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For group recommendations, one objective is to recommend an ordered set of items, a top-N, to a group such that each individual recommendation is relevant for everyone. A common way to do this is to select items on which the group can agree, using so-called ‘aggregation strategies’. One weakness of these aggregation strategies is that they select items independently of each other. They therefore cannot guarantee properties such as fairness, that apply to the set of recommendations as a whole.

In this paper, we give a definition of fairness that ‘balances’ the relevance of the recommended items across the group members in a rank-sensitive way. Informally, an ordered set of recommended items is considered fair to a group if the relevance of the items in the top-N is balanced across the group members for each prefix of the top-N. In other words, the first item in the top-N should, as far as possible, balance the …