SAGA: A Submodular Greedy Algorithm for Group Recommendation

Puthiya Parambath, S. A. , Vijayakumar, N. and Chawla, S. (2018) SAGA: A Submodular Greedy Algorithm for Group Recommendation. In: Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, LA, USA, 2-7 Feb 2018, pp. 3900-3908.

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In this paper, we propose a unified framework and an algo- rithm 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 com- pletely 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 algo- rithms according to commonly used relevance and coverage performance measures on benchmark dataset.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Puthiya Parambath, Dr Sham
Authors: Puthiya Parambath, S. A., Vijayakumar, N., and Chawla, S.
College/School:College of Science and Engineering > School of Computing Science
Publisher:AAAI Press
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