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dc.contributor.authorNguyen TN
dc.contributor.authorRicci F
dc.description.abstractA major challenge for conversational group recommender systems is how to properly exploit the user's preferences induced by the interactions between group members, which may deviate from the user's long-term ones. We argue that the relative importance of the long-term and group-induced preferences should vary according to the specific group settings. To support this claim, we compare alternative ways of combining these two types of user's preferences, under three diverse settings that may influence users' behavior. Our experimental study shows how a combination scheme that weighs more the long-term preferences can better serve the group when the group discussion has no impact on group members' preferences, but when the group context pushes members to have either more or less similar preferences, then users benefit from a recommender that weighs more the group-induced preferences. en_US
dc.relation33rd Annual ACM Symposium on Applied Computing (SAC 2018) ; Pau : 9.4.2018 - 13.4.2018
dc.titleSituation-dependent combination of long-term and session-based preferences in group recommendations: an experimental analysisen_US
dc.typeBook chapteren_US
dc.publication.titleProceedings of the 33rd Annual ACM Symposium on Applied Computing 2018

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