Multicriteria User Modeling in Recommender Systems

Recommender systems are software applications that attempt to reduce information overload by recommending items of interest to end users based on their preferences, possibly giving movie, song, or other product suggestions. A hybrid recommender systems framework creates user profile groups before applying a collaborative filtering algorithm by incorporating techniques from the multiple-criteria decision-analysis (MCDA) field. Multiple-criteria decision analysis (MCDA) is a well-established field of decision science that aims at analyzing and modeling decision makers' value systems to support them in the decision-making process. The key to more effective personalization services is the ability to develop a system able to understand not only what people like, but why they like it. Generally, a clustering algorithm divides the original data set into disjointed groups.