Side Information Aggregated Convolutional Neural Network in News Recommendation

Abstract:

Existing news recommendation models generally consist of the text feature extraction network and the recommendation network. News-related side information， such as category， is not fused into the text feature extraction network. Without fusing it， there are differences between the optimization targets of the text feature extraction network and the recommendation network. In this paper， a general SIACNN （side information aggregated CNN） layer is proposed. The SIACNN layer fuses the side information into the text feature through the attention mechanism， which bridges the gap between text feature extraction and recommendation tasks and improves the effectiveness of the recommendation. CNNs are replaced in many state-of-the-art models which used CNNs to extract the text feature with the SIACNN and several experiments are conducted in a large real-world news recommendation dataset MIND（MIcrosoft News Dataset） collected from MSN（MicroSoft News）. The recommendation effectiveness and generality of SIACNN are verified by several experiments.