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Recommendation with Heterogeneous Information

  • Chuan ShiEmail author
  • Philip S. Yu
Chapter
Part of the Data Analytics book series (DAANA)

Abstract

Recently, heterogeneous information network (HIN) analysis has attracted a lot of attention, and many data mining tasks have been exploited on HIN. As an important data mining task, recommender system includes a lot of object types (e.g., users, movies, actors, and interest groups in movie recommendation) and the rich relations among object types, which naturally constitute an HIN. The comprehensive information integration and rich semantic information of HIN make it promising to generate better recommendation. In this chapter, we introduce three works on recommendation with HIN. One work recommends items with semantic meta paths, and the other two works extend traditional matrix factorization with rich heterogeneous information.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina
  2. 2.University of Illinois at ChicagoChicagoUSA

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