Modeling Stage-wise Evolution of User Interests for News Recommendation
Personalized news recommendation is highly time-sensitive, as user interests are often driven by emerging events, trending topics, and shifting real-world contexts. These dynamics make it essential to model not only users' long-term preferences, which reflect stable reading habits and high-order collaborative patterns, but also their short-term, context-dependent interests that change rapidly over time. However, most existing approaches rely on a single static interaction graph, which struggles to capture both long-term preference patterns and short-term interest changes as user behavior evolves. To address this challenge, we propose a unified framework that learns user preferences from b...
| Authors | Zhiyong Cheng, Yike Jin, Zhijie Zhang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 11 March 2026 |
| Last updated at source | 11 March 2026 |
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