Fusion and Alignment Enhancement with Large Language Models for Tail-item Sequential Recommendation

arXiv:2604.03688v1 Announce Type: cross Abstract: Sequential Recommendation (SR) learns user preferences from their historical interaction sequences and provides personalized suggestions. In real-world scenarios, most items exhibit sparse interactions, known as the tail-item problem. This issue limits the model's ability to accurately capture item transition patterns. To tackle this, large language models (LLMs) offer a promising solution by capturing semantic relationships between items. Despit