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Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA 01002, USA
Department of Computer Science, Rutgers University, 110 Frelinghuysen Rd, Piscataway, NJ 08854, USA
School of Software, Tsinghua University, Beijing 100084, China
Author to whom correspondence should be addressed.
Algorithms 2018, 11(9), 137;
Received: 8 August 2018 / Revised: 1 September 2018 / Accepted: 10 September 2018 / Published: 13 September 2018
(This article belongs to the Special Issue Collaborative Filtering and Recommender Systems)
Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms—especially the collaborative filtering (CF)- based approaches with shallow or deep models—usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely ignored recently due to the availability of vast amounts of data and the learning power of many complex models. However, structured knowledge bases exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users’ historical behaviors and the knowledge is helpful for providing informed explanations regarding the recommended items. A great challenge for using knowledge bases for recommendation is how to integrate large-scale structured and unstructured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements in knowledge-base embedding (KBE) sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge for explanation. In this work, we propose to explain knowledge-base embeddings for explainable recommendation. Specifically, we propose a knowledge-base representation learning framework to embed heterogeneous entities for recommendation, and based on the embedded knowledge base, a soft matching algorithm is proposed to generate personalized explanations for the recommended items. Experimental results on real-world e-commerce datasets verified the superior recommendation performance and the explainability power of our approach compared with state-of-the-art baselines. View Full-Text
Keywords: recommender systems; explainable recommendation; knowledge-base embedding; collaborative filtering recommender systems; explainable recommendation; knowledge-base embedding; collaborative filtering
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MDPI and ACS Style

Ai, Q.; Azizi, V.; Chen, X.; Zhang, Y. Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation. Algorithms 2018, 11, 137.

AMA Style

Ai Q, Azizi V, Chen X, Zhang Y. Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation. Algorithms. 2018; 11(9):137.

Chicago/Turabian Style

Ai, Qingyao, Vahid Azizi, Xu Chen, and Yongfeng Zhang. 2018. "Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation" Algorithms 11, no. 9: 137.

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