# Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

## TL;DR

This paper introduces PGPR, a reinforcement learning-based method that performs explicit, interpretable reasoning over knowledge graphs to enhance personalized recommendations with causal explanations.

## Contribution

It proposes a novel RL approach with a soft reward, user-conditional pruning, and a graph search algorithm for explainable recommendation via knowledge graph reasoning.

## Key findings

- Outperforms state-of-the-art methods on benchmark datasets
- Provides interpretable reasoning paths for recommendations
- Demonstrates effective multi-hop reasoning capabilities

## 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.