Advertisement

A New Trust-Based Collaborative Filtering Measure Using Bhattacharyya Coefficient

  • Xiaofan Qin
  • Wenan TanEmail author
  • Anqiong Tang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1042)

Abstract

With the rapid growth of network data and demands of users, the concept of AI in recommendation system has become a hot academic topic. However, in sparse data, it is difficult for the current user to obtain his efficient neighbors and for some cold-start users, it doesn’t do anything. Therefore, we constructed a new measure of trust between users for neighborhood based on Collaborative filtering(CF) which uses a pair of users common ratings and exploits Bhattacharyya similarity to finds relevance of each pair of rated items. We also have measured the validity of the proposed model through accuracy, recall rate and F1 measures. The results show that although some recall rates will be lost, the precision is greatly improved. Overall, it achieved good results.

Keywords

Collaborative filtering Trust method Bhattacharyya coefficient Sparsity problem 

Notes

Acknowledgement

The paper is funded by the National Natural Science Foundation of Grant No. 61272036. Meanwhile, it is also funded by the Central University Fundamental Research Fund and the Key Discipline of Shanghai Second Polytechnic University. The grant numbers are NZ2013306 and XXKZD1604 respectively.

References

  1. 1.
    Park, S.T., Pennock, D.M.: Applying collaborative filtering techniques to movie search for better ranking and browsing. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 550–559. ACM, New York (2007)  https://doi.org/10.1145/1281192.1281252
  2. 2.
    Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-72079-9_9CrossRefGoogle Scholar
  3. 3.
    Liu, H., Hu, Z., Mian, A., Tian, H., Zhu, X.: A new user similarity model to improve the accuracy of collaborative filtering. Knowl.-Based Syst. 56, 156–166 (2014).  https://doi.org/10.1016/j.knosys.2013.11.006CrossRefGoogle Scholar
  4. 4.
    Najafabadi, M.K., Mahrin, M.N.R., Chuprat, S., Sarkan, H.M.: Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data. Comput. Hum. Behav. 67, 113–128 (2017).  https://doi.org/10.1016/j.chb.2016.11.010CrossRefGoogle Scholar
  5. 5.
    Golbeck, J., Hendler, J.: FilmTrust: Movie recommendations using trust in web-based social networks. In: Proceedings of the IEEE Consumer Communications and Networking Conference, vol. 96, pp. 282–286(2006).  https://doi.org/10.1109/CCNC.2006.1593032
  6. 6.
    Jamali, M., Ester, M.: TrustWalker: a random walk model for combining trust-based and item-based recommendation. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 397–406. ACM, New York (2009).  https://doi.org/10.1145/1557019.1557067
  7. 7.
    Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the fourth ACM Conference on Recommender Systems, pp. 135–142. ACM, Barcelona (2010).  https://doi.org/10.1145/1864708.1864736
  8. 8.
    Jin, J., Chen, Q.: A trust-based Top-K recommender system using social tagging network. In: 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, pp. 1270–1274. IEEE, Sichuan (2012).  https://doi.org/10.1109/FSKD.2012.6234277
  9. 9.
    Jia, D., Zhang, F., Liu, S.: A robust collaborative filtering recommendation algorithm based on multidimensional trust model. JSW 8(1), 11–18 (2013)CrossRefGoogle Scholar
  10. 10.
    Forsati, R., Barjasteh, I., Masrour, F., Esfahanian, A.H., Radha, H.: PushTrust: an efficient recommendation algorithm by leveraging trust and distrust relations. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 51–58. ACM, Vienna (2015).  https://doi.org/10.1145/2792838.2800198
  11. 11.
    Reshma, M., Pillai, R.R.: Semantic based trust recommendation system for social networks using virtual groups. In: 2016 International Conference on Next Generation Intelligent Systems (ICNGIS), pp. 1–6. IEEE, Kottayam (2016).  https://doi.org/10.1109/ICNGIS.2016.7854045
  12. 12.
    Patra, B.K., Launonen, R., Ollikainen, V., Nandi, S.: Exploiting Bhattacharyya similarity measure to diminish user cold-start problem in sparse data. In: Džeroski, S., Panov, P., Kocev, D., Todorovski, L. (eds.) DS 2014. LNCS (LNAI), vol. 8777, pp. 252–263. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-11812-3_22CrossRefGoogle Scholar
  13. 13.
    Patra, B.K., Launonen, R., Ollikainen, V., Nandi, S.: A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data. Knowl.-Based Syst. 82, 163–177 (2015).  https://doi.org/10.1016/j.knosys.2015.03.001CrossRefGoogle Scholar
  14. 14.
    Shambour, Q., Lu, J.: A trust-semantic fusion-based recommendation approach for e-business applications. Decis. Support Syst. 54(1), 768–780 (2012).  https://doi.org/10.1016/j.dss.2012.09.005CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.School of Computer and Information EngineeringShanghai Polytechnic UniversityShanghaiChina

Personalised recommendations