Collaborative Knowledge Base Embedding for Recommender …

Collaborative Knowledge Base Embedding forRecommender SystemsFuzheng Zhang , Nicholas Jing Yuan , Defu Lian , Xing Xie ,Wei-Ying Ma Microsoft Research Big Data Research Center, University of Electronic Science and Technology of different recommendation techniques, Collaborative fil-tering usually suffer from limited performance due to the sparsityof user-item interactions. To address the issues, auxiliary informa-tion is usually used to boost the performance. Due to the rapidcollection of information on the web, the Knowledge base providesheterogeneous information including both structured and unstruc-tured data with different semantics, which can be consumed by var-ious applications. In this paper, we investigate how to leveragethe heterogeneous information in a Knowledge base to improve thequality of Recommender systems. First, by exploiting the knowl-edge base, we design three components to extract items semanticrepresentations from structural content, textual content and visu-al content, respectively.

problem. Section 3 gives an overview of our framework. Sec-tion 4 delves into the usage of embedding components to extract representations from the knowledge base. In Section 5, we discuss how to effectively integrate collaborative filtering with knowledge base embedding into a unified model. The empirical results are dis-

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