SAGA: A Submodular Greedy Algorithm For Group Recommendation
This addresses the problem of recommending items to groups of users, which is incremental as it builds on existing group recommendation algorithms.
The paper tackles the group recommendation problem by proposing a fast greedy algorithm that selects items to maximize group consensus, showing favorable performance compared to state-of-the-art methods on benchmark datasets.
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.