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title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Development and implementation of algorithms for recommendation systems based on heuristic methods
Creators: name: ÐÐ»ÐµÐ¿Ð°ÑÐºÐ¸Ð¹, ÐÐ¸ÐºÐ¸ÑÐ°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2025
Subjects: ÑÐ¸ÑÑÐµÐ¼Ð° ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ð¹
ÐºÐ¾Ð½ÐµÑÐµÐ½ÑÐ°Ñ ÑÐ¸Ð»ÑÑÑÐ°ÑÐ¸Ñ
ÐºÐ¾Ð»Ð»Ð°Ð±Ð°ÑÐ°ÑÐ¸Ð²Ð½Ð°Ñ ÑÐ¸Ð»ÑÑÑÐ°ÑÐ¸Ñ
recommendation system
content filtering
collaborative filtering

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description: The topic of the final thesis: "Development and implementation of algorithms for recommendation systems based on heuristic methods" Tasks that were solved during the research: 1) Analyzing existing recommendation algorithms based on heuristic methods and their applicability to the tasks of a particular platform. 2) Development of algorithms based on heuristic methods that take into account data properties and platform-specific requirements; 3) Testing the developed algorithms on real data; 4) Implementation of the developed algorithms into the existing system infrastructure. The work included a study of existing recommendation methods, including content-based, collaborative, and hybrid approaches. Special attention was paid to heuristic methods that allow taking into account data features, specific dependencies between users and objects, as well as optimizing system operation with limited computing resources. After the algorithms were developed, several stages of testing were carried out: internal testing by the developer, experimental testing on real customer data, and external verification on a limited sample of users. Based on the feedback collected, the main modules of the system were finalized, bugs were fixed and performance was improved. As a result, a recommendation module based on heuristic methods was created, which is successfully integrated into the existing infrastructure.