A Critical Study on Data Leakage in Recommender System Offline Evaluation

TL;DR

This paper critically examines data leakage in offline recommender system evaluation, demonstrating its impact on model performance and proposing a timeline-based evaluation scheme for more realistic assessments.

Contribution

It provides a comprehensive analysis of data leakage causes, effects on model evaluation, and introduces a timeline scheme to improve offline recommendation assessment.

Findings

Data leakage causes models to recommend future items not available at test time.

Data leakage affects the accuracy and relative performance of recommender models.

A timeline scheme for evaluation offers a more realistic assessment of recommender systems.

Abstract

Recommender models are hard to evaluate, particularly under offline setting. In this paper, we provide a comprehensive and critical analysis of the data leakage issue in recommender system offline evaluation. Data leakage is caused by not observing global timeline in evaluating recommenders, e.g., train/test data split does not follow global timeline. As a result, a model learns from the user-item interactions that are not expected to be available at prediction time. We first show the temporal dynamics of user-item interactions along global timeline, then explain why data leakage exists for collaborative filtering models. Through carefully designed experiments, we show that all models indeed recommend future items that are not available at the time point of a test instance, as the result of data leakage. The experiments are conducted with four widely used baseline models - BPR, NeuMF, SASRec, and LightGCN, on four popular offline datasets - MovieLens-25M, Yelp, Amazon-music, and Amazon-electronic, adopting leave-last-one-out data split. We further show that data leakage does impact models' recommendation accuracy. Their relative performance orders thus become unpredictable with different amount of leaked future data in training. To evaluate recommendation systems in a realistic manner in offline setting, we propose a timeline scheme, which calls for a revisit of the recommendation model design.