| 19th
INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE | |
Multi-Agent Information Retrieval and Recommender SystemsIJCAI-2005 Workshop
|
In the first domain, there is a large community that develops architectures, language models and techniques for interaction and communication between agents. An important focus for the information retrieval domain has been the development of techniques for coping with the heterogeneous and dynamic information space represented by the World-Wide Web and the impact of new retrieval devices and interfaces. However, information retrieval systems do not traditionally care about the individual searcher, preferring instead to focus on the development of global retrieval techniques rather than those adapted for the needs of the individual. Consequently, recommender systems research has focused on the interaction between information retrieval and user modelling in order to provide a more personalized and proactive retrieval experience and to help users choose between retrieval alternatives and to refine their queries.
In the future, one can easily imagine a virtual organization of agents with specific tasks such as profile acquisition, web searching and recommendation making. These agents cooperate and negotiate in order to satisfy their users while they put their precious human cycles to a better use. The agents interact to complement their partial solutions or to solve conflicts that may arise. The integration of agent technology will help create and maintain this virtual organization.
Rapidly evolving computer technology, coupled with the exponential growth of the services and information available on the Internet, have already brought us to the point where hundreds of millions of people should have fast, pervasive access to phenomenal amounts of information. The challenge of complex environments is therefore obvious: software is expected to do more in more situations, there is a variety of users, there is a variety of interactions and there is a variety of resources and goals. To cope with such environments, the promise of Multi-Agent Systems is becoming highly attractive.
This workshop will present an opportunity for researchers working in these
three domains to get together and share recent developments and techniques in
order to identify the critical problems and the most promising research avenues.
In particular the workshop will emphasize the interaction between these
different research areas, with a view to establishing a framework for more
flexible multi-agent retrieval and recommendation solutions.
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Application areas include, but are not limited to:
| Time | Event (David Hume Tower Conference Room) |
|---|---|
| 9:00-9:15 | Welcome |
| 9:15-9:40 | David McSherry Conversational CBR in multi-agent recommendation |
| 9:40-10:05 | Tong Zheng and Randy Goebel An NCM-based framework for navigation recommender systems |
| 10:05-10:30 | Conor Hayes, Paolo Avesani and Marco Cova Language games: Learning shared concepts among distributed information agents |
| 10:30-11:00 | Coffee Break |
| 11:00-11:25 | Fabiana Lorenzi, Daniela Scherer dos Santos and Ana L. C. Bazzan Negotiation for task allocation among agents in case-base recommender systems: A swarm-intelligence approach |
| 11:25-11:50 | Wolfgang Woerndl and Georg Groh A proposal for an agent-based architecture for context-aware personalization in the Semantic Web |
| 11:50-12:15 | Alexander Birukov, Enrico Blanzieri and Paolo Giorgini Implicit: A recommender system that uses implicit knowledge to produce suggestions |
| 12:15-13:45 | Lunch |
| 13:45-14:10 | Robin Burke and Bamshad Mobasher Trust and bias in multi-agent recommender systems |
| 14:10-14:35 | Elhadi Shakshuki, André Trudel, Yiqing Xu and Boya Li A probabilistic temporal interval algebra based multi-agent scheduling system |
| 14:35-15:00 | Maria Salamó, Barry Smyth, Kevin McCarthy, James Reilly and Lorraine McGinty Reducing critiquing repetition in conversational recommendation |
| 15:00-15:25 | Coffee Break |
| 15:25-15:50 | Timothy A. Musgrove and Robin H. Walsh Utilizing multiple agents in dynamic product recommendations |
| 15:50-16:15 | William H. Hsu Relational graphical models for collaborative filtering and recommendation of computational workflow components |
| 16:15-16:40 | François Paradis, Jian-Yun Nie and Arman Tajarobi Discovery of business opportunities on the Internet with information extraction |
| 16:40-17:05 | Joel Pinho Lucas, Beatriz Wilges and Ricardo Azambuja Silveira Making use of FIPA multiagent architecture to develop animated pedagogical agents inside intelligent learning environments |
| 17:05-17:30 | Hugues Tremblay-Beaumont and Esma Aïmeur Feature combination in a recommender system using distributed items: The case of JukeBlog |
| 17:35-18:00 | Wrap-up |
For questions and suggestions, please contact Esma Aïmeur
Email: aimeur@iro.umontreal.ca
URL: http://www.iro.umontreal.ca/~aimeur
Phone: +1 (514) 343-6794
Fax: +1 (514) 343-5834
Université de Montréal
Département d'informatique et de recherche
opérationnelle
Pavillon André-Aisenstadt
C.P. 6128, Succ. Centre-Ville,
Montréal (QC)
H3C 3J7 Canada