Graph Enhanced Representation Learning for News Recommendation

Abstract

With the explosion of online news, personalized news recommendation becomes increasingly important for online news platforms to help their users find interesting information. Existing news recommendation methods achieve personalization by building accurate news representations from news content and user representations from their direct interactions with news (e.g., click), while ignoring the high-order relatedness between users and news. Here we propose a news recommendation method which can enhance the representation learning of users and news by modeling their relatedness in a graph setting. In our method, users and news are both viewed as nodes in a bipartite graph constructed from historical user click behaviors. For news representations, a transformer architecture is first exploited to build news semantic representations. Then we combine it with the information from neighbor news in the graph via a graph attention network. For user representations, we not only represent users from their historically clicked news, but also attentively incorporate the representations of their neighbor users in the graph. Improved performances on a large-scale real-world dataset validate the effectiveness of our proposed method.

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• KGAT: Knowledge Graph Attention Network for Recommendation
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Cited by in corpus (24)

• FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation
• Information Retrieval: Recent Advances and Beyond
• Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations
• Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation
• USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence
• KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction
• News Recommender System: A review of recent progress, challenges, and opportunities
• MM-Rec: Multimodal News Recommendation
• Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation
• HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation
• OmniSage: Large Scale, Multi-Entity Heterogeneous Graph Representation Learning
• Research Progress of News Recommendation Methods
• Content Filtering Enriched GNN Framework for News Recommendation
• Neural News Recommendation with Event Extraction
• PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity
• Personalized News Recommendation with Knowledge-aware Interactive Matching
• ESANS: Effective and Semantic-Aware Negative Sampling for Large-Scale Retrieval Systems
• CROWN: A Novel Approach to Comprehending Users' Preferences for Accurate Personalized News Recommendation
• Estimating Fund-Raising Performance for Start-up Projects from a Market Graph Perspective
• IP2: Entity-Guided Interest Probing for Personalized News Recommendation
• Cross-Batch Negative Sampling for Training Two-Tower Recommenders
• Neural News Recommendation with Negative Feedback
• DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning
• Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving