Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

9-2025

Abstract

Next-basket recommendation aims to predict the (sets of) items that a user is most likely to purchase during their next visit, capturing both short-term sequential patterns and long-term user preferences. However, effectively modeling these dynamics remains a challenge for traditional methods, which often struggle with interpretability and computational efficiency, particularly when dealing with intricate temporal dependencies and inter-item relationships. In this paper, we propose ReALM, a Recurrent Autoregressive Linear Model that explicitly captures temporal item-to-item dependencies across multiple time steps. By leveraging a recurrent loss function and a closed-form optimization solution, our approach offers both interpretability and scalability while maintaining competitive accuracy. Experimental results on real-world datasets demonstrate that ReALM outperforms several state-of-the-art baselines in both recommendation quality and efficiency, offering a robust and interpretable solution for modern personalization systems.

Keywords

Next-basket Recommendation, Sparse Approximation, Scalability

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

RecSys '25: Proceedings of the Nineteenth ACM Conference on Recommender Systems, Prague, Czech Republic, September 22-26

First Page

1273

Last Page

1278

Identifier

10.1145/3705328.3759313

Publisher

ACM

City or Country

New York

Additional URL

https://dl.acm.org/doi/10.1145/3705328.3759313

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