DKN: Deep Knowledge-Aware Network for News Recommendation

Abstract

Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods are unaware of such external knowledge and cannot fully discover latent knowledge-level connections among news. The recommended results for a user are consequently limited to simple patterns and cannot be extended reasonably. Moreover, news recommendation also faces the challenges of high time-sensitivity of news and dynamic diversity of users' interests. To solve the above problems, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation. DKN is a content-based deep recommendation framework for click-through rate prediction. The key component of DKN is a multi-channel and word-entity-aligned knowledge-aware convolutional neural network (KCNN) that fuses semantic-level and knowledge-level representations of news. KCNN treats words and entities as multiple channels, and explicitly keeps their alignment relationship during convolution. In addition, to address users' diverse interests, we also design an attention module in DKN to dynamically aggregate a user's history with respect to current candidate news. Through extensive experiments on a real online news platform, we demonstrate that DKN achieves substantial gains over state-of-the-art deep recommendation models. We also validate the efficacy of the usage of knowledge in DKN.

The 27th International Conference on World Wide Web (WWW'18)

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Cited by in corpus (32)

• A Survey on Knowledge Graphs: Representation, Acquisition and Applications
• FedKD: Communication Efficient Federated Learning via Knowledge Distillation
• Learning Intents behind Interactions with Knowledge Graph for Recommendation
• Knowledge Graph Contrastive Learning for Recommendation
• A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation
• NPA: Neural News Recommendation with Personalized Attention
• Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System
• Knowledge Graph Self-Supervised Rationalization for Recommendation
• Graph Enhanced Representation Learning for News Recommendation
• Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning
• CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation
• Neural Collaborative Reasoning
• Explainable Reasoning over Knowledge Graphs for Recommendation
• Prompt Learning for News Recommendation
• Time-aware Path Reasoning on Knowledge Graph for Recommendation
• Recommender systems based on graph embedding techniques: A comprehensive review
• Deep Interest Network for Click-Through Rate Prediction
• Knowledge-refined Denoising Network for Robust Recommendation
• Graph Learning Approaches to Recommender Systems: A Review
• Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems
• Automatic Meta-Path Discovery for Effective Graph-Based Recommendation
• KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction
• Knowledge Graph Embeddings and Explainable AI
• Topic-Centric Explanations for News Recommendation
• User Response Prediction in Online Advertising
• Application of Knowledge Graphs to Provide Side Information for Improved Recommendation Accuracy
• An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation
• A Novel User Representation Paradigm for Making Personalized Candidate Retrieval
• Learning to Select Historical News Articles for Interaction based Neural News Recommendation
• Conceptualize and Infer User Needs in E-commerce
• URIR: Recommendation algorithm of user RNN encoder and item encoder based on knowledge graph
• UGRec: Modeling Directed and Undirected Relations for Recommendation