Personalized News Recommendation with Multi-granularity Candidate-aware User Modeling
This addresses the challenge of capturing diverse user interests for news recommendation, but it is incremental as it builds on existing methods by incorporating multi-granularity features.
The study tackled the problem of matching candidate news with user interests in personalized news recommendation by proposing a multi-granularity candidate-aware user modeling framework, which significantly outperformed baseline models in experiments on a real-world dataset.
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.