Demonstrating the Equivalence of List Based and Aggregate Metrics to Measure the Diversity of Recommendations (Student Abstract)

Authors

  • Maurizio Ferrari Dacrema Politecnico di Milano, Italy

Keywords:

Recommender Systems, Beyond Accuracy, Diversity, Evaluation Metric, Herfindahl Index, Hamming Diversity, Mean Inter-List Diversity

Abstract

The evaluation of recommender systems is frequently focused on accuracy metrics, but this is only part of the picture. The diversity of recommendations is another important dimension that has received renewed interest in recent years. It is known that accuracy and diversity can be conflicting goals and finding appropriate ways to combine them is still an open research question. Several ways have been proposed to measure the diversity of recommendations and to include its optimization in the loss function used to train the model. Methods optimizing list based diversity suffer from two drawbacks: the high computational cost of the loss function and the lack of an efficient way to optimize them. In this paper we show the equivalence of the list based diversity metrics Hamming and Mean Inter-List diversity to the aggregate diversity metric measured with the Herfindahl index, providing a formulation that allows to compute and optimize them easily.

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Published

2021-05-18

How to Cite

Ferrari Dacrema, M. (2021). Demonstrating the Equivalence of List Based and Aggregate Metrics to Measure the Diversity of Recommendations (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15779-15780. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17886

Issue

Section

AAAI Student Abstract and Poster Program