Multi-granularity preference enhancement with hierarchical feature extraction for session-based recommendations

Session-based recommendation predicts the next item a user will interact with based on their short-term session behavior, typically without long-term user profiles. Existing approaches often fail to capture the hierarchical nature of user preferences, leading to suboptimal personalization and limited recommendation accuracy. In this work, we argue that user preferences exhibit coarse-grained and fine-grained characteristics, and item features should be modeled accordingly across these two levels to capture users' preference signals more accurately. To this end, we propose a novel method, Multi-Granularity Preference Enhancement with Hierarchical Feature Extraction (MPEHFE), for session-based recommendation. MPEHFE explicitly captures semantic item relationships at each granularity and enhances fine-grained preference modeling through a differentiable architecture search mechanism. It also identifies interactions inconsistent with the user's general intent as noise, leveraging contrastive learning to reinforce the representation of coarse-grained preferences. Moreover, experiments on three real-world benchmark datasets demonstrate that MPEHFE consistently outperforms state-of-the-art baselines, achieving relative improvements of 3%-9% in P@20 and 11%-56% in MRR@20.