INTELLIGENT TECHNOLOGIES FOR ANALYSIS, CLASSIFICATION AND RECOMMENDATIONS IN COLLECTION MANAGEMENT SYSTEMS

Abstract

The article is devoted to the development and investigation of intelligent technologies for analysis, classification, and recommendation in collection management systems. The study addresses the challenge of organizing and processing collection data, which are characterized by rapid growth in volume and increasing heterogeneity. These factors complicate efficient storage, search, and structuring of collections. It is shown that traditional collection management systems are usually limited to isolated tasks such as basic storage or search and often fail to integrate similarity analysis, classification, and recommendation algorithms into a unified framework. The use of intelligent approaches enables overcoming these limitations and provides opportunities for building universal systems capable of handling large-scale datasets. The purpose of the research is to achieve enhanced efficiency and scalability in collection management by constructing a model of an intelligent system that integrates multimodal object representation, classification methods, duplicate detection, and recommendation mechanisms. To achieve this goal, existing approaches were analyzed, a model for representing collection objects was formalized, similarity computation techniques were developed, and an architecture combining classification and recommendation modules was proposed. Experimental evaluation demonstrated that the integration of these components ensures high classification accuracy, effective duplicate detection, and relevant personalized recommendations. Particular attention was paid to scalability: the system maintained a stable response time even under significant growth in the number of collection objects. The practical significance of the research lies in the universality of the proposed model, which can be applied both in private multimedia and digital collections and in corporate or scientific infrastructures, such as libraries, archives, and databases. The scientific novelty is defined by the creation of a comprehensive architecture that integrates several intelligent methods into a unified system. Future research perspectives include the application of deep learning approaches for multimodal feature processing, the improvement of recommendation algorithms, and the integration with security and access control mechanisms to ensure robustness in large-scale environments.