Cartes de communautés pour l'adaptation interactive de profils dans un système de filtrage d'information

Today, to deal with information overload, more and more efficient tools are needed for information retrieval. In order to get relevant information, users can rely on recommender systems that are based on user profiles. Nevertheless, a change of user interest is not always well accounted for, because of the passive role of users in the majority of existing systems. This paper presents the possibility of using "community maps" for the task of interactive profile adaptation in recommender systems. In order to produce these "community maps", we adopt a process based on a 2D positioning and a classical clustering algorithm. These maps account for two different criteria for user proximity.

[1]  Naren Ramakrishnan,et al.  Studying Recommendation Algorithms by Graph Analysis , 2003, Journal of Intelligent Information Systems.

[2]  Baldo Faieta,et al.  Diversity and adaptation in populations of clustering ants , 1994 .

[3]  Josep Lluís de la Rosa i Esteva,et al.  A Taxonomy of Recommender Agents on the Internet , 2003, Artificial Intelligence Review.

[4]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[5]  John Riedl,et al.  An algorithmic framework for performing collaborative filtering , 1999, SIGIR '99.

[6]  Marco Dorigo,et al.  Ant algorithms and stigmergy , 2000, Future Gener. Comput. Syst..

[7]  Pavel Berkhin,et al.  A Survey of Clustering Data Mining Techniques , 2006, Grouping Multidimensional Data.

[8]  Catherine Berrut,et al.  COCoFil: une plateforme de filtrage collaboratif orientée vers la communauté , 2004, CORIA.

[9]  Jean-Louis Deneubourg,et al.  The dynamics of collective sorting robot-like ants and ant-like robots , 1991 .

[10]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[11]  Denyse Baillargeon,et al.  Bibliographie , 1929 .

[12]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[13]  Marco Dorigo,et al.  On the Performance of Ant-based Clustering , 2003, HIS.

[14]  Alfred Kobsa.  User Modeling and User-Adapted Interaction , 2005, User Modeling and User-Adapted Interaction.

[15]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[16]  John Riedl,et al.  Explaining collaborative filtering recommendations , 2000, CSCW '00.

[17]  Edward A. Fox,et al.  A Connection-Centric Survey of Recommender Systems Research , 2002, ArXiv.