Knowledge-Based Recommendation with Hierarchical Collaborative Embedding

  • Zili Zhou
  • Shaowu Liu
  • Guandong XuEmail author
  • Xing Xie
  • Jun Yin
  • Yidong Li
  • Wu Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10938)


Data sparsity is a common issue in recommendation systems, particularly collaborative filtering. In real recommendation scenarios, user preferences are often quantitatively sparse because of the application nature. To address the issue, we proposed a knowledge graph-based semantic information enhancement mechanism to enrich the user preferences. Specifically, the proposed Hierarchical Collaborative Embedding (HCE) model leverages both network structure and text info embedded in knowledge bases to supplement traditional collaborative filtering. The HCE model jointly learns the latent representations from user preferences, linkages between items and knowledge base, as well as the semantic representations from knowledge base. Experiment results on GitHub dataset demonstrated that semantic information from knowledge base has been properly captured, resulting improved recommendation performance.



The authors thank the reviewers for their helpful comments. This work was partially supported by the Major Research Plan of National Science Foundation of China [No. 91630206].


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Zili Zhou
    • 1
    • 2
  • Shaowu Liu
    • 1
  • Guandong Xu
    • 1
    Email author
  • Xing Xie
    • 3
  • Jun Yin
    • 1
  • Yidong Li
    • 4
  • Wu Zhang
    • 2
  1. 1.Advanced Analytics InstituteUniversity of Technology SydneyUltimoAustralia
  2. 2.School of Computer Engineering and ScienceShanghai UniversityShanghaiChina
  3. 3.Microsoft Research AsiaBeijingChina
  4. 4.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina

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