---
title: "Optimal Greedy Diversity for Recommendation"
authors:
  - "Azin Ashkan"
  - "Branislav Kveton"
  - "Shlomo Berkovsky"
  - "Zheng Wen"
doi: ""
url: "https://ijcai.org/proceedings/2015/248"
pdf: "https://ijcai.org/proceedings/2015/0248.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "1742-1747"
track: "Main Track"
keywords:
---

# Optimal Greedy Diversity for Recommendation

Azin Ashkan, Branislav Kveton, Shlomo Berkovsky, Zheng Wen

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, 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.