Reinforced Negative Sampling over Knowledge Graph for Recommendation

Abstract

Properly handling missing data is a fundamental challenge in recommendation. Most present works perform negative sampling from unobserved data to supply the training of recommender models with negative signals. Nevertheless, existing negative sampling strategies, either static or adaptive ones, are insufficient to yield high-quality negative samples --- both informative to model training and reflective of user real needs. In this work, we hypothesize that item knowledge graph (KG), which provides rich relations among items and KG entities, could be useful to infer informative and factual negative samples. Towards this end, we develop a new negative sampling model, Knowledge Graph Policy Network (KGPolicy), which works as a reinforcement learning agent to explore high-quality negatives. Specifically, by conducting our designed exploration operations, it navigates from the target positive interaction, adaptively receives knowledge-aware negative signals, and ultimately yields a potential negative item to train the recommender. We tested on a matrix factorization (MF) model equipped with KGPolicy, and it achieves significant improvements over both state-of-the-art sampling methods like DNS and IRGAN, and KG-enhanced recommender models like KGAT. Further analyses from different angles provide insights of knowledge-aware sampling. We release the codes and datasets at https://github.com/xiangwang1223/kgpolicy.

WWW 2020 oral presentation

References in corpus (11)

• Neural Graph Collaborative Filtering
• KGAT: Knowledge Graph Attention Network for Recommendation
• RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
• Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
• IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
• Learning over Knowledge-Base Embeddings for Recommendation
• Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation
• Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
• Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews
• Item Silk Road: Recommending Items from Information Domains to Social Users
• Adversarial Sampling and Training for Semi-Supervised Information Retrieval

Cited by in corpus (18)

• Disentangled Graph Collaborative Filtering
• Learning Intents behind Interactions with Knowledge Graph for Recommendation
• A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation
• MetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation
• A Survey on Reinforcement Learning for Recommender Systems
• Learning Robust Recommenders through Cross-Model Agreement
• FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings
• Recommender systems based on graph embedding techniques: A comprehensive review
• On the Theories Behind Hard Negative Sampling for Recommendation
• On the User Behavior Leakage from Recommender System Exposure
• Debiased Explainable Pairwise Ranking from Implicit Feedback
• Rethinking Collaborative Metric Learning: Toward an Efficient Alternative without Negative Sampling
• Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering
• Recommending on graphs: a comprehensive review from a data perspective
• Dual Policy Learning for Aggregation Optimization in Graph Neural Network-based Recommender Systems
• Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers
• Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation
• Generating Negative Samples for Multi-Modal Recommendation