MIAR: Interest-Activated News Recommendation by Fusing Multichannel Information

Abstract

The different news clicked by users reflects the diverse interests of users. Most of the existing news recommendation methods do not consider the interaction with candidate news in the process of modeling user interest representation. This method makes it challenging to precisely match candidate news to specific user interests. We propose a user interest activation recommendation method that fuses multichannel information—MIAR. It utilizes the word embedding of the user’s historical clicked news and the news title embedding generated by aggregation and interacts with the candidate news, respectively, to better match the candidate news with the user’s interests. Our proposed method contains two frameworks (interactive framework and distributed framework). In the interactive framework, we propose a user multichannel interest modeling framework MIF from the word embedding level of news headlines to capture more semantic cues related to user interests. In the distributed framework, we design a candidate-aware interest activation module TAR from the news embedding representation level obtained by attention aggregation. It uses different candidate news vectors to adjust the user representations learned from the user’s historical reading records. This allows the model to build candidate-guided user representations to accurately match candidate news to parts of user interests that are relevant to the candidate news. Finally, we effectively assign the weights of the two frame scores so that the models can fuse better. Extensive experiments on the MIND news recommendation dataset demonstrate the effectiveness of our method.