  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.
