  When adapting Large Language Models for Recommendation (LLMRec), it is
crucial to integrate collaborative information. Existing methods achieve this
by learning collaborative embeddings in LLMs' latent space from scratch or by
mapping from external models. However, they fail to represent the information
in a text-like format, which may not align optimally with LLMs. To bridge this
gap, we introduce BinLLM, a novel LLMRec method that seamlessly integrates
collaborative information through text-like encoding. BinLLM converts
collaborative embeddings from external models into binary sequences -- a
specific text format that LLMs can understand and operate on directly,
facilitating the direct usage of collaborative information in text-like format
by LLMs. Additionally, BinLLM provides options to compress the binary sequence
using dot-decimal notation to avoid excessively long lengths. Extensive
experiments validate that BinLLM introduces collaborative information in a
manner better aligned with LLMs, resulting in enhanced performance. We release
our code at https://github.com/zyang1580/BinLLM.
