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 employ a user
constant viewing flow modeling method to summarize the user's general interest
to recommend articles. 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, which achieves 2.4% improvements 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.

该研究提出了一种名为 “User Viewing Flow Modeling (SINGLE)” 的文章推荐方法，该方法通过建模用户不变的偏好和来自用户点击文章的即时兴趣来进行推荐。该方法使用大型语言模型（Large Language Models）捕捉用户先前点击文章中的恒定偏好，然后设计了用户即时视图流建模方法，建立用户点击历史与候选文章之间的交互。实验结果表明，该方法相对于传统基准模型在在线 A/B 测试中实现了 2.4% 的改进，并具备建立更加个性化推荐系统的能力，以模拟用户的不同文章浏览行为，推荐更适合和多样化的文章来满足用户的兴趣。