Ekar: An Explainable Method for Knowledge Aware Recommendation

22 Jun 2019  ยท  Weiping Song, Zhijian Duan, Ziqing Yang, Hao Zhu, Ming Zhang, Jian Tang ยท

This paper studies recommender systems with knowledge graphs, which can effectively address the problems of data sparsity and cold start. Recently, a variety of methods have been developed for this problem, which generally try to learn effective representations of users and items and then match items to users according to their representations. Though these methods have been shown quite effective, they lack good explanations, which are critical to recommender systems. In this paper, we take a different route and propose generating recommendations by finding meaningful paths from users to items. Specifically, we formulate the problem as a sequential decision process, where the target user is defined as the initial state, and the edges on the graphs are defined as actions. We shape the rewards according to existing state-of-the-art methods and then train a policy function with policy gradient methods. Experimental results on three real-world datasets show that our proposed method not only provides effective recommendations but also offers good explanations.

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Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Recommendation Systems DBbook2014 Ekar* HR@10 0.1874 # 2
nDCG@10 0.1371 # 1
Recommendation Systems Last.FM Ekar* HR@10 0.2483 # 1
nDCG@10 0.1766 # 1
Recommendation Systems MovieLens 1M Ekar* HR@10 0.1994 # 9
nDCG@10 0.3699 # 6

Methods


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