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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

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Type
Article accepté pour publication ou publié
Date
2011
Journal name
IEEE Intelligent Systems
Volume
26
Number
2
Publisher
IEEE
Pages
64-76
Publication identifier
http://dx.doi.org/10.1109/MIS.2011.33
Metadata
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Author(s)
Tsoukiàs, Alexis cc
Matsatsinis, Nikolaos
Lakiotaki, Kleanthi
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 / Keywords
Preference Modeling; User Modeling and Clustering; Recommender Systems; Multiple Criteria Decision Analysis; Disaggregation – Aggregation approach; Collaborative Filtering

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