Achille Fokoue
ABSTRACT
Decision makers (humans or software agents alike) are faced with the challenge of examining large volumes of information originating from heterogeneous sources with the goal of ascertaining trust in various pieces of information. In this paper we argue (using examples) that traditional trust models are limited in their data model by assuming a pair-wise numeric rating between two entities (e.g., eBay recommendations, Netflix movie rating, etc). We present a novel trust computational model for rich, complex and uncertain information encoded using Bayesian Description Logics. We present security and scalability tradeoffs that arise in the new model, and the results of an evaluation of the first prototype implementation under a variety attack scenarios.
- }}F. Baader, D. Calvanese, D. McGuinness, D. Nardi, and P. Patel-Schneider. The Description Logic Handbook. Cambridge University Press, 2003. Google Scholar
Digital Library
- }}C. D'Amato, N. Fanizzi, and T. Lukasiewicz. Tractable reasoning with bayesian description logics. In Scalable Uncertainty Management (SUM08), pages 146--159, 2008. Google ScholarDigital Library
- }}J. Dolby, A. Fokoue, A. Kalyanpur, E. Schonberg, and K. Srinivas. Scalable highly expressive reasoner (sher). J. Web Sem., 7(4):357--361, 2009. Google ScholarDigital Library
- }}T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. In Springer Series in Statistics, 2009.Google Scholar
- }}A. Kalyanpur. Debugging and Repair of OWL-DL Ontologies. PhD thesis, University of Maryland, 2006. Google ScholarDigital Library
- }}S. Kamvar, M. Schlosser, and H. Garcia-Molina. EigenTrust: Reputation management in P2P networks. In WWW, 2003. Google ScholarDigital Library
- }}U. Kuter and J. Golbeck. SUNNY: A New Algorithm for Trust Inference in Social Networks, using Probabilistic Confidence Models. In AAAI-07, 2007. Google ScholarDigital Library
- }}Netflix. Netflix Prize. http://www.netflixprize.com/.Google Scholar
- }}J. B. Schafer, J. Konstan, and J. Riedl. Recommender Systems in E-Commerce. In ACM Electronic Commerce, 1999. Google ScholarDigital Library
- }}L. Xiong and L. Liu. Supporting reputation based trust in peer-to-peer communities. In IEEE TKDE, Special Issue on P2P Data Management, vol. 71, 16(7), July 2004. Google ScholarDigital Library
Index Terms
- Assessing trust in uncertain information using Bayesian description logic
Recommendations
Average Shilling Attack against Trust-Based Recommender Systems
ICIII '09: Proceedings of the 2009 International Conference on Information Management, Innovation Management and Industrial Engineering - Volume 04Collaborative Filtering (CF) is considered a powerful technique for generating personalized recommendation. However, significant vulnerabilities have recently been identified in collaborative filtering recommender systems. Malicious users can inject a ...
Shilling recommender systems for fun and profit
WWW '04: Proceedings of the 13th international conference on World Wide WebRecommender systems have emerged in the past several years as an effective way to help people cope with the problem of information overload. One application in which they have become particularly common is in e-commerce, where recommendation of items ...
Using a trust network to improve top-N recommendation
RecSys '09: Proceedings of the third ACM conference on Recommender systemsTop-N item recommendation is one of the important tasks of recommenders. Collaborative filtering is the most popular approach to building recommender systems which can predict ratings for a given user and item. Collaborative filtering can be extended ...
Comments