Proceedings Abstracts of the Twenty-Fourth International Joint Conference on Artificial Intelligence

Optimal Greedy Diversity for Recommendation / 1742
Azin Ashkan, Branislav Kveton, Shlomo Berkovsky, Zheng Wen

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.