Fusion and Alignment Enhancement with Large Language Models for Tail-item Sequential Recommendation
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 qua...
| Authors | Zhifu Wei, Yizhou Dang, Guibing Guo et al. |
|---|---|
| Primary category | cs.IR |
| Published | 4 April 2026 |
| Last updated at source | 4 April 2026 |
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