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.
|Titolo:||An Agent-based Architecture to Recommend Educational Video|
|Data di pubblicazione:||2014|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|