Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

Abstract

Recent advances in personalized recommendation have sparked great interest in the exploitation of rich structured information provided by knowledge graphs. Unlike most existing approaches that only focus on leveraging knowledge graphs for more accurate recommendation, we perform explicit reasoning with knowledge for decision making so that the recommendations are generated and supported by an interpretable causal inference procedure. To this end, we propose a method called Policy-Guided Path Reasoning (PGPR), which couples recommendation and interpretability by providing actual paths in a knowledge graph. Our contributions include four aspects. We first highlight the significance of incorporating knowledge graphs into recommendation to formally define and interpret the reasoning process. Second, we propose a reinforcement learning (RL) approach featuring an innovative soft reward strategy, user-conditional action pruning and a multi-hop scoring function. Third, we design a policy-guided graph search algorithm to efficiently and effectively sample reasoning paths for recommendation. Finally, we extensively evaluate our method on several large-scale real-world benchmark datasets, obtaining favorable results compared with state-of-the-art methods.

Accepted in SIGIR 2019

References in corpus (3)

• Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering
• RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
• Translation-based Recommendation

Cited by in corpus (37)

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• KG4Vis: A Knowledge Graph-Based Approach for Visualization Recommendation
• Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning
• CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation
• A Survey on Reinforcement Learning for Recommender Systems
• PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems
• Time-aware Path Reasoning on Knowledge Graph for Recommendation
• ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
• Explainable Fairness in Recommendation
• Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations
• Neural Graph Matching based Collaborative Filtering
• Learning to Ask Appropriate Questions in Conversational Recommendation
• Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation
• Recommender systems based on graph embedding techniques: A comprehensive review
• Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation
• Exploration and Regularization of the Latent Action Space in Recommendation
• Efficient Non-Sampling Knowledge Graph Embedding
• Finding Paths for Explainable MOOC Recommendation: A Learner Perspective
• ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models
• Counterfactual Collaborative Reasoning
• Comparative Explanations of Recommendations
• Aligning Robot and Human Representations
• Recommending on graphs: a comprehensive review from a data perspective
• Dual Policy Learning for Aggregation Optimization in Graph Neural Network-based Recommender Systems
• The Importance of Causality in Decision Making: A Perspective on Recommender Systems
• Future Impact Decomposition in Request-level Recommendations
• Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS)
• A User Interface for Sense-making of the Reasoning Process while Interacting with Robots
• Topic-Aware Knowledge Graph with Large Language Models for Interoperability in Recommender Systems
• Explainable Recommendation with Simulated Human Feedback
• Can Offline Metrics Measure Explanation Goals? A Comparative Survey Analysis of Offline Explanation Metrics in Recommender Systems
• Value Function Decomposition in Markov Recommendation Process