Agent-based recommender systems are tools able to assist users’ choices with suggestions coming closest to their orientations. In this context, it is relevant to identify those users that are the most similar to the Target user in order to require them suitable suggestions. However, particularly when we deal with video contents for e-Learning, it should be appropriate also to consider (i) recommendations coming from those students resulted the most effective in Suggesting video and (ii) the effects of the device currently exploited. To address such issues in a multimedia scenario, we propose a multi-agent trust based recommender architecture, called ELSA, appositely conceived to this aim. Some preliminary performed simulations permitted to evaluate our proposal with respect to the other considered agent- based RSs.

An Agent-based Architecture to Recommend Educational Video / Rosaci, D; Sarne', G. - 1260:(2014), pp. 1-6. (Intervento presentato al convegno 15th Workshop “from Objects to Agents” (WOA-2014) tenutosi a Catania (Italy) nel 25-26 September 2014).

An Agent-based Architecture to Recommend Educational Video

ROSACI D;SARNE' G
2014-01-01

Abstract

Agent-based recommender systems are tools able to assist users’ choices with suggestions coming closest to their orientations. In this context, it is relevant to identify those users that are the most similar to the Target user in order to require them suitable suggestions. However, particularly when we deal with video contents for e-Learning, it should be appropriate also to consider (i) recommendations coming from those students resulted the most effective in Suggesting video and (ii) the effects of the device currently exploited. To address such issues in a multimedia scenario, we propose a multi-agent trust based recommender architecture, called ELSA, appositely conceived to this aim. Some preliminary performed simulations permitted to evaluate our proposal with respect to the other considered agent- based RSs.
2014
Device adaptivity; e-Learning; Multimedia; Recommender system; Trust system
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/12310
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