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Title: Reinforced Negative Sampling over Knowledge Graph for Recommendation
Authors: Xiang Wang 
Yaokun Xu
Xiangnan He 
Yixin Cao 
Meng Wang
Tat-Seng Chua 
Keywords: Explainability
Knowledge Graphs
Recommendation System
Reinforcement Learning
Issue Date: 12-Mar-2020
Publisher: Association for Computing Machinery, Inc
Citation: Xiang Wang, Yaokun Xu, Xiangnan He, Yixin Cao, Meng Wang, Tat-Seng Chua (2020-03-12). Reinforced Negative Sampling over Knowledge Graph for Recommendation. WWW 2020 : 285-294. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
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. © 2019 Association for Computing Machinery.
Source Title: WWW 2020
ISBN: 9781450370233
DOI: 10.1145/3366423.3380098
Rights: Attribution 4.0 International
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