Pro-active Multi-Dimensional Recommender System using Multi-Agents

Pro-active Multi-Dimensional Recommender System using Multi-Agents

Multi-agent

multi-dimensional rating

pro-activity

recommender system

Recommender systems currently used in many applications, including tourism, tend to simply be reactive to user request. The recommender system proposed in this paper uses multi-agents and multi-dimensional contextual information to achieve proactive behavior. User profile and behavior get implicitly incorporated and subsequently updated in the system. The recommender system has been developed and applied to the tourism domain. It was tested and evaluated by relatively large set of real users The evaluation conducted shows that most of the users are satisfied with the functionality of the system and its ability to produce the recommendation adaptively and proactively taking into considerations different factors.

Al Tair, Hend

Zemerly, Mohamed Jamal

AL-Qutayri, Mahmoud

Leida, Marcello

International Journal of Interactive Mobile Technologies (iJIM); Vol. 6 No. 3 (2012); pp. 4-12

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2012-07-09

Copyright (c) 2017 Hend Al Tair, Mohamed Jamal Zemerly, Mahmoud AL-Qutayri, Marcello Leida

application/pdf

eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article