Modeling Stage-wise Evolution of User Interests for News Recommendation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911505671258112 |
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| author | Cheng, Zhiyong Jin, Yike Zhang, Zhijie Chen, Huilin Duan, Zhangling Wang, Meng |
| author_facet | Cheng, Zhiyong Jin, Yike Zhang, Zhijie Chen, Huilin Duan, Zhangling Wang, Meng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_10471 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Modeling Stage-wise Evolution of User Interests for News Recommendation Cheng, Zhiyong Jin, Yike Zhang, Zhijie Chen, Huilin Duan, Zhangling Wang, Meng Information Retrieval Artificial Intelligence H.3.3; H.5.1 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. |
| title | Modeling Stage-wise Evolution of User Interests for News Recommendation |
| topic | Information Retrieval Artificial Intelligence H.3.3; H.5.1 |
| url | https://arxiv.org/abs/2603.10471 |