This paper proposes the User Viewing Flow Modeling (SINGLE) method for the article recommendation task, which models the user constant preference and instant interest from user-clicked articles. Specifically, we first employ a user constant viewing flow modeling method to summarize the user's general interest to recommend articles. In this case, we utilize Large Language Models (LLMs) to capture constant user preferences from previously clicked articles, such as skills and positions. Then we design the user instant viewing flow modeling method to build interactions between user-clicked article history and candidate articles. It attentively reads the representations of user-clicked articles and aims to learn the user's different interest views to match the candidate article. Our experimental results on the Alibaba Technology Association (ATA) website show the advantage of SINGLE, achieving a 2.4% improvement over previous baseline models in the online A/B test. Our further analyses illustrate that SINGLE has the ability to build a more tailored recommendation system by mimicking different article viewing behaviors of users and recommending more appropriate and diverse articles to match user interests.

翻译：本文提出用户阅读流建模（SINGLE）方法用于文章推荐任务，该方法从用户点击的文章中建模用户的稳定偏好与即时兴趣。具体而言，我们首先采用用户稳定阅读流建模方法，总结用户的总体兴趣以推荐文章。在此过程中，我们利用大语言模型（LLMs）从用户此前点击的文章中捕获稳定偏好（如技能与职位）。随后，我们设计用户即时阅读流建模方法，构建用户点击文章历史与候选文章之间的交互关系。该方法通过注意力机制读取用户点击文章的表征，旨在学习用户针对候选文章的不同兴趣视角。我们在阿里巴巴技术协会（AlTA）网站上的实验结果表明，SINGLE方法具备优势，在线A/B测试中相较此前基线模型实现2.4%的提升。进一步分析显示，SINGLE通过模拟用户不同的文章阅读行为，能够构建更具个性化的推荐系统，推荐更恰当且多样化的文章以匹配用户兴趣。