Neural TV program recommendation with heterogeneous attention

TV program recommendation is very important to avoid confusing users with large amounts of information. The existing methods are mainly based on collaborative filtering to utilize the interaction between users and items. However, they ignore auxiliary information that contains rich semantic information. In this paper, we propose a neural TV program recommendation with heterogeneous attention, which incorporates the multi-level features of auxiliary information and neural networks based on attention mechanism to obtain accurate program and user representations. In the program encoder module, we learn the different semantic information of labels and titles contained in each program through a neural network with heterogeneous attention to identify multi-hierarchical program information. In the user encoder module, we incorporate auxiliary information and interactions between users and programs. In addition, we utilize a personalized attention mechanism to learn the importance of different programs for each user to reveal user preferences. Specifically, we collect and process user viewing data in the capital of China to provide a real scenario for personalized recommendation. Experiments on real dataset show that our method can effectively improve the effectiveness of TV program recommendations than other existing models.