Addressing the new user problem with a personality based user similarity measure
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The new user problem is a recurring problem in memory based collaborative recommender systems (MBCR). It occurs when a new user is added to the system and there are not enough information to make a good selection of the user's neighbours. As a consequence, the recommended items have poor correlation with the user's interests. We addressed the new user problem by observing the user similarity measure (USM). In this paper we present two novelties that address the new user problem : (i) the usage of a personality based USM to alleviate the new user problem and (ii) a method for establishing the boundary of the cold start period. We succesfully used a personality based USM that yielded significantly better recommender performance in the period where the new user problem occurs. Furthermore we presented a new methodology for assessing the boundary of the period where the new user problem occurs.