Sequential recommendation is to predict the next item of interest for a user, based on her/his interaction history with previous items. In conventional sequential recommenders, a common approach is to model item sequences using discrete IDs, learning representations that encode sequential behaviors and reflect user preferences. Inspired by recent success in empowering large language models (LLMs) to understand and reason over diverse modality data (e.g., image, audio, 3D points), a compelling research question arises: ``Can LLMs understand and work with hidden representations from ID-based sequential recommenders?''.To answer this, we propose a simple framework, RecInterpreter, which examines the capacity of open-source LLMs to decipher the representation space of sequential recommenders. Specifically, with the multimodal pairs (\ie representations of interaction sequence and text narrations), RecInterpreter first uses a lightweight adapter to map the representations into the token embedding space of the LLM. Subsequently, it constructs a sequence-recovery prompt that encourages the LLM to generate textual descriptions for items within the interaction sequence. Taking a step further, we propose a sequence-residual prompt instead, which guides the LLM in identifying the residual item by contrasting the representations before and after integrating this residual into the existing sequence. Empirical results showcase that our RecInterpreter enhances the exemplar LLM, LLaMA, to understand hidden representations from ID-based sequential recommenders, especially when guided by our sequence-residual prompts. Furthermore, RecInterpreter enables LLaMA to instantiate the oracle items generated by generative recommenders like DreamRec, concreting the item a user would ideally like to interact with next. Codes are available at https://github.com/YangZhengyi98/RecInterpreter.

翻译：序列推荐旨在根据用户与历史物品的交互记录，预测其下一个感兴趣的目标物品。在传统序列推荐器中，常见方法是通过离散ID对物品序列建模，学习能够编码序列行为并反映用户偏好的表示。受大语言模型（LLMs）成功理解与推理多模态数据（如图像、音频、三维点云）的启发，一个引人入胜的研究问题随之产生：“大语言模型能否理解并操作基于ID的序列推荐器所生成的隐藏表示？”为回答这一问题，我们提出一个简单框架RecInterpreter，该框架用于探究开源大语言模型解读序列推荐器表示空间的能力。具体而言，通过多模态配对数据（即交互序列表示与文本描述），RecInterpreter首先利用轻量级适配器将表示映射至大语言模型的词元嵌入空间；随后，构建序列恢复提示，引导大语言模型为交互序列中的物品生成文本描述。更进一步，我们提出序列残差提示，该提示通过对比将残差物品纳入现有序列前后的表示差异，指导大语言模型识别该残差物品。实验结果表明，我们的RecInterpreter能够增强代表大语言模型LLaMA对基于ID的序列推荐器中隐藏表示的理解能力，尤其是在采用序列残差提示时。此外，RecInterpreter使LLaMA能够实例化生成式推荐器（如DreamRec）所生成的理想物品，即将用户最理想交互的下个物品具体化。代码详见https://github.com/YangZhengyi98/RecInterpreter。