Time-Aware Item Weighting for the Next Basket Recommendations

Romanov A.; Lashinin O.; M. Ananyeva; Kolesnikov S.

doi:10.1145/3604915.3608859

Publications

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Time-Aware Item Weighting for the Next Basket Recommendations

P. 985–992.

Romanov A., Lashinin O., Ananyeva M., Kolesnikov S.

In this paper we study the next basket recommendation problem. Recent methods use different approaches to achieve better perfor- mance. 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 Weight- ing (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.

Keywords: Hawkes processes next-basket recommendation repeat consumption

In book

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

Association for Computing Machinery (ACM), 2023.

Mutually exciting point processes with latency

Volkov V., Potiron Y., Journal of the American Statistical Association 2025 P. 1–28

A novel statistical approach to estimating latency, defined as the time it takes to learn about anevent and generate response to this event, is proposed. Our approach only requires amultidimensional point process describing event times, which circumvents the use of moredetailed datasets which may not even be available. We consider the class of parametric Hawkesmodels ...

Added: July 3, 2025

Time-Dependent Next-Basket Recommendations

Naumov S., Ananyeva M., Lashinin O. et al., , in: Advances in Information Retrieval. 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2–6, 2023, Proceedings, Part II.: Springer, 2023. P. 502–511.

here 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 ...

Added: June 14, 2023

Next-Basket Recommendation Constrained by Total Cost

Лашинин О. А., Ananyeva M., CEUR Workshop Proceedings 2022 P. 1–4

The next-basket recommendations are a well-studied problem in the academic field. As a rule, we want to predict top-K goods that might be added to the last known basket of the user based on the previous purchase history. However, the existing studies do not take into account that each user can be limited by the ...

Added: June 14, 2023

Hawkes processes for forecasting currency crashes: Evidence from Russia

Egorova L., Климюк И. Ю., , in: Procedia Computer ScienceVol. 122: 5th International Conference on Information Technology and Quantitative Management, ITQM 2017.: Elsevier, 2017. P. 1182–1188.

We consider models for predicting shocks in a foreign exchange market that take into account the endogenous nature of such crashes on the basis of the Hawkes processes. The intensity of the Hawkes processes depends on previous events that allow modeling the clustering effect and self-exciting behavior of returns after the crash. The models were ...

Added: January 15, 2018