  In this paper, we propose a unified framework and an algorithm for the
problem of group recommendation where a fixed number of items or alternatives
can be recommended to a group of users. The problem of group recommendation
arises naturally in many real world contexts, and is closely related to the
budgeted social choice problem studied in economics. We frame the group
recommendation problem as choosing a subgraph with the largest group consensus
score in a completely connected graph defined over the item affinity matrix. We
propose a fast greedy algorithm with strong theoretical guarantees, and show
that the proposed algorithm compares favorably to the state-of-the-art group
recommendation algorithms according to commonly used relevance and coverage
performance measures on benchmark dataset.
