Time-Dependent Next-Basket Recommendations

There are various real-world applications for next-basket recommender systems. One of them is guiding a website user who wants to buy anything toward a collection of items. Recent works demonstrate that methods based on the frequency of prior purchases outperform other deep learning algorithms in terms of performance. These techniques, however, do not consider timestamps and time intervals between interactions. Additionally, they often miss the time period that passes between the last known basket and the prediction time. In this study, we explore whether such knowledge could improve current state-of-the-art next-basket recommender systems. Our results on three real-world datasets show how such enhancement may increase prediction quality. These findings might pave the way for important research directions in the field of next-basket recommendations.

Automated summary

This study explores the potential improvement of next-basket recommender systems by incorporating time information. Experimental results on three real-world datasets demonstrate that adding time information can enhance prediction quality, paving the way for further research directions in the field of next-basket recommendations.