Title: Optimal greedy diversity for recommendation

Authors: A. Ashkan, B. Kveton, S. Berkovsky, and Z. Wen


The need for diversification manifests in various recommendation use cases. In this work, we pro- pose a novel approach to diversifying a list of rec- ommended 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 eval- uate our approach in an offline analysis, which in- corporates a number of baselines and metrics, and in two online user studies. In all the experiments, our method outperforms the baseline methods.

Full Text: [PDF]

Accessibility at Yale   Inference, Information, and Decision Group at Yale