Personalized News Recommendation with Multi-granularity Candidate-aware User Modeling

2025-04-19

AI Summary

Existing news recommendation methods predominantly rely on static user representations derived solely from click behaviors, failing to capture users’ diverse interests and overlooking multi-granularity associations between candidate news and user preferences. To address these limitations, we propose a multi-granularity candidate-aware user modeling framework. Our approach introduces, for the first time, a candidate-driven three-level attention mechanism—operating at the word, entity, and news levels—to jointly model fine-grained, dynamic, and context-sensitive relevance between candidate news and user interests. The architecture integrates a news text encoder with a knowledge-enhanced entity extractor and incorporates a dedicated multi-granularity feature fusion network. Extensive experiments on real-world datasets demonstrate that our method achieves a 1.82% improvement in AUC over state-of-the-art baselines, validating the effectiveness of multi-granularity candidate awareness in enhancing recommendation accuracy.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Matching candidate news with diverse user interests
• Capturing multi-granularity relatedness in news recommendations
• Improving user modeling with candidate-aware mechanisms

Innovation

• Multi-granularity candidate-aware user modeling framework
• Knowledge-enhanced entity information extractor for news
• Word-level, entity-level, news-level interest representation