Towards a Spatio-Temporal Agent-Based Recommender System

Amel Ben Othmane 1 Andrea Tettamanzi 1 Serena Villata 1 Nhan Le Thanh 1
1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : Agent-based recommender systems have been widely employed in the last years to provide informative suggestions to users, showing the advantage of exploiting components like beliefs, goals and trust in the recommendation computation. However, many real-world recommendation scenarios, like the traffic or the health ones, require to represent and reason about spatial and temporal knowledge, considering also their inner incomplete and vague connotation. This paper tackles this challenge, and introduces STARS, an agent-based recommender system based on the Belief-Desire-Intention (BDI) architecture. Our approach extends the BDI model with spatial and temporal reasoning to represent and reason about fuzzy beliefs and desires dynamics.
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Communication dans un congrès
Kate Larson; Michael Winikoff; Sanmay Das; Edmund Durfee. 16th Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2017), May 2017, São Paulo, Brazil. ACM, 2017, Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2017, São Paulo, Brazil, May 8–12, 2017. 〈http://dl.acm.org/citation.cfm?id=3091397〉
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Amel Ben Othmane, Andrea Tettamanzi, Serena Villata, Nhan Le Thanh. Towards a Spatio-Temporal Agent-Based Recommender System. Kate Larson; Michael Winikoff; Sanmay Das; Edmund Durfee. 16th Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2017), May 2017, São Paulo, Brazil. ACM, 2017, Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2017, São Paulo, Brazil, May 8–12, 2017. 〈http://dl.acm.org/citation.cfm?id=3091397〉. 〈hal-01531169〉

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