通过多粒度匹配提升新闻推荐精准度
Personalized News Recommendation with Multi-granularity Candidate-aware User Modeling
- 构建多粒度候选新闻感知的用户建模框架
- 在真实数据集上显著优于基线模型
- 适合关注个性化推荐与兴趣建模的研究者
将候选新闻与用户兴趣匹配是个性化新闻推荐的关键。现有方法通常基于点击新闻生成单一用户画像，难以全面捕捉用户兴趣的多样性。尽管部分方法引入候选新闻或主题信息，但仍因忽略候选新闻与用户兴趣之间的多粒度关联而效果有限。为此，本文提出一种多粒度候选新闻感知的用户建模框架，融合不同粒度下的用户兴趣特征。该框架包含候选新闻编码和用户建模两部分：通过文本信息提取器和知识增强实体提取器捕获候选新闻特征；利用词级、实体级、新闻级的候选感知机制，实现对用户兴趣的全面表征。在真实世界数据集上的大量实验表明，所提模型显著优于基线模型。
原文摘要 · Abstract (English)
Matching candidate news with user interests is crucial for personalized news recommendations. Most existing methods can represent a user's reading interests through a single profile based on clicked news, which may not fully capture the diversity of user interests. Although some approaches incorporate candidate news or topic information, they remain insufficient because they neglect the multi-granularity relatedness between candidate news and user interests. To address this, this study proposed a multi-granularity candidate-aware user modeling framework that integrated user interest features across various levels of granularity. It consisted of two main components: candidate news encoding and user modeling. A news textual information extractor and a knowledge-enhanced entity information extractor can capture candidate news features, and word-level, entity-level, and news-level candidate-aware mechanisms can provide a comprehensive representation of user interests. Extensive experiments on a real-world dataset demonstrated that the proposed model could significantly outperform baseline models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。