A Systematical Evaluation for Next-Basket Recommendation Algorithms

TL;DR

This paper conducts a systematic empirical evaluation of various next-basket recommendation algorithms by applying them to the same datasets and settings, enabling fair comparison and analysis.

Contribution

It provides a unified evaluation framework for NBR algorithms, addressing inconsistencies in previous comparative studies.

Findings

Different NBR approaches show varying performance on the same datasets.

The evaluation framework enables fair and consistent comparison of NBR methods.

Insights into the strengths and weaknesses of representative NBR algorithms.

Abstract

Next basket recommender systems (NBRs) aim to recommend a user's next (shopping) basket of items via modeling the user's preferences towards items based on the user's purchase history, usually a sequence of historical baskets. Due to its wide applicability in the real-world E-commerce industry, the studies NBR have attracted increasing attention in recent years. NBRs have been widely studied and much progress has been achieved in this area with a variety of NBR approaches having been proposed. However, an important issue is that there is a lack of a systematic and unified evaluation over the various NBR approaches. Different studies often evaluate NBR approaches on different datasets, under different experimental settings, making it hard to fairly and effectively compare the performance of different NBR approaches. To bridge this gap, in this work, we conduct a systematical empirical study in NBR area. Specifically, we review the representative work in NBR and analyze their cons and pros. Then, we run the selected NBR algorithms on the same datasets, under the same experimental setting and evaluate their performances using the same measurements. This provides a unified framework to fairly compare different NBR approaches. We hope this study can provide a valuable reference for the future research in this vibrant area.