Optimal Greedy Diversity for Recommendation
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.
Cite
Text
Ashkan et al. "Optimal Greedy Diversity for Recommendation." International Joint Conference on Artificial Intelligence, 2015.Markdown
[Ashkan et al. "Optimal Greedy Diversity for Recommendation." International Joint Conference on Artificial Intelligence, 2015.](https://mlanthology.org/ijcai/2015/ashkan2015ijcai-optimal/)BibTeX
@inproceedings{ashkan2015ijcai-optimal,
title = {{Optimal Greedy Diversity for Recommendation}},
author = {Ashkan, Azin and Kveton, Branislav and Berkovsky, Shlomo and Wen, Zheng},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2015},
pages = {1742-1748},
url = {https://mlanthology.org/ijcai/2015/ashkan2015ijcai-optimal/}
}