AutoRecLab: An Autonomous Recommender Systems Lab

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Recommender systems (RecSys) research depends on extensive empirical evaluation, yet translating experimental designs into executable code remains a manual, error-prone process. This paper presents AutoRecLab, a Python-based autonomous RecSys lab that automates RecSys experimentation from natural-language prompts. Starting from a research idea, AutoRecLab derives explicit experiment requirements, develops and validates a prototype, and iteratively refines it into the requested full experiment by combining retrieval-augmented generation (RAG) documentation lookup, static type verification, and execution-steered tree search. As a demonstration, AutoRecLab autonomously implements an explicit-to-implicit feedback conversion study and, in a baseline comparison across six algorithms and three datasets, achieves 8 out of 9 successful runs at an average cost of approximately $1 per run using GPT-5.4-mini.