Optimal greedy diversity for recommendation

Azin Ashkan, Branislav Kveton, Shlomo Berkovsky, Zheng Wen

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

62 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence
EditorsMichael Wooldridge, Qiang Yang
PublisherAssociation for the Advancement of Artificial Intelligence
Number of pages7
ISBN (Electronic)9781577357384
Publication statusPublished - 2015
Externally publishedYes
Event24th International Joint Conference on Artificial Intelligence, IJCAI 2015 - Buenos Aires, Argentina
Duration: 25 Jul 201531 Jul 2015


Other24th International Joint Conference on Artificial Intelligence, IJCAI 2015
CityBuenos Aires


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