Auteur
Tsoukiàs, Alexis
Matsatsinis, Nikolaos
Lakiotaki, Kleanthi
Type
Article accepté pour publication ou publié
Résumé en anglais
Recommender systems are software applications that attempt to reduce information overload. Their
goal is to recommend items of interest to the end users based on their preferences. To achieve that,
most Recommender Systems exploit the Collaborative Filtering approach. In parallel, Multiple
Criteria Decision Analysis (MCDA) is a well established field of Decision Science that aims at
analyzing and modeling decision maker’s value system, in order to support him/her in the decision
making process. In this work, a hybrid framework that incorporates techniques from the field of
MCDA, together with the Collaborative Filtering approach, is analyzed. The proposed methodology
improves the performance of simple Multi-rating Recommender Systems as a result of two main
causes; the creation of groups of user profiles prior to the application of Collaborative Filtering
algorithm and the fact that these profiles are the result of a user modeling process, which is based
on individual user’s value system and exploits Multiple Criteria Decision Analysis techniques.
Experiments in real user data prove the aforementioned statement.