GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Intelligent Adaptive Information Agents
Journal of Intelligent Information Systems - Special issue: adaptive intelligent agents
A hybrid user model for news story classification
UM '99 Proceedings of the seventh international conference on User modeling
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Product recommendation with interactive query management and twofold similarity
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Case-based recommender systems: a unifying view
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
Recommending in Inclusive Lifelong Learning Scenarios: Identifying and Managing Runtime Situations
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Advanced technologies in e-tourism
ACS'09 Proceedings of the 9th WSEAS international conference on Applied computer science
| Hi-index | 0.00 |
This thesis investigates a way of using knowledge in dynamic and distributed domains for supporting recommendation, keeping the consistence of the decision knowledge that change over time. We propose the use of a multiagent knowledge-based recommender approach capable of dealing with distributed expert knowledge in order to support travel agents in recommending tourism packages. Agents work as experts cooperating and communicating to each other in the recommendation process. Each agent has a truth maintenance system (TMS) component that helps the agents to keep the integrity of their knowledge bases.