Wide and Deep Model of Multi-Source Information-Aware Recommender System

Collaborative filtering recommendation suffers from the problems of high data sparsity, poor expansibility, cold start, and the difficulty of modeling user preferences, among which data sparsity is the greatest issue. Although our previous work on matrix completion model, named low rank non-negative matrix factorization and completion algorithm (LR-NMFC) and stochastic sub-gradient based low rank matrix completion algorithm, could effectively alleviate the sparsity problem, they customarily model the linear feature interactions instead of the complex nonlinear structures between users and items when making recommendations. To better depict user preferences and item features, we deepen the linear model LR-NMFC to establish a wide and deep model, which we named Wide and Deep model of Multi-source information-Aware recommender system (WDMMA), based on multi-source information composed of user-item interaction matrix, attributes, and context. The wide part mainly handles the linear interactions between users and items, while the deep part portrays the high-order nonlinear interactions. We pre-train both the wide and the deep part using LR-NMFC in the embedding layer. In the pooling layer, we define a pooling operation, AC-pooling, which is used to model the various interactions among users, items, attributes, and context information. Upon the pooling layer, we stack some hidden layers to capture the high-order nonlinear feature interactions. Experiments on two public datasets show that WDMMA can learn complex nonlinear feature patterns successfully and effectively and is beneficial to improve the recommendation performance. Therefore, it is an effective way to consider both linear user-item interactions and multi-source information-aware nonlinear interactions in a deep learning framework when making recommendations.