AutoRecLab：用自然语言自动生成推荐系统实验代码
AutoRecLab: Describe the Experiment, Get the Code!
朋友，有个叫 AutoRecLab 的工具，能让你用自然语言描述实验需求，它就能自动帮你生成代码并运行实验，省去手动写代码的麻烦。
AutoRecLab 是一个 Python 工具，能根据自然语言指令自动设计并执行推荐系统实验。它通过 RAG 查找文档、静态类型验证和执行引导的树搜索来工作。在基准测试中，它成功完成了 9 次中的 8 次实验，平均每次花费约 1 美元使用 GPT-5.4-mini。
AutoRecLab: Describe the Experiment, Get the Code!
Empirical evaluation is central to recommender-systems (RecSys) research, but turning experimental designs into executable code remains a manual and error-prone task. We present AutoRecLab, a Python-based autonomous RecSys lab that automates RecSys experiments from natural-language prompts. Given a research idea, AutoRecLab derives explicit experiment requirements, builds and validates a prototype, and iteratively expands it into the requested full experiment. The workflow combines retrieval-augmented generation (RAG) for documentation lookup, static type verification, and execution-steered tree search. In our demonstration, AutoRecLab autonomously implements an explicit-to-implicit feedback conversion study. In a baseline comparison across six algorithms and three datasets, 8 of 9 runs succeed at an average cost of approx- imately $1 per run with GPT-5.4-mini.
