融合多视图新闻与长短时用户偏好，提升推荐效果。
Co-NAML-LSTUR: A Combined Model with Attentive Multi-View Learning and Long- and Short-term User Representations for News Recommendation
- 结合NAML的多视图新闻编码与LSTUR的层次化用户建模。
- 在MIND数据集上比NRMS高1.55% AUC，比NAML高2.45% AUC。
- 适合资源有限场景，代码开源，实用性强。
新闻推荐系统在缓解信息过载方面至关重要，核心挑战在于联合建模新闻的多视图表征，并捕捉用户兴趣的动态双尺度特性——短时与长时偏好。现有方法常依赖单视图特征或对用户行为时间建模不足。本文提出Co-NAML-LSTUR，一个融合NAML的注意力多视图新闻编码与LSTUR的分层用户建模的混合框架，专为有限数据训练设计。模型采用BERT嵌入增强语义表示。在两个常用基准MIND-small和MIND-large上评估，结果表明该模型显著优于强基线：相比NRMS，AUC提升1.55%，MRR提升1.15%；相比NAML，AUC提升2.45%，MRR提升1.71%。这些结果验证了该高效混合模型的有效性，其结合多视图新闻建模与双尺度用户表征，在实际资源受限场景中更具优势，而非追求绝对最佳性能。模型实现已公开于https://github.com/MinhNguyenDS/Co-NAML-LSTUR。
原文摘要 · Abstract (English)
News recommendation systems play a critical role in alleviating information overload by delivering personalized content. A key challenge lies in jointly modeling multi-view representations of news articles and capturing the dynamic, dual-scale nature of user interests-encompassing both short- and long-term preferences. Prior methods often rely on single-view features or insufficiently model user behavior across time. In this work, we introduce Co-NAML-LSTUR, a hybrid news recommendation framework that integrates NAML for attentive multi-view news encoding and LSTUR for hierarchical user modeling, designed for training on limited data resources. Our approach leverages BERT-based embeddings to enhance semantic representation. We evaluate Co-NAML-LSTUR on two widely used benchmarks, MIND-small and MIND-large. Results show that our model significantly outperforms strong baselines, achieving improvements over NRMS by 1.55% in AUC and 1.15% in MRR, and over NAML by 2.45% in AUC and 1.71% in MRR. These findings highlight the effectiveness of our efficiency-focused hybrid model, which combines multi-view news modeling with dual-scale user representations for practical, resource-limited resources rather than a claim to absolute state-of-the-art (SOTA). The implementation of our model is publicly available at https://github.com/MinhNguyenDS/Co-NAML-LSTUR
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