Intelligent art recommender

Abstract

In this paper we present a proof-of-concept art recommender that intelligently, through user clustering, suggests pieces of art to the user that match his or her profile. The prototype proposed implements an item clustering technique so as to tackle issues related to inaccurate outcomes as a result of user clustering. It utilises a hybrid of collaborative and contentbased recommendation approaches by employing such clustering and rule-mining techniques. An element of two interesting concepts, namely, serendipity and competition also form part of this work to give the prototype an added edge. Personal recommendations and a point awarding system combined in a mobile app bring together a challenging project and new interesting research values.