关键词: 新闻推荐, 多维度兴趣建模, 隐式反馈, 注意力机制

Abstract: User preference modeling is a key factor in improving the quality of personalized news recommendations. Existing researches usually model the task as click-through rate estimation tasks, starting from the user’s explicit feedback signal to construct an interest representation. However, due to the lack of explicit feedback signals and the variety of user interests, current news recommendation methods often have the problem of data sparseness and information cocoons. This paper  proposes a news recommendation algorithm based on implicit feedback signals and multi-dimensional interests. By introducing implicit feedback signals such as user exposure unclicked, the data sparsity problem in modeling the recommendation model is alleviated, and a comparative attention mechanism is proposed to model the fusion of user clicked and unclicked news. Besides, this paper also proposes user dynamic interest modeling based on candidate news perception and contrast learning modeling of dynamic and static interests. Multi-dimensional user interests achieve rich and dynamic user preference accurate localization. This study conducts extensive experiments on a real dataset. Three evaluation metrics and a variety of performance tests are used to compare with other baseline methods to verify that the proposed model  outperforms other methods.

Key words: news recommendation, multi-dimensional interest modeling, implicit feedback, attention mechanism