A Next Basket Recommendation Reality Check

The goal of a next basket recommendation (NBR) system is to recommend items for the next basket for a user, based on the sequence of their prior baskets. We examine whether the performance gains of the NBR methods reported in the literature hold up under a fair and comprehensive comparison. To clarify the mixed picture that emerges from our comparison, we provide a novel angle on the evaluation of next basket recommendation (NBR) methods, centered on the distinction between repetition and exploration: the next basket is typically composed of previously consumed items (i.e., repeat items) and new items (i.e., explore items). We propose a set of metrics that measure the repetition/exploration ratio and performance of NBR models. Using these new metrics, we provide a second analysis of state-of-the-art NBR models. The results help to clarify the extent of the actual progress achieved by existing NBR methods as well as the underlying reasons for any improvements that we observe. Overall, our work sheds light on the evaluation problem of NBR, provides a new evaluation protocol, and yields useful insights for the design of models for this task.

Automated summary

The study aims to investigate the performance of NBR methods in practical applications and proposes a new set of evaluation metrics to measure the performance of NBR models. By conducting experimental analysis on state-of-the-art NBR models, it reveals the actual progress and improvements of NBR methods in the recommendation process.