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AlignGenRec: Aligning collaborative and textual knowledge for llm-based generative recommendation
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DOI：10.1007/s10844-025-01002-x.png)
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摘要
En 中文
Recommender systems based on collaborative filtering (CF) effectively model user-item interactions but struggle with data sparsity and cold-start issues. On the other hand, large language models (LLMs) offer strong semantic understanding but fail to capture structured user-item relationships. To bridge this gap, we propose AlignGenRec, a framework that integrates collaborative and textual knowledge for generative recommendation. Specifically, we introduce an embedding alignment mechanism that aligns item embeddings from a pre-trained CF model with text embeddings from item descriptions. These aligned embeddings are then transferred to the LLM, enabling structured knowledge integration while preserving generative capabilities. Additionally, constrained sequence decoding ensures that generated recommendations correspond to valid items, improving recommendation accuracy. Experimental results demonstrate that AlignGenRec outperforms both CF-based and LLM-based baselines, particularly in cold-start scenarios. Beyond recommendation tasks, AlignGenRec also supports preference prediction and user profiling, highlighting its versatility in real-world applications. Our code is available at https://github.com/xingjinshuo/AligenGenRec.git.
Keyword:
Recommendation systems
Collaborative filtering
Large language models
Generative recommendation
期刊
J
IF:
3.4
论文数:
78
被引数:
0
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引用论文
Wang, L.; Lim, E.-P. Zero-Shot Next-Item Recommendation using Large Pretrained Language Models. arXiv 2023, arXiv:2304.03153. [Google Scholar] [CrossRef]王, L.; Lim, E.-P. 基于大型预训练语言模型的零样本下一项推荐. arXiv 2023, arXiv:2304.03153. [Google Scholar] [CrossRef].png)
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