Abstract
Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours. Costly annotations by experts are replaced by a distributed process where the users take the initiative. While the collaborative approach enables the collection of a vast amount of data, a new issue arises: the quality assessment. The elicitation of trust values among users, termed “web of trust”, allows a twofold enhancement of Recommender Systems. Firstly, the filtering process can be informed by the reputation of users which can be computed by propagating trust. Secondly, the trust metrics can help to solve a problem associated with the usual method of similarity assessment, its reduced computability. An empirical evaluation on Epinions.com dataset shows that trust propagation can increase the coverage of Recommender Systems while preserving the quality of predictions. The greatest improvements are achieved for users who provided few ratings.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American (2001)
Eaton, A.: RVW module for syndicating and aggregating reviews (2004), http://www.pmbrowser.info/rvw/0.2
Golbeck, J., Hendler, J., Parsia, B.: Trust networks on the Semantic Web. In: Proceedings of Cooperative Intelligent Agents (2003)
Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Communications of the ACM 35(12), 61–70 (1992)
Guha, R.: Open rating systems. Technical report, Stanford University, CA, USA (2003)
Guha, R., Kumar, R., Raghavan, P., Tomkins, A.: Propagation of trust and distrust. In: Proceedings of WWW 2004 (2004)
Herlocker, J., Konstan, J., Borchers, J.A., Riedl, J.: An Algorithmic Framework for Performing Collaborative Filtering. In: Proceedings of the 1999 Conference on Research and Development in Information Retrieval (1999)
Levien, R.: Advogato Trust Metric. PhD thesis, UC Berkeley, USA (2003)
Massa, P., Bhattacharjee, B.: Using trust in recommender systems: an experimental analysis. In: Proc. of 2nd Int. Conference on Trust Management (2004)
O’Mahony, M., Hurley, N., Kushmerick, N., Silvestre, G.: Collaborative recommendation: A robustness analysis. In: Proceedings of Int’l Semantic Web Conf., ISWC 2003 (2003)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report, Stanford, USA (1998)
Resnick, P., Varian, H.R.: Recommender systems. Communications of the ACM 40(3), 56–58 (1997)
Ziegler, C.: Semantic web recommender systems. In: Joint ICDE/EDBT Ph.D. Workshop (2004)
Ziegler, C., Lausen, G.: Spreading activation models for trust propagation. In: IEEE International Conference on e-Technology, e-Commerce, and e-Service, EEE 2004 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Massa, P., Avesani, P. (2004). Trust-Aware Collaborative Filtering for Recommender Systems. In: Meersman, R., Tari, Z. (eds) On the Move to Meaningful Internet Systems 2004: CoopIS, DOA, and ODBASE. OTM 2004. Lecture Notes in Computer Science, vol 3290. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30468-5_31
Download citation
DOI: https://doi.org/10.1007/978-3-540-30468-5_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23663-4
Online ISBN: 978-3-540-30468-5
eBook Packages: Springer Book Archive