Pro-active Multi-Dimensional Recommender System using Multi-Agents
DOI:
https://doi.org/10.3991/ijim.v6i3.2012Keywords:
Multi-agent, multi-dimensional rating, pro-activity, recommender systemAbstract
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.
Downloads
Published
How to Cite
Issue
Section
License
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution Licence (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
By submitting an article the author grants to this journal the non-exclusive right to publish it. The author retains the copyright and the publishing rights for his article without any restrictions.
This journal has been awarded the SPARC Europe Seal for Open Access Journals (What's this?)