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

摘要： 用户偏好建模是提升个性化新闻推荐质量的关键因素。现有的研究通常将任务建模为点击率预估任务，从用户的显式反馈信号入手，构造兴趣表征。然而，由于显式反馈信号的匮乏以及用户兴趣的多样化和多变化，目前的新闻推荐方法往往存在数据稀疏和信息茧房的问题。提出了一种基于用户隐式反馈信号和多维度兴趣的新闻推荐算法。通过引入用户曝光未点击这类隐式反馈信号，缓解推荐模型建模时的数据稀疏问题，提出对比注意力机制对用户点击和未点击新闻进行融合建模。此外，还提出了基于候选新闻感知的用户动态兴趣建模和动静态兴趣的对比学习建模，最终得到多维度用户兴趣表征，以实现丰富、准确、动态更新的用户偏好定位。该研究在真实数据集上进行了大量实验，用三种评价指标以及多种性能测试与其他基线方法进行比较，验证了提出的模型优于其他方法。

关键词: 新闻推荐, 多维度兴趣建模, 隐式反馈, 注意力机制