AutoRecLab: A Python-based autonomous RecSys lab
AutoRecLab: Describe the Experiment, Get the Code!
For researchers in recommender systems, AutoRecLab automates the process of turning experimental designs into executable code, reducing manual work and potential errors.
AutoRecLab is a Python tool that automates RecSys experiments from natural-language prompts. It derives experiment requirements, builds prototypes, and expands them into full experiments. In a demonstration, it autonomously implemented a feedback conversion study, successfully running 8 of 9 baseline comparisons across six algorithms and three datasets at an average cost of about $1 per run using 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.
