Towards Unified Metrics for Accuracy and Diversity for Recommender Systems

Recommender systems evaluation has evolved rapidly in recent years. However, for offline evaluation, accuracy is the de facto standard for assessing the superiority of one method over another, with most research comparisons focused on tasks ranging from rating prediction to ranking metrics for top-n recommendation. Simultaneously, recommendation diversity and novelty have become recognized as critical to users' perceived utility, with several new metrics recently proposed for evaluating these aspects of recommendation lists. Consequently, the accuracy-diversity dilemma frequently shows up as a choice to make when creating new recommendation algorithms. We propose a novel adaptation of a unified metric, derived from one commonly used for search system evaluation, to Recommender Systems. The proposed metric combines topical diversity and accuracy, and we show it to satisfy a set of desired properties that we formulate axiomatically. These axioms are defined as fundamental constraints that a good unified metric should always satisfy. Moreover, beyond the axiomatic analysis, we present an experimental evaluation of the metric with collaborative filtering data. Our analysis shows that the metric respects the desired theoretical constraints and behaves as expected when performing offline evaluation.

• Information Retrieval and the Web

• Responsible AI