Co-NAML-LSTUR: A Combined Model with Attentive Multi-View Learning and Long- and Short-term User Representations for News Recommendation

• URL: http://arxiv.org/abs/2507.20210v1
• Date: Sun, 27 Jul 2025 10:18:22 GMT
• Title: Co-NAML-LSTUR: A Combined Model with Attentive Multi-View Learning and Long- and Short-term User Representations for News Recommendation
• Authors: Minh Hoang Nguyen, Thuat Thien Nguyen, Minh Nhat Ta,
• Abstract summary: News recommendation systems play a vital role in mitigating information overload by delivering personalized news content.<n>A central challenge is to effectively model both multi-view news representations and the dynamic nature of user interests.<n>We propose Co-NAML-LSTUR, a hybrid news recommendation framework that integrates NAML for attentive multi-view news modeling and LSTUR for capturing user preferences.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: News recommendation systems play a vital role in mitigating information overload by delivering personalized news content. A central challenge is to effectively model both multi-view news representations and the dynamic nature of user interests, which often span both short- and long-term preferences. Existing methods typically rely on single-view features of news articles (e.g., titles or categories) or fail to comprehensively capture user preferences across time scales. In this work, we propose Co-NAML-LSTUR, a hybrid news recommendation framework that integrates NAML for attentive multi-view news modeling and LSTUR for capturing both long- and short-term user representations. Our model also incorporates BERT-based word embeddings to enhance semantic feature extraction. We evaluate Co-NAML-LSTUR on two widely used benchmarks, MIND-small and MIND-large. Experimental results show that Co-NAML-LSTUR achieves substantial improvements over most state-of-the-art baselines on MIND-small and MIND-large, respectively. These results demonstrate the effectiveness of combining multi-view news representations with dual-scale user modeling. The implementation of our model is publicly available at https://github.com/MinhNguyenDS/Co-NAML-LSTUR.

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