Optimal Greedy Diversity for Recommendation
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 1742-1747.
Abstract
The need for diversification manifests in various recommendation use cases. In this work, we propose a novel approach to diversifying a list of recommended items, which maximizes the utility of the items subject to the increase in their diversity. From a technical perspective, the problem can be viewed as maximization of a modular function on the polytope of a submodular function, which can be solved optimally by a greedy method. We evaluate our approach in an offline analysis, which incorporates a number of baselines and metrics, and in two online user studies. In all the experiments, our method outperforms the baseline methods.
@inproceedings{ijcai2015p248,
title = {Optimal Greedy Diversity for Recommendation},
author = {Ashkan, Azin and Kveton, Branislav and Berkovsky, Shlomo and Wen, Zheng},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {1742--1747},
year = {2015},
month = {7},
note = {Main Track},
url = {https://ijcai.org/proceedings/2015/248},
}
TY - CPAPER TI - Optimal Greedy Diversity for Recommendation AU - Ashkan, Azin AU - Kveton, Branislav AU - Berkovsky, Shlomo AU - Wen, Zheng T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 1742 EP - 1747 UR - https://ijcai.org/proceedings/2015/248 ER -Copied
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