title: ÐÐ»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¿ÐµÑÑÐ¾Ð½Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ð¹ Ð¿ÑÐ¾Ð´ÑÐºÑÐ¾Ð² Ð¸ ÑÑÐ»ÑÐ³ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¿ÑÐµÐ´Ð¿Ð¾ÑÑÐµÐ½Ð¸Ð¹ Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»Ñ
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: Machine learning algorithms for the personalization of product and service recommendations based on user preference analysis
Creators: name: Ð¢Ð¸ÑÐ¾Ð½Ð¾Ð²Ñ, ÐÐ°ÑÐ¸Ð»Ð¸Ð¹Ñ
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: 2023
Subjects: ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ¸ÑÑÐµÐ¼Ð°
Ð¾ÑÐµÐ½ÐºÐ°
ÑÐ¸Ð»ÑÑÑÐ°ÑÐ¸Ñ
Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð°Ñ Ð½Ð° ÑÐ¾Ð´ÐµÑÐ¶Ð¸Ð¼Ð¾Ð¼
Ð³Ð¸Ð±ÑÐ¸Ð´Ð½Ð°Ñ ÑÐ¸Ð»ÑÑÑÐ°ÑÐ¸Ñ
Python
recommender system
evaluation
filtering
forecasting
machine learning
neural network
content-based
hybrid filtering
Python.

description: Ð¢ÐµÐ¼Ð° ÑÐ°Ð±Ð¾ÑÑ: Â«ÐÐ»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¿ÐµÑÑÐ¾Ð½Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ð¹ Ð¿ÑÐ¾Ð´ÑÐºÑÐ¾Ð² Ð¸ ÑÑÐ»ÑÐ³ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¿ÑÐµÐ´Ð¿Ð¾ÑÑÐµÐ½Ð¸Ð¹ Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»ÑÂ». Ð¦ÐµÐ»Ñ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ ÑÐ¾ÑÐ½Ð¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð´Ð»Ñ Ð¾ÑÑÑÐµÑÑÐ²Ð»ÐµÐ½Ð¸Ñ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ð¹ ÑÐ¸Ð»ÑÐ¼Ð¾Ð². Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¸ ÑÐµÑÐµÐ½Ñ ÑÐ»ÐµÐ´ÑÑÑÐ¸Ðµ Ð·Ð°Ð´Ð°ÑÐ¸: 1) ÐÐ±Ð·Ð¾Ñ Ð¸ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¾Ð² Ðº ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ð¹, Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐ¸ÑÑÐµÐ¼. 2) ÐÐ½Ð°Ð»Ð¸Ð· Ð¸ Ð¿ÑÐµÐ´Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð½Ð°Ð±Ð¾ÑÐ° Ð´Ð°Ð½Ð½ÑÑ. 3) ÐÐ¾ÑÑÑÐ¾ÐµÐ½Ð¸Ðµ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½Ð¾Ð¹ ÑÐµÑÐ¸ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° KNN, Ð¿Ð¾Ð´Ð±Ð¾Ñ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½ÑÑ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð². 4) Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½Ð¾Ð¹ ÑÐµÑÐ¸ Ð½Ð° Ð±Ð°Ð·Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° SVD. 5) Ð ÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½Ð¾Ð¹ ÑÐµÑÐ¸ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, Ð²ÑÐ±Ð¾Ñ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð° Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸, Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸. 6) ÐÐ½Ð°Ð»Ð¸Ð· Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð², Ð²ÑÐ±Ð¾Ñ Ð»ÑÑÑÐµÐ³Ð¾ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð° Ðº Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐ¸ÑÑÐµÐ¼.
description: The subject of the paper: "Machine learning algorithms for the personalization of product and service recommendations based on user preference analysis". The aim of this paper: to implement the most accurate algorithm for movie recommendations. Tasks that were solved during the work: 1) Review and comparison of approaches to the recommendation problem, identifying the challenges of recommender systems. 2) Analysis and preprocessing of the data set. 3) Building a recommendation network based on the KNN algorithm, selection of optimal parameters. 4) Developing a recommendation network based on the SVD algorithm. 5) Implementation of a deep learning-based recommendation network, selection of the optimal optimization method, neural network training. 6) Analysis of the obtained results, selection of the best approach to the construction of recommendation systems.