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

Researchers propose FAERec, a new framework that uses large language models to improve sequential recommendation systems for rarely-interacted (tail) items. The system addresses fusion and alignment challenges between collaborative signals and semantic knowledge to enhance recommendation accuracy.