Time-Aware Item Weighting for the Next Basket Recommendations
A Romanov, O Lashinin, M Ananyeva… - Proceedings of the 17th …, 2023 - dl.acm.org
A Romanov, O Lashinin, M Ananyeva, S Kolesnikov
Proceedings of the 17th ACM Conference on Recommender Systems, 2023•dl.acm.orgIn 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 …
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 …
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.
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