[2604.03688] Fusion and Alignment Enhancement with Large Language Models for Tail-item Sequential Recommendation

Computer Science > Information Retrieval arXiv:2604.03688 (cs) [Submitted on 4 Apr 2026] Title:Fusion and Alignment Enhancement with Large Language Models for Tail-item Sequential Recommendation Authors:Zhifu Wei, Yizhou Dang, Guibing Guo, Chuang Zhao, Zhu Sun View a PDF of the paper titled Fusion and Alignment Enhancement with Large Language Models for Tail-item Sequential Recommendation, by Zhifu Wei and 4 other authors View PDF HTML (experimental) 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. Despite previous efforts to leverage LLM-derived embeddings for enriching tail items, they still face the following limitations: 1) They struggle to effectively fuse collaborative signals with semantic knowledge, leading to suboptimal item embedding quality. 2) Existing methods overlook the structural inconsistency between the ID and LLM embedding spaces, causing conflicting signals that degrade recommendation accuracy. In this work, we propose a Fusion and Alignment Enhancement framework with LLMs for Tail-item Sequential Recommendation (FAERec), which improves item representations...