On the role of time and sessions in diversifying recommendation results

Intent-Aware approaches to diversification have been proposed in the last years to provide the user with a list of recommendations covering different aspects of her behavior. In this paper, we present two diversification methods taking into account temporal aspects of the user profile: in the first one, in order to emphasize the importance of more recent items, we adopt a temporal decay function while in the second one we take into account temporal sessions. The two proposed methods have been implemented as temporal variants of the well-known xQuAD framework and tested against the Netix 100M dataset.