Recommending Unanimously Preferred Items to Groups

Due to the pervasiveness of group activities in people’s daily life, group recommendation has attracted a massive research effort in both industry and academia. A fundamental challenge in group recommendation is how to aggregate the preferences of group members to select a set of items maximizing the overall satisfaction of the group; this is the focus of this paper. Specifically, we introduce a dual adjustment aggregation score, which measures the relevance of an item to a group. We then propose a recommendation scheme, termed 𝑘 -dual adjustment unanimous skyline, that seeks to retrieve the 𝑘 items with the highest score, while discarding items that are unanimously considered inap-propriate. Furthermore, we design and develop algorithms for computing the 𝑘 -dual adjustment unanimous skyline efficiently. Finally, we demonstrate both the retrieval effectiveness and the efficiency of our approach through an extensive experimental evaluation on real datasets.