Content Filtering Enriched GNN Framework for News Recommendation
It addresses data sparsity and bias issues in news recommendation for users, but appears incremental as it builds on existing GNN-based approaches.
The paper tackles the problem of data sparsity and popularity bias in news recommendation by proposing ConFRec, a content filtering enriched GNN framework that captures both collaborative and content filtering information, showing effectiveness over state-of-the-art baselines in experiments on real-world datasets.
Learning accurate users and news representations is critical for news recommendation. Despite great progress, existing methods seem to have a strong bias towards content representation or just capture collaborative filtering relationship. However, these approaches may suffer from the data sparsity problem (user-news interactive behavior sparsity problem) or maybe affected more by news (or user) with high popularity. In this paper, to address such limitations, we propose content filtering enriched GNN framework for news recommendation, ConFRec in short. It is compatible with existing GNN-based approaches for news recommendation and can capture both collaborative and content filtering information simultaneously. Comprehensive experiments are conducted to demonstrate the effectiveness of ConFRec over the state-of-the-art baseline models for news recommendation on real-world datasets for news recommendation.