HiCORE: Enhancing consumer intent prediction via hybrid attention and contrastive learning in sequential recommendation Jin, Keyan Blanco Encomienda, Francisco Javier Sequential recommendation Consumer intent prediction Contrastive learning Accurately predicting consumer intent from sequential interactions is essential for enhancing recommendation accuracy and driving effective marketing strategies. In this paper, we introduce HiCORE, a Transformer-based sequential recommendation model integrating Layer-wise Hybrideak Attention, Rotary Positional Embeddings (RoPE), and a Contrastive Self-Supervised Learning (SSL) objective. HiCORE effectively captures both global consumer preferences and local contextual interests, explicitly encodes relative positional information, and ensures robust sequence-level representations, addressing common limitations such as data sparsity and noise. Extensive experiments on three real-world datasets demonstrate that HiCORE significantly outperforms state-of-the-art methods in recommendation accuracy metrics. Moreover, HiCORE’s enhanced capability in consumer intent prediction provides valuable insights for consumer segmentation and targeted marketing, bridging advanced computational methods and practical business applications. 2026-01-08T11:46:06Z 2026-01-08T11:46:06Z 2026-04-05 journal article Jin, K., & Blanco-Encomienda, F. J. (2026). HiCORE: Enhancing consumer intent prediction via hybrid attention and contrastive learning in sequential recommendation. Expert Systems With Applications, 305(130860), 130860. https://doi.org/10.1016/j.eswa.2025.130860 https://hdl.handle.net/10481/109323 10.1016/j.eswa.2025.130860 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Elsevier