  Next Basket Recommender Systems (NBRs) function to recommend the subsequent
shopping baskets for users through the modeling of their preferences derived
from purchase history, typically manifested as a sequence of historical
baskets. Given their widespread applicability in the E-commerce industry,
investigations into NBRs have garnered increased attention in recent years.
Despite the proliferation of diverse NBR methodologies, a substantial challenge
lies in the absence of a systematic and unified evaluation framework across
these methodologies. Various studies frequently appraise NBR approaches using
disparate datasets and diverse experimental settings, impeding a fair and
effective comparative assessment of methodological performance. To bridge this
gap, this study undertakes a systematic empirical inquiry into NBRs, reviewing
seminal works within the domain and scrutinizing their respective merits and
drawbacks. Subsequently, we implement designated NBR algorithms on uniform
datasets, employing consistent experimental configurations, and assess their
performances via identical metrics. This methodological rigor establishes a
cohesive framework for the impartial evaluation of diverse NBR approaches. It
is anticipated that this study will furnish a robust foundation and serve as a
pivotal reference for forthcoming research endeavors in this dynamic field.

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