Exploring Data Splitting Strategies for the Evaluation of Recommendation Models

Abstract

Effective methodologies for evaluating recommender systems are critical, so that different systems can be compared in a sound manner. A commonly overlooked aspect of evaluating recommender systems is the selection of the data splitting strategy. In this paper, we both show that there is no standard splitting strategy and that the selection of splitting strategy can have a strong impact on the ranking of recommender systems during evaluation. In particular, we perform experiments comparing three common data splitting strategies, examining their impact over seven state-of-the-art recommendation models on two datasets. Our results demonstrate that the splitting strategy employed is an important confounding variable that can markedly alter the ranking of recommender systems, making much of the currently published literature non-comparable, even when the same datasets and metrics are used.