CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation
CoRR(2023)
摘要
Leveraging Large Language Models as Recommenders (LLMRec) has gained
significant attention and introduced fresh perspectives in user preference
modeling. Existing LLMRec approaches prioritize text semantics, usually
neglecting the valuable collaborative information from user-item interactions
in recommendations. While these text-emphasizing approaches excel in cold-start
scenarios, they may yield sub-optimal performance in warm-start situations. In
pursuit of superior recommendations for both cold and warm start scenarios, we
introduce CoLLM, an innovative LLMRec methodology that seamlessly incorporates
collaborative information into LLMs for recommendation. CoLLM captures
collaborative information through an external traditional model and maps it to
the input token embedding space of LLM, forming collaborative embeddings for
LLM usage. Through this external integration of collaborative information,
CoLLM ensures effective modeling of collaborative information without modifying
the LLM itself, providing the flexibility to employ various collaborative
information modeling techniques. Extensive experiments validate that CoLLM
adeptly integrates collaborative information into LLMs, resulting in enhanced
recommendation performance. We release the code and data at
https://github.com/zyang1580/CoLLM.
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关键词
collaborative embeddings,large language models
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