From AutoRecSys to AutoRecLab: A Call to Build, Evaluate, and Govern Autonomous Recommender-Systems Research Labs

• URL: http://arxiv.org/abs/2510.18104v1
• Date: Mon, 20 Oct 2025 20:58:50 GMT
• Title: From AutoRecSys to AutoRecLab: A Call to Build, Evaluate, and Govern Autonomous Recommender-Systems Research Labs
• Authors: Joeran Beel, Bela Gipp, Tobias Vente, Moritz Baumgart, Philipp Meister,
• Abstract summary: We argue for a shift from narrow AutoRecSys tools to an Autonomous Recommender-Systems Research Lab (AutoRecLab)<n>AutoRecLab integrates end-to-end automation: problem ideation, literature analysis, experimental design and execution, result interpretation, manuscript drafting, and logging.<n>We conclude with a call to organise a community retreat to coordinate next steps and co-author guidance for the responsible integration of automated research systems.
• Score: 5.34658805289521
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommender-systems research has accelerated model and evaluation advances, yet largely neglects automating the research process itself. We argue for a shift from narrow AutoRecSys tools -- focused on algorithm selection and hyper-parameter tuning -- to an Autonomous Recommender-Systems Research Lab (AutoRecLab) that integrates end-to-end automation: problem ideation, literature analysis, experimental design and execution, result interpretation, manuscript drafting, and provenance logging. Drawing on recent progress in automated science (e.g., multi-agent AI Scientist and AI Co-Scientist systems), we outline an agenda for the RecSys community: (1) build open AutoRecLab prototypes that combine LLM-driven ideation and reporting with automated experimentation; (2) establish benchmarks and competitions that evaluate agents on producing reproducible RecSys findings with minimal human input; (3) create review venues for transparently AI-generated submissions; (4) define standards for attribution and reproducibility via detailed research logs and metadata; and (5) foster interdisciplinary dialogue on ethics, governance, privacy, and fairness in autonomous research. Advancing this agenda can increase research throughput, surface non-obvious insights, and position RecSys to contribute to emerging Artificial Research Intelligence. We conclude with a call to organise a community retreat to coordinate next steps and co-author guidance for the responsible integration of automated research systems.

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