Time-Aware Item Weighting for the Next Basket Recommendations

TL;DR

This paper introduces TAIW, a novel time-aware method for next basket recommendation that leverages timestamps and intervals, demonstrating superior performance over existing approaches through extensive experiments.

Contribution

The paper proposes TAIW, the first method to incorporate time information explicitly into item weighting for next basket recommendation.

Findings

TAIW outperforms state-of-the-art baselines in experiments.

Time information improves recommendation accuracy.

Ablation and case studies validate the effectiveness of TAIW.

Abstract

In this paper we study the next basket recommendation problem. Recent methods use different approaches to achieve better performance. However, many of them do not use information about the time of prediction and time intervals between baskets. To fill this gap, we propose a novel method, Time-Aware Item-based Weighting (TAIW), which takes timestamps and intervals into account. We provide experiments on three real-world datasets, and TAIW outperforms well-tuned state-of-the-art baselines for next-basket recommendations. In addition, we show the results of an ablation study and a case study of a few items.