A Regularization Method with Inference of Trust and Distrust in Recommender Systems

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10535)


In this study we investigate the recommendation problem with trust and distrust relationships to overcome the sparsity of users’ preferences, accounting for the fact that users trust the recommendations of their friends, and they do not accept the recommendations of their foes. In addition, not only users’ preferences are sparse, but also users’ social relationships. So, we first propose an inference step with multiple random walks to predict the implicit-missing trust relationships that users might have in recommender systems, while considering users’ explicit trust and distrust relationships during the inference. We introduce a regularization method and design an objective function with a social regularization term to weigh the influence of friends’ trust and foes’ distrust degrees on users’ preferences. We formulate the objective function of our regularization method as a minimization problem with respect to the users’ and items’ latent features and then we solve our recommendation problem via gradient descent. Our experiments confirm that our approach preserves relatively high recommendation accuracy in the presence of sparsity in both the users’ preferences and social relationships, significantly outperforming several state-of-the-art methods.


Recommender systems Collaborative filtering Social relationships Regularization 



Dimitrios Rafailidis was supported by the COMPLEXYS and INFORTECH Research Institutes of University of Mons.


  1. 1.
    Forsati, R., Barjasteh, I., Masrour, F., Esfahanian, A., Radha, H.: Pushtrust: an efficient recommendation algorithm by leveraging trust and distrust relations. In: Proceedings of the 9th ACM Conference on Recommender Systems, Vienna, Austria, pp. 51–58 (2015)Google Scholar
  2. 2.
    Forsati, R., Mahdavi, M., Shamsfard, M., Sarwat, M.: Matrix factorization with explicit trust and distrust side information for improved social recommendation. ACM Trans. Inf. Syst. 32(4), 17:1–17:38 (2014)CrossRefGoogle Scholar
  3. 3.
    Guha, R.V., Kumar, R., Raghavan, P., Tomkins, A.: Propagation of trust and distrust. In: Proceedings of the 13th ACM International Conference on World Wide Web, New York, NY, USA, pp. 403–412 (2004)Google Scholar
  4. 4.
    Guo, G., Zhang, J., Yorke-Smith, N.: TrustSVD: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence, Austin, Texas, USA, pp. 123–129 (2015)Google Scholar
  5. 5.
    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, Paris, France, pp. 397–406 (2009)Google Scholar
  6. 6.
    Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the 2010 ACM Conference on Recommender Systems, Barcelona, Spain, pp. 135–142 (2010)Google Scholar
  7. 7.
    Jang, M., Faloutsos, C., Kim, S., Kang, U., Ha, J.: PIN-TRUST: fast trust propagation exploiting positive, implicit, and negative information. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, Indianapolis, IN, USA, pp. 629–638 (2016)Google Scholar
  8. 8.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA, pp. 426–434 (2008)Google Scholar
  9. 9.
    Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, Denver, CO, USA, pp. 556–562 (2000)Google Scholar
  10. 10.
    Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Boston, MA, USA, pp. 203–210 (2009)Google Scholar
  11. 11.
    Ma, H., Lyu, M.R., King, I.: Learning to recommend with trust and distrust relationships. In: Proceedings of the 2009 ACM Conference on Recommender Systems, New York, NY, USA, pp. 189–196 (2009)Google Scholar
  12. 12.
    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, Napa Valley, California, USA, pp. 931–940 (2008)Google Scholar
  13. 13.
    Milgram, S.: The small world problem. Psychol. Today 2, 60–67 (1967)Google Scholar
  14. 14.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The pageRank citation ranking: bringing order to the web. Technical report, Stanford Digital Libraries SIDL-WP-1999-0120 (1999)Google Scholar
  15. 15.
    Rafailidis, D.: Modeling trust and distrust information in recommender systems via joint matrix factorization with signed graphs. In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, Pisa, Italy, pp. 1060–1065 (2016)Google Scholar
  16. 16.
    Rafailidis, D., Kefalas, P., Manolopoulos, Y.: Preference dynamics with multimodal user-item interactions in social media recommendation. Expert Syst. Appl. 74, 11–18 (2017)CrossRefGoogle Scholar
  17. 17.
    Rafailidis, D., Nanopoulos, A.: Modeling the dynamics of user preferences in coupled tensor factorization. In: Proceedings of the 8th ACM Conference on Recommender Systems, Foster City, Silicon Valley, CA, USA, pp. 321–324 (2014)Google Scholar
  18. 18.
    Rafailidis, D., Nanopoulos, A.: Modeling users preference dynamics and side information in recommender systems. IEEE Trans. Syst. Man Cybern. Syst. 46(6), 782–792 (2016)CrossRefGoogle Scholar
  19. 19.
    Tang, J., Aggarwal, C.C., Liu, H.: Recommendations in signed social networks. In: Proceedings of the 25th ACM International Conference on World Wide Web, Montreal, Canada, pp. 31–40 (2016)Google Scholar
  20. 20.
    Tang, J., Chang, Y., Aggarwal, C., Liu, H.: A survey of signed network mining in social media. ACM Comput. Surv. 49(3), 42:1–42:37 (2016)CrossRefGoogle Scholar
  21. 21.
    Tang, J., Hu, X., Chang, Y., Liu, H.: Predictability of distrust with interaction data. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, Shanghai, China, pp. 181–190 (2014)Google Scholar
  22. 22.
    Tang, J., Hu, X., Liu, H.: Social recommendation: a review. Soc. Netw. Anal. Min. 3(4), 1113–1133 (2013)CrossRefGoogle Scholar
  23. 23.
    Victor, P., Cornelis, C., Cock, M.D., Teredesai, A.: Trust-and distrust-based recommendations for controversial reviews. IEEE Intell. Syst. 26(1), 48–55 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of MonsMonsBelgium
  2. 2.Faculty of InformaticsUniversità della Svizzera italianaLuganoSwitzerland

Personalised recommendations