Model for Data Objects Selection by Search Image for Intelligent Recommender Systems

The research is conducted to develop and analyze an object filtering model for intelligent recommender systems. The main objective is to solve the problem of orientation in the vast amounts of information accumulated by mankind. The aim of the research is to create an effective tool for systematization and knowledge management, which in turn contributes to the optimization of decision-making processes and interaction with data. The paper focuses on the research and development of an object filtering model for intelligent recommender systems. Within the methodology and research area, the developed model is described in detail and the theoretical and practical aspects of the methodology are analyzed. In this paper, a variant of the problem statement of the research and development of an object filtering model for intelligent recommender systems is presented. In addition, the paper deconstructs the second stage of this problem, emphasizing its importance in the context of achieving system performance. The results of the study are analyzed in detail, highlighting the key points and features of the proposed model. The scope of the study is reviewed, detailing the prospects of applying the results in scientific and practical applications, providing the reader with a deeper understanding of the potential of the proposed model. The object filtering model has a high potential of usefulness for business and manufacturing. This work will be useful for developers and researchers of recommender systems in which users rarely or never interact with the same object.