会话推荐的项目-语言模型

Item-Language Model for Conversational Recommendation

June 5, 2024

作者: Li Yang, Anushya Subbiah, Hardik Patel, Judith Yue Li, Yanwei Song, Reza Mirghaderi, Vikram Aggarwal

cs.AI

摘要

大型语言模型（LLMs）在复杂对话理解、推理和编码等任务中取得了极大成功，这归功于它们的新兴能力。这些新兴能力已经通过多模态扩展到包括图像、音频和视频功能。另一方面，推荐系统对于信息搜索和物品发现需求至关重要。最近，已经有尝试将LLMs应用于推荐系统。目前尝试的一个困难是，基础LLM通常未经过在推荐系统数据上的训练，而这些数据主要包含用户交互信号且通常不公开。另一个困难是，用户交互信号往往与自然语言文本具有不同的模式，目前尚不清楚LLM训练设置是否能够从交互信号中学习到比传统推荐系统方法更多的非平凡知识。最后，对于不同用例难以训练多个LLMs，并且在从推荐系统数据中学习时保留原始语言和推理能力也是困难的。为了解决这三个限制，我们提出了一个物品语言模型（ILM），它由一个物品编码器组成，用于生成文本对齐的物品表示，编码用户交互信号，并且包含一个冻结的LLM，可以理解这些物品表示并保留预训练知识。我们进行了大量实验，展示了语言对齐和用户交互知识在物品编码器中的重要性。

English

Large-language Models (LLMs) have been extremely successful at tasks like complex dialogue understanding, reasoning and coding due to their emergent abilities. These emergent abilities have been extended with multi-modality to include image, audio, and video capabilities. Recommender systems, on the other hand, have been critical for information seeking and item discovery needs. Recently, there have been attempts to apply LLMs for recommendations. One difficulty of current attempts is that the underlying LLM is usually not trained on the recommender system data, which largely contains user interaction signals and is often not publicly available. Another difficulty is user interaction signals often have a different pattern from natural language text, and it is currently unclear if the LLM training setup can learn more non-trivial knowledge from interaction signals compared with traditional recommender system methods. Finally, it is difficult to train multiple LLMs for different use-cases, and to retain the original language and reasoning abilities when learning from recommender system data. To address these three limitations, we propose an Item-Language Model (ILM), which is composed of an item encoder to produce text-aligned item representations that encode user interaction signals, and a frozen LLM that can understand those item representations with preserved pretrained knowledge. We conduct extensive experiments which demonstrate both the importance of the language-alignment and of user interaction knowledge in the item encoder.