Sudden Death: A New Way to Compare Recommendation Diversification

07/31/2019

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This paper describes problems with the current way we compare the diversity of different recommendation lists in offline experiments. We illustrate the problems with a case study. We propose the Sudden Death score as a new and better way of making these comparisons.

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Sudden Death: A New Way to Compare Recommendation Diversification

Study of a bias in the offline evaluation of a recommendation algorithm

Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network

A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels

FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems

DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation

An Evaluation-Focused Framework for Visualization Recommendation Algorithms

Offline Evaluation of Ranked Lists using Parametric Estimation of Propensities

Sudden Death: A New Way to Compare Recommendation Diversification

Related Research

Study of a bias in the offline evaluation of a recommendation algorithm

Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network

A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels

FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems

DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation

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