Modeling Stage-wise Evolution of User Interests for News Recommendation

• URL: http://arxiv.org/abs/2603.10471v1
• Date: Wed, 11 Mar 2026 06:42:52 GMT
• Title: Modeling Stage-wise Evolution of User Interests for News Recommendation
• Abstract summary: We propose a unified framework that learns user preferences from both global and local temporal perspectives.<n>A global preference modeling component captures long-term collaborative signals from the overall interaction graph.<n>A local preference modeling component partitions historical interactions into stage-wise temporal subgraphs to represent short-term dynamics.
• Score: 11.474027863114854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 both global and local temporal perspectives. A global preference modeling component captures long-term collaborative signals from the overall interaction graph, while a local preference modeling component partitions historical interactions into stage-wise temporal subgraphs to represent short-term dynamics. Within this module, an LSTM branch models the progressive evolution of recent interests, and a self-attention branch captures long-range temporal dependencies. Extensive experiments on two large-scale real-world datasets show that our approach consistently outperforms strong baselines and delivers fresher and more relevant recommendations across diverse user behaviors and temporal settings.

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