Advertisement

International Conference on Web-Age Information Management

WAIM 2015: Web-Age Information Management pp 53-64 | Cite as

A Novel Recommendation Algorithm Based on Heterogeneous Information Network Similarity and Preference Diffusion

  • Bangzuo ZhangEmail author
  • Shulin Tang
  • Zongming Ying
  • Yongjian Cai
  • Guiping Xu
  • Kun Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9391)

Abstract

Recommender system has been proposed as a key tool to overcome the problem of information overload. In the present era of big data, how to utilization the side information of users, items is a new challenge. This paper put forward a novel solution based on the heterogeneous information network and preference diffusion. The similarity matrices of users and items are initially computed based on meta-path similarity algorithm; three new preference diffusion methods has been proposed to fuse the similarity matrix and the user-item rating matrix; finally uses the traditional recommendation techniques based on matrix factorization to predict the results. With the experiment in a classical data set MovieLens 100 K and the movie attributes extended from IMDb, verifies the effectiveness of the solution that with heterogeneous information network to make full use of users and item attributes information and the preference diffusion with rating matrix can improve the recommendation accuracy effectively.

Keywords

Heterogeneous information network Matrix factorization Meta-path Collaborative filtering Recommender system 

Notes

Acknowledgments

This work is supported by Jilin Provincial Science and Technology Key Project (20150204040GX), National Training Programs of Innovation and Entrepreneurship for Undergraduates (201410200042).

References

  1. 1.
    Lu, L., Medo, M., Yeung, C.H., Zhang, Y.-C., Zhang, Z.-K., Zhou, T.: Recommender systems. Phys. Rep. 519(1), 1–49 (2012)CrossRefGoogle Scholar
  2. 2.
    Speier, C., Valacich, J.S., Vessey, I.: The influence of task interruption on individual decision making: an information overload perspective. Decis. Sci. 30(2), 337–360 (1999)CrossRefGoogle Scholar
  3. 3.
    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)CrossRefGoogle Scholar
  4. 4.
    Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press, Cambridge (2011)Google Scholar
  5. 5.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Know. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  6. 6.
    Shi, Y., Larson, M., Hanjalic, A.: Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges. ACM Computing Surveys (CSUR), 47(1), Article No.3 (2014)Google Scholar
  7. 7.
    Han, J.: Mining Heterogeneous Information Networks by Exploring the Power of Links. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds.) DS 2009. LNCS, vol. 5808, pp. 13–30. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Cai, D., Shao, Z., He, X., Yan, X., Han, J.: Community mining from multi-relational networks. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 445–452. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Sun, Y., Han, J.: Mining Heterogeneous Information Networks: Principles and Methodologies. Morgan & Claypool, Beijing (2012)Google Scholar
  10. 10.
    Grčar, M., Lavrač, N.: A methodology for mining document-enriched heterogeneous information networks. In: Elomaa, T., Hollmén, J., Mannila, H. (eds.) DS 2011. LNCS, vol. 6926, pp. 107–121. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Shi, C., Zhou, C., Kong, X., Yu, P., Liu, G.: HeteRecom: a semantic recommendation system in heterogeneous networks. In: Proceedings of the 18st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1552–1555 (2012)Google Scholar
  12. 12.
    Feng, W., Wang, J.: Incorporating heterogeneous information for personalized tag recommendation in social tagging systems. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1276–1284 (2012)Google Scholar
  13. 13.
    Jamali, M., Lakshmanan, L.: HeteroMF: recommendation in heterogeneous information networks using context dependent factor models. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 643–654 (2013)Google Scholar
  14. 14.
    Liu, X., Yu, Y., Guo, C., Sun, Y., Gao, L.: Full-text based context-rich heterogeneous network mining approach for citation recommendation. In: ACM/IEEE Joint Conference on Digital Libraries (JCDL 2014), London (2014)Google Scholar
  15. 15.
    Yu, X., Ren, X., Sun, Y., Sturt, B., Khandelwal, U., Gu, Q., Norick, B., Han, J.: Recommendation in heterogeneous information networks with implicit user feedback. In: Proceedings of 2013 ACM International Conference Series on Recommendation Systems (2013)Google Scholar
  16. 16.
    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: Proceedings of 2014 ACM International Conference on Web Search and Data Mining (WSDM 2014) (2014)Google Scholar
  17. 17.
    Yu, X., Ren, X., Gu, Q., Sun, Y., Han, J.: Collaborative filtering with entity similarity regularization in heterogeneous information networks. In: Proceedings of IJCAI-2013 HINA Workshop (2013)Google Scholar
  18. 18.
    Sun, Y., Han, J., Yan, X., Yu, P.S., Tianyi, W.: PathSim: meta path-based top-K similarity search in heterogeneous information networks. PVLDB 4(11), 992–1003 (2011)Google Scholar
  19. 19.
    Shi, C., Kong, X., Yu, P.S., Xie, S., Wu, B.: Relevance search in heterogeneous networks. In: Proceedings of the 15th International Conference on Extending Database Technology, pp. 180–191 (2012)Google Scholar
  20. 20.
    Shi, C., Kong, X., Huang, Y., Yu, P.S., Bin, W.: HeteSim: a general framework for relevance measure in heterogeneous networks. IEEE Trans. Knowl. Data Eng. 26(10), 2479–2492 (2014)CrossRefGoogle Scholar
  21. 21.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. Technical report, Stanford Univ. Database Group (1998)Google Scholar
  22. 22.
    Jeh, G., Widom, J.: Simrank: a measure of structural-context similarity. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 538–543 (2002)Google Scholar
  23. 23.
    Sarwar, B.M., Karypis, G., Konstan, J.A., Reidl, J.: Application of dimensionality reduction in recommender system - a case study. In: ACM WebKDD 2000 Web Mining for E-Commerce Workshop (2000)Google Scholar
  24. 24.
    Lee, D.D., Seung, H.S.: Learning the parts of Objects by Non-negative Matrix Factorization. Lett. Nat. 401, 788–791 (1999)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Bangzuo Zhang
    • 1
    Email author
  • Shulin Tang
    • 1
  • Zongming Ying
    • 1
  • Yongjian Cai
    • 1
  • Guiping Xu
    • 1
  • Kun Xu
    • 1
  1. 1.School of Computer Science and Information TechnologyNortheast Normal UniversityChangchunChina

Personalised recommendations