IMFuse: Instance-Aware Multi-Layer Fusion for LLM-Enhanced Sequential Recommendation
Yuheng Zheng, Yu Cui, Bin Wu, Jian Zhang, Ye Feng, Can Wang, Jiawei Chen
原文摘要
Leveraging large language models (LLMs) to enrich item embeddings with textual semantics has become a prevailing paradigm for enhancing sequential recommendation (SR). However, existing methods typically rely solely on the final-layer LLM embeddings, leaving the semantics encoded in other layers largely unexploited. Through an empirical analysis, we uncover three key findings: 1) item embeddings from deeper LLM layers suffer from increasingly severe spectral collapse, which reduces their information richness and hinders the optimization; 2) intermediate LLM layers encode complementary item semantics beyond the final layer; 3) different items exhibit heterogeneous layer-wise semantic patterns. These findings suggest that the final layer alone is insufficient and that multi-layer LLM semantics should be exploited adaptively for each item. To this end, we propose IMFuse, a novel Instance-Aware MultiLayer Fusion strategy for LLM-enhanced SR. IMFuse adaptively aggregates embeddings from all LLM layers with learnable layerwise fusion weights, and further customizes the fusion for each item via an instance-aware Mixture-of-Experts (MoE) module. As a plugand-play strategy, IMFuse can be seamlessly integrated into various SR backbones and LLM-based enhancement methods. Extensive experiments on four real-world datasets demonstrate that IMFuse consistently improves state-of-the-art methods, achieving an average relative improvement of 6.72% with limited computational overhead.
cs.IR