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

  • Chuan Shi
  • Zhiqiang Zhang
  • Yugang Ji
  • Weipeng Wang
  • Philip S. Yu
  • Zhiping Shi


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.


Heterogeneous information network Recommendation Similarity Meta path 



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.


  1. 1.
    Burke, R., Vahedian, F., Mobasher, B.: Hybrid recommendation in heterogeneous networks. In: UMAP, pp. 49–60 (2014)Google Scholar
  2. 2.
    Cao, X., Zheng, Y., Shi, C., Li, J., Wu, B.: Meta-path-based link prediction in schema-rich heterogeneous information network. Int. J. Data Sci. Analytics 3(4), 285–296 (2017)CrossRefGoogle Scholar
  3. 3.
    Feng, W., Wang, J.: Incorporating heterogeneous information for personalized tag recommendation in social tagging systems. In: KDD, pp. 1276–1284 (2012)Google Scholar
  4. 4.
    Han, J.: Mining heterogeneous information networks: the next frontier. In: KDD, p. Keynote speech (2012)Google Scholar
  5. 5.
    Haveliwala, T.H.: Topic-sensitive pagerank: a context-sensitive ranking algorithm for web search[J]. IEEE Trans. Knowl. Data Eng. 15(4), 784–796 (2003)CrossRefGoogle Scholar
  6. 6.
    Jamali, M., Lakshmanan, L.V.: Heteromf: recommendation in heterogeneous information networks using context dependent factor models. In: WWW, pp. 643–653 (2013)Google Scholar
  7. 7.
    Ji, M., Han, J., Danilevsky, M.: Ranking-based classification of heterogeneous information networks. In: KDD, pp. 1298–1306 (2011)Google Scholar
  8. 8.
    Kuo, T.T., Yan, R., Huang, Y., Kung, P.H., Lin, S.D.: Unsupervised link prediction using aggregative statistics on heterogeneous social networks. In: SIGKDD, pp. 775–783 (2013)Google Scholar
  9. 9.
    Lao, N., Cohen, W.: Fast query execution for retrieval models based on path constrained random walks. In: KDD, pp. 881–888 (2010)Google Scholar
  10. 10.
    Lao, N., Cohen, W.W.: Relational retrieval using a combination of path-constrained random walks. Mach. Learn. 81(2), 53–67 (2010)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Lee, S., Song, S., Kahng, M., Lee, D., Lee, S.: Random walk based entity ranking on graph for multidimensional recommendation. In: RecSys, pp. 93–100 (2011)Google Scholar
  12. 12.
    Lee, S., Park, S., Kahng, M., Lee, S.: Ranking nodes on a heterogeneous graph for flexible hybrid recommender systems. Expert Syst. Appl. 40, 684–697 (2013)CrossRefGoogle Scholar
  13. 13.
    Li, X., Chen, H.: Recommendation as link prediction in bipartite graphs: a graph kernel-based machine learning approach, decision support systems. Decis. Support Syst. 54, 880–890 (2013)CrossRefGoogle Scholar
  14. 14.
    Li, Y., Shi, C., Philip, S.Y., Chen, Q.: Hrank: a path based ranking method in heterogeneous information network. In: WAIM, pp. 553–565 (2014)Google Scholar
  15. 15.
    Lin, C.J.: Projected gradient methods for non-negative matrix factorization. In: Neural Computation, pp. 2756–2279 (2007)Google Scholar
  16. 16.
    Lippert, C., Weber, S. H., Huang, Y., Tresp, V., Schubert, M., Kriegel, H.P.: Relation prediction in multi-relational domains using matrix factorization. In: NIPS Workshop on Structured Input Structure Output (2008)Google Scholar
  17. 17.
    Liu, X., Yu, Y., Guo, C., Sun, Y.: Meta-path-based ranking with pseudo relevance feedback on heterogeneous graph for citation recommendation. In: CIKM, pp. 121–130 (2014)Google Scholar
  18. 18.
    Luo, C., Pang, W., Wang, Z., Lin, C.: Hete-cf: social-based collaborative filtering recommendation using heterogeneous relations. In: ICDM, pp. 917–922 (2014)Google Scholar
  19. 19.
    Ma, H., Yang, H., Lyu, M.R., King, I.: Sorec: social recommendation using probabilistic matrix factorization. In: CIKM, pp. 931–940 (2008)Google Scholar
  20. 20.
    Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: SIGIR, pp. 203–210 (2011)Google Scholar
  21. 21.
    Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: WSDM, pp. 287–296 (2011)Google Scholar
  22. 22.
    Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS, vol. 20 (2008)Google Scholar
