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Opinion-Based Filtering through Trust

  • Miquel Montaner
  • Beatriz López
  • Josep Lluís de la Rosa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2446)

Abstract

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.

Keywords

Recommender System Multiagent System Relevance Feedback Social Model Initial Trust 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Miquel Montaner
    • 1
  • Beatriz López
    • 1
  • Josep Lluís de la Rosa
    • 1
  1. 1.Institut d’Informàtica i Aplicacions Agents Research LaboratoryUniversitat de GironaGironaSpain

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