Advertisement

Multiple Random Walks for Personalized Ranking with Trust and Distrust

  • Dimitrios RafailidisEmail author
  • Fabio Crestani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10450)

Abstract

Social networks with trust and distrust relationships has been an emerging topic, aiming at identifying users’ friends and foes when sharing information in social networks or purchasing products online. In this study we investigate how to generate accurate personalized rankings while considering both trust and distrust user relationships. This paper includes the following contributions, first we propose a social inference step of missing (indirect) trust relationships via multiple random walks, while considering users’ direct trust and distrust relationships during the inference. In doing so, we can better capture the missing trust relationships between users in an enhanced signed network. Then, we introduce a regularization framework to account for (i) the structural properties of the enhanced graph with the inferred trust relationships, and (ii) the user’s trust and distrust personalized preferences in the graph to produce his/her personalized ranking list. We evaluate the performance of the proposed approach on a benchmark dataset from Slashdot. Our experiments demonstrate the superiority of the proposed approach over state-of-the-art methods that also consider trust and distrust relationships in the personalized ranking task.

Keywords

Personalized ranking Signed graphs Social inference 

Notes

Acknowledgments

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

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    Jung, J., Jin, W., Sael, L., Kang, U.: Personalized ranking in signed networks using signed random walk with restart. In: Proceedings of 16th IEEE International Conference on Data Mining, Barcelona, Spain, pp. 973–978 (2016)Google Scholar
  5. 5.
    Kunegis, J., Lommatzsch, A., Bauckhage, C.: The slashdot zoo: mining a social network with negative edges. In: Proceedings of the 18th International Conference on World Wide Web, Madrid, Spain, pp. 741–750 (2009)Google Scholar
  6. 6.
    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
  7. 7.
    Rafailidis, D., Crestani, F.: Collaborative ranking with social relationships for top-n recommendations. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, Pisa, Italy, pp. 785–788 (2016)Google Scholar
  8. 8.
    Rafailidis, D., Crestani, F.: Joint collaborative ranking with social relationships in top-n recommendation. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, Indianapolis, IN, USA, pp. 1393–1402 (2016)Google Scholar
  9. 9.
    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
  10. 10.
    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
  11. 11.
    Rafailidis, D., Nanopoulos, A.: Repeat consumption recommendation based on users preference dynamics and side information. In: Proceedings of the 24th International Conference on World Wide Web Companion, Florence, Italy, pp. 99–100 (2015)Google Scholar
  12. 12.
    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
  13. 13.
    Shahriari, M., Jalili, M.: Ranking nodes in signed social networks. Social Netw. Analys. Min. 4(1), 172 (2014)CrossRefGoogle Scholar
  14. 14.
    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
  15. 15.
    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
  16. 16.
    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
  17. 17.
    Wu, Z., Aggarwal, C.C., Sun, J.: The troll-trust model for ranking in signed networks. In: Proceedings of the 9th ACM International Conference on Web Search and Data Mining, San Francisco, CA, USA, pp. 447–456 (2016)Google Scholar
  18. 18.
    Zhang, J., Zhan, Q., He, L., Aggarwal, C.C., Yu, P.S.: Trust hole identification in signed networks. In: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, Riva del Garda, Italy, pp. 697–713 (2016)Google Scholar
  19. 19.
    Zhou, D., Schlkopf, B.: A regularization framework for learning from graph data. In: ICML Workshop on Statistical Relational Learning and Its Connections to Other Fields, pp. 132–137 (2004)Google 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