Title: Analysing user behaviour in recommender systems

Authors: Kleanthi Lakiotaki; Nikolaos Matsatsinis

Addresses: Department Production and Management Engineering, Technical University of Crete, Kounoupidiana-Chania-Crete, GR73100, Greece. ' Department Production and Management Engineering, Technical University of Crete, Kounoupidiana-Chania-Crete, GR73100, Greece

Abstract: 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.

Keywords: user profile analysis; personalisation; recommender systems; marketing; multicriteria decision analysis; MCDA; cluster analysis; e-business; electronic business; internet marketing; user behaviour; modelling; recommendations.

DOI: 10.1504/IJEB.2012.048740

International Journal of Electronic Business, 2012 Vol.10 No.1, pp.1 - 19

Published online: 10 Apr 2015 *

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