# SAGA: A Submodular Greedy Algorithm For Group Recommendation

arXiv: https://arxiv.org/abs/1712.09123 · HTML: https://mlsanity.com/paper/1712.09123/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2017.

## Built on by (most cited first, 3 of 3)

- [Balancing Utility and Fairness in Submodular Maximization (Technical Report)](https://mlsanity.com/paper/2211.00980/) (2022)
- [Linear Submodular Maximization with Bandit Feedback](https://mlsanity.com/paper/2407.02601/) (2024)
- [Fast Approximation Algorithm for Non-Monotone DR-submodular Maximization under Size Constraint](https://mlsanity.com/paper/2511.02254/) (2025)

## Abstract (arXiv)

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.