Heterogeneous Recommendation via Deep Low-Rank Sparse Collective Factorization.

A real-world recommender usually adopts heterogeneous types of user feedbacks, for example, numerical ratings such as 5-star grades and binary ratings such as likes and dislikes. In this work, we focus on transferring knowledge from binary ratings to numerical ratings, facing a more serious data sparsity problem. Conventional Collective Factorization methods usually assume that there are shared user and item latent factors across multiple related domains, but may ignore the shared common knowledge of rating patterns. Furthermore, existing works may also fail to consider the hierarchical structures in the heterogeneous recommendation scenario (i.e., genre, sub-genre, detailed-category). To address these challenges, in this paper, we propose a novel Deep Low-rank Sparse Collective Factorization (DLSCF) framework for heterogeneous recommendation. Specifically, we adopt low-rank sparse decomposition to capture the common rating patterns in related domains while splitting the domain-specific patterns. We also factorize the model in multiple layers to capture the affiliation relation between latent categories and sub-categories. We propose both batch and Stochastic Gradient Descent (SGD) based optimization algorithms for solving DLSCF. Experimental results on MoviePilot, Netfilx, Flixter, MovieLens10M and MovieLens20M datasets demonstrate the effectiveness of the proposed algorithms, by comparing them with several state-of-the-art batch and SGD based approaches.