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Trust-aware recommender systems
Abstract
Recommender Systems based on Collaborative Filtering suggest to users items they might like. However due to data sparsity of the input ratings matrix, the step of finding similar users often fails. We propose to replace this step with the use of a trust metric, an algorithm able to propagate trust over the trust network and to estimate a trust weight that can be used in place of the similarity weight. An empirical evaluation on Epinions.com dataset shows that Recommender Systems that make use of trust information are the most effective in term of accuracy while preserving a good coverage. This is especially evident on users who provided few ratings.
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Cited by
A Probabilistic Model for Using Social Networks in Personalized Item Recommendation
Chaney, A. J. B., Blei, D. M., & Eliassi-Rad, T. (2015) · Proceedings of the 9th ACM Conference on Recommender Systems · doi:10.1145/2792838.2800193 · Cited by 164
Multi-faceted trust and distrust prediction for recommender systems
Fang, H., Guo, G., & Zhang, J. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.01.005 · Cited by 66
Review of Extracting Information From the Social Web for Health Personalization
Fernandez-Luque, L., Karlsen, R., & Bonander, J. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1432 · Cited by 61
Tang, J., & Liu, H. (2015) · Synthesis Lectures on Information Security, Privacy, and Trust · doi:10.2200/s00657ed1v01y201507spt013 · Cited by 53
Huang, J., Oosterhuis, H., Mansoury, M., van Hoof, H., & de Rijke, M. (2024) · Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3626772.3657749 · Cited by 17
Metadata
- Authors
- Massa, P., & Avesani, P.
- Venue
- Proceedings of the 2007 ACM conference on Recommender systems
- Publisher
- ACM
- Published
- 2007-10-19
- Pages
- 17-24
- OpenAlex
- W2133266261
- Cited by
- 1,190
- References
- 0
- FWCI
- 23.02
- Type
- conference paper
- Language
- en
- Open access
- closed
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