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An Agent-Based Architecture for Personalized Recommendations

  • Amel Ben Othmane
  • Andrea Tettamanzi
  • Serena Villata
  • Nhan LE Thanh
  • Michel Buffa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10162)

Abstract

This paper proposes a design framework for a personalized multi-agent recommender system. More precisely, the proposed framework is a multi-context based recommender system that takes into account user preferences to generate a plan satisfying those preferences. Agents in this framework have a Belief-Desire-Intention (BDI) component based on the well-known BDI architecture. These BDI agents are empowered with cognitive capabilities in order to interact with others agents. They are also able to adapt to the environment changes and to the information coming from other agents. The architecture includes also a planning module based on ontologies in order to represent and reason about plans and intentions. The applicability of the proposed model is shown through a simulation in the NetLogo environment.

Keywords

Recommender System Possibility Distribution Trust Degree Mental Attitude Necessity Measure 
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 International Publishing AG 2017

Authors and Affiliations

  • Amel Ben Othmane
    • 1
  • Andrea Tettamanzi
    • 3
  • Serena Villata
    • 2
  • Nhan LE Thanh
    • 3
  • Michel Buffa
    • 3
  1. 1.WIMMICS Research Team, InriaSophia AntipolisFrance
  2. 2.CNRSSophia AntipolisFrance
  3. 3.Univ. Nice Sophia AntipolisSophia AntipolisFrance

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