推荐即指令遵循：一种大语言模型赋能的推荐方法
Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach
AI总结：
本文提出将推荐视为大语言模型的指令遵循，通过指令微调开源LLM（3B Flan-T5-XL），设计39个模板并生成252K条指令数据，在多个推荐任务上超越包括GPT-3.5在内的基线，实现更自然的用户交互推荐。
摘要：
在过去的几十年中，推荐系统在研究和工业界都引起了广泛关注，大量研究致力于开发有效的推荐模型。基本上，这些模型主要从历史行为数据中学习用户的潜在偏好，然后估计用户与物品的匹配关系以进行推荐。受大语言模型（LLMs）近期进展的启发，我们采用了一种不同的方法来开发推荐模型，将推荐视为LLMs的指令遵循。关键思想是，用户的偏好或需求可以用自然语言描述（称为指令）来表达，这样LLMs就能理解并进一步执行指令以完成推荐任务。我们没有使用LLMs的公共API，而是对开源LLM（3B Flan-T5-XL）进行指令微调，以便更好地使LLMs适应推荐系统。为此，我们首先设计了一种通用的指令格式，用自然语言描述用户的偏好、意图、任务形式和上下文。然后，我们手动设计了39个指令模板，并自动生成了大量具有不同类型偏好和意图的用户个性化指令数据（252K条指令）。为了证明我们方法的有效性，我们将指令模板实例化为几个广泛研究的推荐（或搜索）任务，并在真实世界数据集上对这些任务进行了广泛实验。实验结果表明，所提出的方法可以在这些评估任务上优于几个有竞争力的基线，包括强大的GPT-3.5。我们的方法为开发更用户友好的推荐系统提供了思路，在这些系统中，用户可以通过自然语言指令自由地与系统通信，并获得更准确的推荐。
英文摘要：
In the past decades, recommender systems have attracted much attention in both research and industry communities, and a large number of studies have been devoted to developing effective recommendation models. Basically speaking, these models mainly learn the underlying user preference from historical behavior data, and then estimate the user-item matching relationships for recommendations. Inspired by the recent progress on large language models (LLMs), we take a different approach to developing the recommendation models, considering recommendation as instruction following by LLMs. The key idea is that the preferences or needs of a user can be expressed in natural language descriptions (called instructions), so that LLMs can understand and further execute the instruction for fulfilling the recommendation task. Instead of using public APIs of LLMs, we instruction tune an open-source LLM (3B Flan-T5-XL), in order to better adapt LLMs to recommender systems. For this purpose, we first design a general instruction format for describing the preference, intention, task form and context of a user in natural language. Then we manually design 39 instruction templates and automatically generate a large amount of user-personalized instruction data (252K instructions) with varying types of preferences and intentions. To demonstrate the effectiveness of our approach, we instantiate the instruction templates into several widely-studied recommendation (or search) tasks, and conduct extensive experiments on these tasks with real-world datasets. Experiment results show that the proposed approach can outperform several competitive baselines, including the powerful GPT-3.5, on these evaluation tasks. Our approach sheds light on developing more user-friendly recommender systems, in which users can freely communicate with the system and obtain more accurate recommendations via natural language instructions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
