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SemRec: a personalized semantic recommendation method based on weighted heterogeneous information networks

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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 a HIN. The comprehensive information integration and rich semantic information of HIN make it promising to generate better recommendations. However, conventional HINs do not consider the attribute values on links, and the widely used meta path in HIN may fail to accurately capture semantic relations among objects, due to the existence of rating scores (usually ranging from 1 to 5) between users and items in recommender system. In this paper, we introduce the weighted HIN and weighted meta path concepts to subtly depict the path semantics through distinguishing different link attribute values. Furthermore, we propose a semantic path based personalized recommendation method SemRec to predict the rating scores of users on items. Through setting meta paths, SemRec not only flexibly integrates heterogeneous information but also obtains prioritized and personalized weights representing user preferences on paths. Experiments on three real datasets illustrate that SemRec achieves better recommendation performance through flexibly integrating information with the help of weighted meta paths. Moreover, extensive experiments validate the benefits of weighted meta paths.

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Notes

  1. http://movie.douban.com/

  2. http://www.yelp.com/dataset_challenge/

  3. http://book.douban.com/

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Acknowledgments

This work is supported in part by the National Natural Science Foundation of China (No. 61772082, 61375058, 61472468), the National Key Research and Development Program of China (2017YFB0803304), and the Co-construction Project of Beijing Municipal Commission of Education.

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Authors and Affiliations

  1. Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China

    Chuan Shi & Zhiping Shi

  2. Beijing Key Lab of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing Shi, China

    Chuan Shi & Yugang Ji

  3. Ant Financial Services Group, Zhejiang, China

    Zhiqiang Zhang

  4. University of Southern California, Los Angeles, CA, USA

    Weipeng Wang

  5. University of Illinois at Chicago, Chicago, IL, USA

    Philip S. Yu

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  1. Chuan Shi
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  2. Zhiqiang Zhang
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  3. Yugang Ji
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  4. Weipeng Wang
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  5. Philip S. Yu
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  6. Zhiping Shi
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Correspondence to Zhiping Shi.

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Shi, C., Zhang, Z., Ji, Y. et al. SemRec: a personalized semantic recommendation method based on weighted heterogeneous information networks. World Wide Web 22, 153–184 (2019). https://doi.org/10.1007/s11280-018-0553-6

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  • DOI: https://doi.org/10.1007/s11280-018-0553-6

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