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Leveraging Large Language Models as Recommenders (LLMRec) has gainedsignificant attention and introduced fresh perspectives in user preferencemodeling. Existing LLMRec approaches prioritize text semantics, usuallyneglecting the valuable collaborative information from user-item interactionsin recommendations. While these text-emphasizing approaches excel in cold-startscenarios, they may yield sub-optimal performance in warm-start situations. Inpursuit of superior recommendations for both cold and warm start scenarios, weintroduce CoLLM, an innovative LLMRec methodology that seamlessly incorporatescollaborative information into LLMs for recommendation. CoLLM capturescollaborative information through an external traditional model and maps it tothe input token embedding space of LLM, forming collaborative embeddings forLLM usage. Through this external integration of collaborative information,CoLLM ensures effective modeling of collaborative information without modifyingthe LLM itself, providing the flexibility to employ various collaborativeinformation modeling techniques. Extensive experiments validate that CoLLMadeptly integrates collaborative information into LLMs, resulting in enhancedrecommendation performance. We release the code and data athttps://github.com/zyang1580/CoLLM.

CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation