基于大语言模型增强和协同信息融合的序列推荐方法

doi:10.16451/j.cnki.issn1003-6059.202510005

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Abstract Existing sequential recommendation methods fail to fully explore item attribute semantic information and suffer from a semantic space migration mismatch. These limitations result in inadequate recommendation capabilities of the existing methods for long-tail items. To address this issue, a method for sequential recommendation with large language model enhancement and collaborative information fusion（LLM-CFSR） is proposed in this paper. First, fine-grained semantic embeddings are generated with a large language model through attribute-level data augmentation and contrastive fine-tuning techniques to capture the deep semantic associations of long-tail items. Then, a dual-view modeling framework is designed to jointly model user preferences from both semantic and collaborative views. Finally, to promote deep interaction between semantic information and collaborative signals, a cross-attention mechanism is introduced to achieve multi-level information fusion across embedding layers, sequence layers, and prediction layers. Experimental results on Yelp, Amazon Fashion and Amazon Beauty datasets demonstrate that LLM-CFSR improves the overall recommendation performance and the long-tail item recommendation performance.

Key words： Sequential Recommendation Large Language Model Semantic Enhancement Collaborative Information Fusion

Received: 29 September 2025

TP 391

Fund:National Natural Science Foundation of China（No.62266023,62466028）, Natural Science Foundation of Jiangxi Province（No.20242BAB20045）

Corresponding Authors: HUANG Qi, Ph.D., lecturer. His research interests include social network analysis, rumor detection, graph neural networks, natural language processing and information retrieval.

About author:: ZHA Longbao, Master student. His research interests include natural language processing and recommender systems. WANG Mingwen, Ph.D., professor. His research interests include natural language processing, information extraction, information retrieval and data mining. ZHOU Junxiang, Master student. His research interests include natural language processing and recommender systems. LUO Wenbing, Ph.D., senior experimentalist. His research interests include natural language processing, information retrieval and knowledge graph.

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	ZHA Longbao
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	LUO Wenbing

Cite this article:

ZHA Longbao,HUANG Qi,WANG Mingwen等. Sequential Recommendation with Large Language Model Enhancement and Collaborative Information Fusion[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(10): 925-937.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202510005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I10/925

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