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Analysing user behaviour in recommender systems

Matsatsinis Nikolaos, Kleanthi Lakiotaki

Πλήρης Εγγραφή

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Περίληψη

Nowadays, recommender systems are considered to be a valuable tool for internet marketing. Multi-criteria user modelling methodologies have been successfully applied to increase recommender systems accuracy. However, modelling user behaviour can be hard and often misleading when only the overall preference rate is considered. Various multi-criteria recommendation algorithms have been proposed that try to achieve high recommendation scores, but the gap from research ideas to real life applications remain large. Hence, studies concerning the understanding and interpretation of theoretical results together with direct application in real user data will improve and establish multi-criteria user profiling techniques as an important tool for recommender systems. In this direction, we analyse movie user profiles as a result of a multi-criteria recommendation methodology, applied to real user data, in order to reveal any hidden aspect of user behaviour that would eventually improve current system’s performance

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