HiCORE: Enhancing consumer intent prediction via hybrid attention and contrastive learning in sequential recommendation

Abstract

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.