We are proud to have the following paper accepted by a team of IRLab-ers by ACM Transactions on Information Systems:

• A Next Basket Recommendation Reality Check by Ming Li, Sami Jullien, Mozhdeh Ariannezhad, and Maarten de Rijke
• The goal of a next basket recommendation 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 next basket recommendation (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 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.