Recommendation Systems Research Automaticization with AutoRecLab

The experimental evaluation in machine learning and artificial intelligence research plays a critical role. However, there is a long and error-able process to transform the code running from designing these experiences. A new study released in arXiv offers a Python-based autonomous laboratory called AutoRecLab to solve this problem.

• AutoRecLab can automatically create proposal systems (RecSys) experiments from natural language instructions
• The researchers are just enough to explain their ideas; system requirements write çıkarıp code
• Prototyping and verification phases are being automated
• Thanks to improved expansion, complex systems can be reached from simple experiments
• significantly reduces handwriting coding errors and repetitive work

This tool targets a basic problem in RecSys research: space between experimental designs and code running them. Normally researchers explain their own ideas in paper, then someone else or the same person writes that code. This process takes time and is easy to reveal errors.

Thanks to AutoRecLab, researchers are able to push faster and less technical load exposure. Especially to test new ideas, experiment with different data sets and verify results is becoming much more efficient. This approach shows that automation and code production technologies are played an increasingly important role in AI and data science research.