Abstract
Recommender systems suggest the most appropriate items to users in order to help customers to find the most relevant items and facilitate sales. Collaborative filtering recommendation algorithm is the most successful technique for recommendation. In view of the fact that collaborative filtering systems depend on neighbors as the source of information, the recommendation quality of this approach depends on the neighbor’s selection. However, selecting neighbors can either stem from similarity or trust metrics. In this paper, we analyze these two types of neighbor’s selection metrics used in the field of recommendation in the literature. For each type, we first define it and then review different proposed metrics.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Gediminas, A., Alexander, T.: Toward the next generation of recommender systems: a survey of the state of the art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 734–749 (2005)
Guibing, G., Jie, Z., Neil, Y.S.: A novel Bayesian similarity measure for recommender systems. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), pp. 2619–2625 (2013)
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Addison Wesley, Boston (2006). Chap. 2
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)
Mayer, R.C., Davis, J.H., Schoorman, F.D.: An integrative model of organizational trust. Acad. Manag. Rev. 20, 709–734 (1995)
Guo, G.: Integrating trust and similarity to ameliorate the data sparsity and cold start for recommender systems. In: Proceedings of the 7th ACM Conference on Recommender Systems (RecSys) (2013)
Jennifer, G., James, H.: Filmtrust: movie recommendations using trust in web-based social networks. In: Proceedings of the IEEE Consumer Communications and Networking Conference (CCNC), vol. 1, pp. 282–286 (2006)
Christiano, C., Rino, F.: Trust Theory: A Socio-Cognitive and Computational Model. Wiley, Hoboken (2010)
Raph, L., Alexander, A.: Advogato’s Trust Metric (2002). http://advogato.org/trust-metric.html
Ziegler, C.-N.: Towards decentralized recommender systems. Ph.D. thesis, University of Freiburg (2005)
Jennifer, A.G.: Computing and applying trust in web-base social networks. Ph.D. thesis (2005)
Paolo, M., Paolo, A.: Trust-aware recommender systems. In: Proceedings of the 2007 ACM Conference on Recommender Systems, pp. 17–24 (2007)
Roger, C., Davis, J.H., Schoorman, F.D.: An integrative model of organizational trust. Acad. Manage. Rev. 20(3), 709–734 (1995)
Young, A.K., Rasik, P.: A trust prediction framework in rating-based experience sharing social networks without a web of trust. Inf. Sci. 191, 128–145 (2012)
Nguyen, V.A., Lim, E.P., Jiang, J., Sun, A.: To trust or not to trust? Predicting online trusts using trust antecedent framework (2008)
Guinbing, G., Jie, Z., Daniel, T., Neil, Y.S.: ETAF: an extended trust antecedents framework for trust prediction. In: International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 540–547 (2014)
Guinbing, G.: Integrating trust and similarity to ameliorate the data sparsity and cold start for recommender systems. In: 7th ACM Conference on Recommender Systems (RecSys) (2013)
Lathia, N., Hailes, S., Capra, L.: Trust-based collaborative filtering. In: Karabulut, Y., Mitchell, J., Herrmann, P., Jensen, C.D. (eds.) IFIPTM 2008. ITIFIP, vol. 263, pp. 119–134. Springer, Heidelberg (2008). doi:10.1007/978-0-387-09428-1_8
Papagelis, M., Plexousakis, D., Kutsuras, T.: Alleviating the sparsity problem of collaborative filtering using trust inferences. In: Herrmann, P., Issarny, V., Shiu, S. (eds.) iTrust 2005. LNCS, vol. 3477, pp. 224–239. Springer, Heidelberg (2005). doi:10.1007/11429760_16
Hwang, C.-S., Chen, Y.-P.: Using trust in collaborative filtering recommendation. In: Okuno, H.G., Ali, M. (eds.) IEA/AIE 2007. LNCS (LNAI), vol. 4570, pp. 1052–1060. Springer, Heidelberg (2007). doi:10.1007/978-3-540-73325-6_105
Qusai, S., Jie, L.: A trust-semantic fusion-based recommendation approach for e-business applications. Decis. Support Syst. 54(1), 768–780 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Jallouli, M., Lajmi, S., Amous, I. (2017). Similarity and Trust Metrics Used in Recommender Systems: A Survey. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_102
Download citation
DOI: https://doi.org/10.1007/978-3-319-53480-0_102
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-53479-4
Online ISBN: 978-3-319-53480-0
eBook Packages: EngineeringEngineering (R0)