Multi-Criteria User Modeling in Recommender Systems
Tsoukiàs, Alexis; Matsatsinis, Nikolaos; Lakiotaki, Kleanthi (2011), Multi-Criteria User Modeling in Recommender Systems, IEEE Intelligent Systems, 26, 2, p. 64-76. http://dx.doi.org/10.1109/MIS.2011.33
TypeArticle accepté pour publication ou publié
Journal nameIEEE Intelligent Systems
MetadataShow full item record
Abstract (EN)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.
Subjects / KeywordsPreference Modeling; User Modeling and Clustering; Recommender Systems; Multiple Criteria Decision Analysis; Disaggregation – Aggregation approach; Collaborative Filtering
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