LAUSR

Aspect information mining from user comments has become an important means to improve the performance of recommendation systems (RSs). This is because aspect information in comments is fine-grained and tends to reflect the interactions and preferences of users over items in multiple dimensions. These interactions are different from ratings, which are often explicit and linear. Most current RSs based on aspect information learn the contribution of explicit interactions of aspects in a linear manner, while ignoring the implicit features and non-linear interactions of aspects. Since Chinese grammar is greatly different with English grammar, there are few recommendation models based on Chinese movie comment aspects. In this work, we propose an architecture, named aspect-based neural collaborative filtering (ANCF), to extract comment aspect terms based on rules formulated in Chinese dependency parsing. The proposed ANCF integrates a generalized tensor factorization and a tensorized multi-layer perceptrons into the neural network to capture user-item-aspect interactions in a mixed linear and nonlinear way. The aspect potential interaction vector and the actual interaction vector are layered and fused into tensor processing, which can reduce the tensor sparsity and solve the cold start problem of collaborative filtering to a certain extent. Performance results show that the proposed model outperforms some of the traditional ones in terms of recommendation accuracy and effectiveness.