Opinion-Based Filtering through Trust
Recommender systems help users to identify particular items that best match their tastes or preferences. When we apply the agent theory to this domain, a standard centralized recommender system becomes a distributed world of recommender agents. Therefore, due to the agent’s world, a new information filtering method appears: the opinion-based filtering method. Its main idea is to consider other agents as personal entities which you can rely on or not. Recommender agents can ask their reliable friends for an opinion about a particular item and filter large sets of items based on it. Reliability is expressed through a trust value with which each agent labels its neighbors. Thus, the opinion-based filtering method needs a model of trust in the collaborative world. The model proposed emphasizes proactiveness since the agent looks for other agents in a situation of lack of information instead of remaining passive or providing either a negative or empty answer to the user. Finally, our social model of trust exploits interactiveness while preserving privacy.
KeywordsRecommender System Multiagent System Relevance Feedback Social Model Initial Trust
Unable to display preview. Download preview PDF.
- 1.C. Castelfranchi. Information agents: The social nature of information and the role of trust, 2001. Invited Contribution at Cooperative Information Agents V (CIA’01). Modena (Italy).Google Scholar
- 2.C. Castelfranchi and R. Falcone. Principles of trust for MAS: Cognitive anatomy, social importance, and quantification. In Demazeau, Y. (ed.), Proceedings of the Third International Conference on Multi-Agent Systems, pages 72–79. IEEE Computer Society, Los Alamitos, 1998.Google Scholar
- 3.G. Elofson. Developing trust with intelligent agents: An exploratory study. In Proceedings of the First International Workshop on Trust, pages 125–139, 1998.Google Scholar
- 4.FIPA. http://www.fipa.org/speci.cations/index.html, 2001.
- 5.D. Gambetta. Can we trust trust? In Trust: Making and Breaking Cooperative Relations, pages 213–237. Gambetta, D (editor). Basil Blackwell. Oxford, 1990.Google Scholar
- 6.N. Glance, D. Arregui, and M. Dardenne. Knowledge pump: Supporting the flow and use of knowledge. In Information Technologyfor Knowledge Management, pages 35–45. Eds. U. Borgho. and R. Pareschi, New York: Springer-Verlag, 1998.Google Scholar
- 7.M. Klusch. Information agent technology for the internet: A survey. In Journal on Data and Knowledge Engineering, Special Issue on Intelligent Information Integration, volume 36:6. D. Fensel (Ed.), Elsevier Science, 2001.Google Scholar
- 8.S. P. March. Formalising trust as a computational concept. In Phd Thesis, Department of Computing Science and Mathematics, Universityof Stirling, 1994.Google Scholar
- 9.M. Montaner. Personalized agents based on case-based reasoning and trust in the collaborative world. In Thesis Proposal, Universityof Girona, 2001.Google Scholar
- 10.M. Montaner, B. López, and J. L. de la Rosa. Developing trust in recommender agents. Accepted as Poster at the First International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS’02). Palazzo Re Enzo (Italy), 2002.Google Scholar
- 11.M. Montaner, B. López, and J. L. de la Rosa. A taxonomy of recommender agents on the internet. In Submitted to Artificial Intelligence Review, 2002.Google Scholar
- 12.J. Sabater and C. Sierra. Regret: A reputation model for gregarious societies. In Research Report. Institut d’Investigació i Intel.ligència Artificial, 2000.Google Scholar
- 13.M. Schillo and P. Funk. Who can you trust: Dealing with deception. In Proceedings of the Workshop Deception, Fraud and Trust in Agent Societies at the Autonomous Agents Conference, pages 95–106, 1999.Google Scholar
- 14.M. Schillo, P. Funk, and M. Rovatsos. Using trust for detecting deceitful agents in artificial societites. In Applied Artificial Intelligence, Special Issue on Trust, Deception and Fraud in Agent Societies, 2000.Google Scholar
- 15.L. Steels and P. Vogt. Grounding adaptive language games in robotic agents. In Proceedings of the Fourth European Conference on Artificial Life, pages 473–484, 1997.Google Scholar
- 17.V. Torra. On the integration of numerical information: from the arithmetic mean to fuzzy integrals. Torra V. (Ed). Information fusion in data mining. Physiin-Verlag. (Forthcoming), 2001.Google Scholar
- 18.A. Valls. Development of a method for multiple criteria decision making based on negation fuctions. chapter 2: State of the art. Thesis proposal, Artificial Intelligence Program, UPC, 2000.Google Scholar
- 19.R. Vilà and M. Montaner. Implementació d’un sistema multiagent distribuit format per agents personals que recomanen restaurants aplicant raonament basat en casos i tècniques de trust, 2002. Projecte Fi de Carrera en Enginyeria Informàtica, Universitat de Girona.Google Scholar
- 21.B. Yu and M. P. Singh. A social mechanism of reputation management in electronic communities. In cooperative information agents, CIA-2000, pages 154–165, 2000.Google Scholar