SAGA: A Submodular Greedy Algorithm For Group Recommendation

TL;DR

This paper introduces SAGA, a fast greedy algorithm for group recommendation that maximizes consensus scores, offering strong theoretical guarantees and improved performance over existing methods.

Contribution

The paper presents a unified submodular greedy algorithm for group recommendation with theoretical guarantees and competitive empirical performance.

Findings

The algorithm achieves higher relevance scores than baseline methods.

It provides better coverage in group recommendations.

Theoretical analysis confirms approximation guarantees.

Abstract

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.