AutoRecLab: Describe the Experiment, Get the Code!

2026-09-18

AI Summary

为解决推荐系统实验设计转代码难题，AutoRecLab通过自然语言提示自动生成实验代码，采用RAG、静态类型验证和执行引导树搜索方法。

Abstract

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.

Problem

• recommender systems
• experimental design
• automated code generation
• natural language processing

Innovation

• AutoRecLab
• retrieval-augmented generation (RAG)
• static type verification
• execution-steered tree search