  23. 23.
    Shi, C., Kong, X., Huang, Y., Yu, P.S., Wu, B.: Hetesim: A general framework for relevance measure in heterogeneous networks. IEEE Trans. Knowl. Data Eng. 26(10), 2479–2492 (2014)CrossRefGoogle Scholar
  24. 24.
    Shi, C., Zhang, Z., Luo, P., Yu, P.S., Yue, Y., Wu, B.: Semantic path based personalized recommendation on weighted heterogeneous information networks. In: CIKM, pp. 453–462 (2015)Google Scholar
  25. 25.
    Shi, C., Liu, J., Zhuang, F., Philip, S.Y., Wu, B.: Integrating heterogeneous information via flexible regularization framework for recommendation. Knowl. Inf. Syst. 49(3), 835–859 (2016)CrossRefGoogle Scholar
  26. 26.
    Shi, C., Li, Y., Zhang, J., Sun, Y., Yu, P.S.: A survey of heterogeneous information network analysis. CoRR arXiv:1511.04854
  27. 27.
    Srebro, N., Jaakkola, T.: Weighted low-rank approximations. In: ICML, pp. 720–727 (2003)Google Scholar
  28. 28.
    Sun, Y., Han, J., Yan, X., Yu, P., Wu, T.: Pathsim: meta path-based top-k similarity search in heterogeneous information networks. In: VLDB, pp. 992–1003 (2011)Google Scholar
  29. 29.
    Sun, Y., Han, J.: Mining heterogeneous information networks: a structural analysis approach. SIGKDD Explor. 14(2), 20–28 (2012)CrossRefGoogle Scholar
  30. 30.
    Sun, Y., Norick, B., Han, J., Yan, X., Yu, P.S., Yu, X.: Integrating meta-path selection with user guided object clustering in heterogeneous information networks. In: KDD, pp. 1348–1356 (2012)Google Scholar
  31. 31.
    Vahedian, F., Burke, R., Mobasher, B.: Weighted random walks for meta-path expansion in heterogeneous networks. In: RecSys 2016 Poster Proceedings (2016)Google Scholar
  32. 32.
    Wang, W., Yin, H., Chen, L., Sun, Y., Sadiq, S., Zhou, X.: Geo-sage: a geographical sparse additive generative model for spatial item recommendation. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1255–1264. ACM (2015)Google Scholar
  33. 33.
    Xie, M., Yin, H., Wang, H., Xu, F., Chen, W., Wang, S.: Learning graph-based poi embedding for location-based recommendation. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 15–24. ACM (2016)Google Scholar
  34. 34.
    Yin, H., Zhou, X., Shao, Y., Wang, H., Sadiq, S.: Joint modeling of user check-in behaviors for point-of-interest recommendation. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1631–1640. ACM (2015)Google Scholar
  35. 35.
    Yu, X., Ren, X., Gu, Q., Sun, Y., Han, J.: Collaborative filtering with entity similarity regularization in heterogeneous information networks. In: IJCAI HINA (2013)Google Scholar
  36. 36.
    Yu, X., Ren, X., Sun, Y., Gu, Q., Sturt, B., Khandelwal, U., Norick, B., Han, J.: Personalized entity recommendation: a heterogeneous information network approach. In: WSDM, pp. 283–292 (2014)Google Scholar
  37. 37.
    Yuan, Q., Chen, L., Zhao, S.: Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation. In: RecSys, pp. 245–252 (2011)Google Scholar
  38. 38.
    Zhang, Z., Zhou, T., Zhang, Y.: Personalized recommendation via integrated diffusion on usercitemctag tripartite graphs. Physica A: Stat. Mech. Appl. 389, 179–186 (2010)CrossRefGoogle Scholar
  39. 39.
    Zheng, J., Liu, J., Shi, C., Zhuang, F., Li, J., Wu, B.: Dual similarity regularization for recommendation. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 542–554. Springer (2016)Google Scholar
  40. 40.
    Zheng, J., Liu, J., Shi, C., Zhuang, F., Li, J., Wu, B.: Recommendation in heterogeneous information network via dual similarity regularization. Int. J. Data Sci. Analytics 3(1), 35–48 (2017)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Beijing Advanced Innovation Center for Imaging TechnologyCapital Normal UniversityBeijingChina
  2. 2.Beijing Key Lab of Intelligent Telecommunications Software and MultimediaBeijing University of Posts and TelecommunicationsBeijing ShiChina
  3. 3.Ant Financial Services GroupZhejiangChina
  4. 4.University of Southern CaliforniaLos AngelesUSA
  5. 5.University of Illinois at ChicagoChicagoUSA

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