Fatemeh Fazaeefar, Paolo Speziali, Szymon Bobek, Grzegorz J. Nalepa, Diederik M. Roijers, Ann Nowe, Mehrdad Asadi
Standard collaborative filtering is efficient for large retail datasets but overlooks the temporal dynamics of customer behavior, while neural and complex models capture these dynamics at the cost of heavy computational demands. We propose a hybrid recommendation method that retains the efficiency of classical approaches while incorporating lightweight temporal modeling to capture implicit feedback and personalized cyclic purchasing patterns. The experiments verify that the proposed method achieves comparable performance to state-of-the-art methods, maintaining a linear computational overhead, and provides an interpretable temporal feature. In particular, our method consistently outperforms the frequency-based baseline across all metrics, achieving relative improvements ranging from 1.9\% to 3.0\%. Applied to a real-world retail use case with large-scale transactional data, the method demonstrates its practicality and effectiveness for personalized product recommendations in physical retail stores.
Fazaeefar, F, Speziali, P, Bobek, S, J. Nalepa, G, Roijers, DM, Nowe, A & Asadi, M 2026, Personalized Next-Basket Recommendation with Interpretable Cycle-Aware Purchase Modeling. in M Paszynski, AS Barnard & YJ Zhang (eds), Computational Science – ICCS 2026 Workshops: 26th International Conference, ICCS 2026, Hamburg, Germany, June 29 – July 1, 2026, Proceedings, Part IV. Lecture Notes in Computer Science, vol. 16789 LNCS, Springer, pp. 277-291, 26th International Conference on Computational Science, Hamburg, Germany, 29/06/26. https://doi.org/10.1007/978-3-032-29918-5_20
Fazaeefar, F., Speziali, P., Bobek, S., J. Nalepa, G., Roijers, D. M., Nowe, A., & Asadi, M. (2026). Personalized Next-Basket Recommendation with Interpretable Cycle-Aware Purchase Modeling. In M. Paszynski, A. S. Barnard, & Y. J. Zhang (Eds.), Computational Science – ICCS 2026 Workshops: 26th International Conference, ICCS 2026, Hamburg, Germany, June 29 – July 1, 2026, Proceedings, Part IV (pp. 277-291). (Lecture Notes in Computer Science; Vol. 16789 LNCS). Springer. https://doi.org/10.1007/978-3-032-29918-5_20
@inproceedings{8d37cd44a57c4eb5a0b5afa5457d5229,
title = "Personalized Next-Basket Recommendation with Interpretable Cycle-Aware Purchase Modeling",
abstract = "Standard collaborative filtering is efficient for large retail datasets but overlooks the temporal dynamics of customer behavior, while neural and complex models capture these dynamics at the cost of heavy computational demands. We propose a hybrid recommendation method that retains the efficiency of classical approaches while incorporating lightweight temporal modeling to capture implicit feedback and personalized cyclic purchasing patterns. The experiments verify that the proposed method achieves comparable performance to state-of-the-art methods, maintaining a linear computational overhead, and provides an interpretable temporal feature. In particular, our method consistently outperforms the frequency-based baseline across all metrics, achieving relative improvements ranging from 1.9\% to 3.0\%. Applied to a real-world retail use case with large-scale transactional data, the method demonstrates its practicality and effectiveness for personalized product recommendations in physical retail stores.",
author = "Fatemeh Fazaeefar and Paolo Speziali and Szymon Bobek and \{J. Nalepa\}, Grzegorz and Roijers, \{Diederik M.\} and Ann Nowe and Mehrdad Asadi",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; 26th International Conference on Computational Science, ICCS ; Conference date: 29-06-2026 Through 01-07-2026",
year = "2026",
month = jun,
day = "27",
doi = "10.1007/978-3-032-29918-5\_20",
language = "English",
isbn = "9783032299178",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "277--291",
editor = "Maciej Paszynski and Barnard, \{Amanda S.\} and Zhang, \{Yongjie Jessica\}",
booktitle = "Computational Science – ICCS 2026 Workshops",
url = "https://www.iccs-meeting.org/iccs2026/",
}