Bridging Offline-Online Evaluation with a Time-dependent and Popularity Bias-free Offline Metric for Recommenders

TL;DR

This paper proposes a new offline evaluation metric for recommender systems that accounts for time and popularity biases, improving the correlation with online performance.

Contribution

It introduces a time-dependent and popularity bias-free offline metric that better predicts real-world online recommendation effectiveness.

Findings

Penalizing popular items enhances evaluation accuracy.

Considering transaction timing improves model selection.

The new metric correlates better with online performance.

Abstract

The evaluation of recommendation systems is a complex task. The offline and online evaluation metrics for recommender systems are ambiguous in their true objectives. The majority of recently published papers benchmark their methods using ill-posed offline evaluation methodology that often fails to predict true online performance. Because of this, the impact that academic research has on the industry is reduced. The aim of our research is to investigate and compare the online performance of offline evaluation metrics. We show that penalizing popular items and considering the time of transactions during the evaluation significantly improves our ability to choose the best recommendation model for a live recommender system. Our results, averaged over five large-size real-world live data procured from recommenders, aim to help the academic community to understand better offline evaluation and optimization criteria that are more relevant for real applications of recommender systems.