address the above problems. For the explicit representation learning, we devise a user-specific relation aggregator to aggregate the most important attributes. For the implicit part, we propose Decomposed Graph Convolutional Network (DGCN), which decomposes users and items into multiple factor-level representations, then utilizesfactor-level attention and attentive relation aggregation to model implicit factors behind collaborative signals in fine-grained level. Moreover, to reflect more diverse implicit factors, we augment the model with disagreement regularization. We conduct experiments on three public accessible datasets and the results demonstrate the significant improvement of our method over several state-of-the-art baselines. Further studies verify the efficacy and interpretability benefits bought from the fine-grained implicit relation modeling. Our Code will be made available upon acceptance.