Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

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 s...

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Bibliographic Details
Main Authors: Qingyao Ai, Vahid Azizi, Xu Chen, Yongfeng Zhang
Format: Article
Language:English
Published: MDPI AG 2018-09-01
Series:Algorithms
Subjects:
recommender systems
explainable recommendation
knowledge-base embedding
collaborative filtering
Online Access:http://www.mdpi.com/1999-4893/11/9/137

Internet

http://www.mdpi.com/1999-4893/11/9/137

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