Orynbekova K.Kadyrov Sh.Bogdanchikov A.Oktamov S.2025-08-132025-08-132024Orynbekova K , Kadyrov Sh ,Bogdanchikov A , Oktamov S /A novel recommender system for adapting single machine problems to distributed systems within MapReduce / Bulletin of Electrical Engineering and Informatics / 20242302-9285https://repository.sdu.edu.kz/handle/123456789/1877This research introduces a novel recommender system for adapting singlemachine problems to distributed systems within the MapReduce (MR) framework, integrating knowledge and text-based approaches. Categorizing common problems by five MR categories, the study develops and tests a tutorial with promising results. Expanding the dataset, machine learning models recommend solutions for distributed systems. Results demonstrate the logistic regression model's effectiveness, with a hybrid approach showing adaptability. The study contributes to advancing the adaptation of single-machine problems to distributed systems MR, presenting a novel framework for tailored recommendations, thereby enhancing scalability and efficiency in data processing workflows. Additionally, it fosters innovation in distributed computing paradigms.enDistributed systemKnowledge-based approachMachine learning modelArticle