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A Recommendation Algorithm Considering User Trust and Interest

  • Chuanmin MiEmail author
  • Peng Peng
  • Rafał Mierzwiak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)

Abstract

A traditional collaborative filtering recommendation algorithm has problems with data sparseness, a cold start and new users. With the rapid development of social network and e-commerce, building the trust between users and user interest tags to provide a personalized recommendation is becoming an important research issue. In this study, we propose a probability matrix factorization model (STUIPMF) by integrating social trust and user interest. First, we identified implicit trust relationship between users and potential interest label from the perspective of user rating. Then, we used a probability matrix factorization model to conduct matrix decomposition of user ratings information, user trust relationship, and user interest label information, and further determined the user characteristics to ease data sparseness. Finally, we used an experiment based on the Epinions website’s dataset to verify our proposed method. The results show that the proposed method can improve the recommendation’s accuracy to some extent, ease a cold start and solve new user problems. Meanwhile, the STUIPMF approach, we propose, also has a good scalability.

Keywords

Data mining Recommender system Collaborative filtering Social trust Interest tag Probability matrix factorization 

References

  1. 1.
    Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl.-Based Syst. 46, 109–132 (2013)CrossRefGoogle Scholar
  2. 2.
    Borchers, A., Herlocker, J., Konstan, J., Reidl, J.: Ganging up on information overload. Computer 31(4), 106–108 (1998)CrossRefGoogle Scholar
  3. 3.
    Gemulla, R., Nijkamp, E., Haas, P.J., Sismanis, Y.: Large-scale matrix factorization with distributed stochastic gradient descent. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 69–77. ACM (2011)Google Scholar
  4. 4.
    Golbeck, J.: Personalizing applications through integration of inferred trust values in semantic web-based social networks. In: 2005 Proceedings on Semantic Network Analysis Workshop, Galway, Ireland (2005)Google Scholar
  5. 5.
    Guo, G., Zhang, J., Zhu, F., Wang, X.: Factored similarity models with social trust for top-N item recommendation. Knowl.-Based Syst. 122, 17–25 (2017)CrossRefGoogle Scholar
  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 (2009)Google Scholar
  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 (2010)Google Scholar
  8. 8.
    Kim, H., Kim, H.-J.: A framework for tag-aware recommender systems. Expert Syst. Appl. 41(8), 4000–4009 (2014)CrossRefGoogle Scholar
  9. 9.
    Koenigstein, N., Paquet, U.: Xbox movies recommendations: variational Bayes matrix factorization with embedded feature selection. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 129–136. ACM (2013)Google Scholar
  10. 10.
    Lee, W.P., Ma, C.Y.: Enhancing collaborative recommendation performance by combining user preference and trust-distrust propagation in social networks. Knowl.-Based Syst. 106, 125–134 (2016)CrossRefGoogle Scholar
  11. 11.
    Li, J., Chen, C., Chen, H., Tong, C.: Towards context-aware social recommendation via individual trust. Knowl.-Based Syst. 127, 58–66 (2017)CrossRefGoogle Scholar
  12. 12.
    Lim, H., Kim, H.-J.: Item recommendation using tag emotion in social cataloging services. Expert Syst. Appl. 89, 179–187 (2017)CrossRefGoogle Scholar
  13. 13.
    Lu, Z., Agarwal, D., Dhillon, I.S.: A spatio-temporal approach to collaborative filtering. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 13–20. ACM (2009)Google Scholar
  14. 14.
    Luo, X., Xia, Y., Zhu, Q.: Incremental collaborative filtering recommender based on regularized matrix factorization. Knowl.-Based Syst. 27, 271–280 (2012)CrossRefGoogle Scholar
  15. 15.
    Ma, H., Yang, H., Lyu, M.R., King, I.: SoRec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 931–940. ACM (2008)Google Scholar
  16. 16.
    Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 287–296. ACM (2011)Google Scholar
  17. 17.
    Ma, H., Zhou, T.C., Lyu, M.R., King, I.: Improving recommender systems by incorporating social contextual information. ACM Trans. Inf. Syst. (TOIS) 29(2), 9 (2011)CrossRefGoogle Scholar
  18. 18.
    Massa, P., Avesani, P.: Trust-aware recommender systems. In: Proceedings of the 2007 ACM Conference on Recommender Systems, pp. 17–24. ACM (2007)Google Scholar
  19. 19.
    Mi, C., Shan, X., Qiang, Y., Stephanie, Y., Chen, Y.: A new method for evaluating tour online review based on grey 2-tuple linguistic. Kybernetes 43(3/4), 601–613 (2014)CrossRefGoogle Scholar
  20. 20.
    Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, pp. 1257–1264 (2008)Google Scholar
  21. 21.
    Sun, X., Kong, F., Ye, S.: A comparison of several algorithms for collaborative filtering in startup stage. In: 2005 IEEE Proceedings of Networking, Sensing and Control, pp. 25–28. IEEE (2005)Google Scholar
  22. 22.
    Tao, J., Zhang, N.: Similarity measurement method based on user’s interesting-ness in collaborative filtering. Comput. Syst. Appl. 20(5), 55–59 (2011)Google Scholar
  23. 23.
    Zuo, Y., Zeng, J., Gong, M., Jiao, L.: Tag-aware recommender systems based on deep neural networks. Neurocomputing 204, 51–60 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Nanjing University of Aeronautics and AstronauticsNanjingPeople’s Republic of China
  2. 2.Poznan University of TechnologyPoznanPoland

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