AlignGenRec: Aligning collaborative and textual knowledge for llm-based generative recommendation

Xing, Jinshuo

doi:10.1007/s10844-025-01002-x

AlignGenRec: Aligning collaborative and textual knowledge for llm-based generative recommendation

2025-10-01

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DOI：10.1007/s10844-025-01002-x

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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.

Keywords:

Recommendation systems

Collaborative filtering

Large language models

Generative recommendation

AlignGenRec: Aligning collaborative and textual knowledge for llm-based generative recommendation

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