Recommending New Items to Ephemeral Groups Using Contextual User Influence

Abstract

Group recommender systems help groups of users in finding appropriate items to be enjoyed together. Lots of activities, like watching TV or going to the restaurant, are intrinsically group-based, thus making the group recommendation problem very relevant. In this paper we study ephemeral groups, i.e., groups where the members might be together for the first time. Recent approaches have tackled this issue introducing complex models to be learned offline, making them unable to deal with new items; on the contrary, we propose a group recommender able to manage new items too. In more detail, our technique determines the preference of a group for an item by combining the individual preferences of the group members on the basis of their contextual influence, where the contextual influence represents the ability of an individual, in a given situation, to direct the group's decision. We conducted an extensive experimental evaluation on a TV dataset containing a log of viewings performed by real groups, showing how our approach outperforms the comparable techniques from the literature.