RecGen: No-Coding Shell of Rule-Based Expert System with Digital Twin and Capability-Driven Approach Elements for Building Recommendation Systems

• Published in: Applied sciences Vol. 15; no. 19; p. 10482
• Main Authors: Kodors, Sergejs, Apeinans, Ilmars, Zarembo, Imants, Lonska, Jelena
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2025
• Subjects: Artificial intelligence; capability-driven development; Code reuse; Community support; Computer software industry; Decision making; decision support; digital twin; Digital twins; expert system; food waste; International economic relations; Knowledge representation; Logic; Plant diseases; Python; Recommender systems; Software; Systems development; User interface; New York; United Kingdom; Latvia; United States
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app151910482

Translating knowledge into formal representation for the purpose of building an expert system is a daunting task for domain experts and requires information technology (IT) competence and software developer support. The availability of open and robust expert system shells is a way to solve this task. A new architecture of a rule-based expert system combining the digital twin paradigm and a capability-driven approach is presented in this study. The aim of the architecture is to provide a user-friendly framework for domain experts to build upon without the need to delve into technical aspects. To support this architecture, an open-source no-coding shell RecGen has been developed (Python and Django framework). RecGen was validated on a use case of an expert system for providing recommendations to reduce plate waste in schools. In addition, the article presents experiments with large language models (LLMs) by implementing a question-answering functionality in an attempt to improve the user experience while working with large expert system knowledge bases. A mean classification accuracy of 74.1% was achieved experimentally using the injection method with language prefixes. The ablation test was applied in order to investigate the effect of augmentation, injection, a linear layer size, and lowercase text on LLM accuracy. However, the analysis of the results showed that clustering algorithms would be a more suitable solution for future improvements of the expert system shell RecGen.