HSUGA: LLM-Enhanced Recommendation with Hierarchical Semantic Understanding and Group-Aware Alignment

TL;DR

HSUGA enhances LLM-based recommendation by improving user embedding extraction through hierarchical understanding and by customizing semantic utilization based on user activity, leading to better recommendation accuracy.

Contribution

Introduces HSUGA, a novel framework with hierarchical semantic understanding and group-aware alignment to address limitations in LLM-enhanced recommendations.

Findings

HSUGA outperforms existing methods on three benchmark datasets.

Hierarchical semantic understanding improves user embedding reliability.

Group-aware alignment adapts semantic utilization to user activity levels.

Abstract

Large language model (LLM)-enhanced sequential recommendation typically aims to improve two core components: user semantic embedding extraction and utilization. Despite promising results, existing methods still have two limitations: 1) In the extraction stage, most methods directly input long interaction sequence fragments into LLM for preference summarization. However, excessively long sequences increase inference difficulty, making it challenging to reliably infer accurate user embeddings. 2) In the utilization stage, most methods employ the same semantic embedding utilization strategy for all users, neglecting the differences caused by user activity levels, leading to suboptimal performance. To address these issues, we propose HSUGA, which introduces a simple yet effective plugin for each of the two core components: Hierarchical Semantic Understanding (HSU) and Group-Aware Alignment (GAA). HSU performs a staged two-phase preference mining and models preference evolution through constrained editing operations, thereby improving the reliability of user semantic extraction. GAA adjusts the intensity of semantic utilization based on user activity levels, providing weaker alignment for active users and stronger guidance for users with sparse historical data. Finally, extensive experiments on three benchmark datasets demonstrate the effectiveness and compatibility of HSUGA.