title: Ð ÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½Ð°Ñ ÑÐ¸ÑÑÐµÐ¼Ð° Ð´Ð»Ñ ÑÐ»ÐµÐºÑÑÐ¾Ð½Ð½Ð¾Ð¹ ÐºÐ¾Ð¼Ð¼ÐµÑÑÐ¸Ð¸ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸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: A recommender system for e-commerce based on machine learning techniques
Creators: name: Ð¢Ð°Ð³Ð¸ÑÐ¾Ð², ÐÐ»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: 2022
Subjects: ÐºÐ¾Ð»Ð»Ð°Ð±Ð¾ÑÐ°ÑÐ¸Ð²Ð½Ð°Ñ ÑÐ¸Ð»ÑÑÑÐ°ÑÐ¸Ñ
Ð³Ð¸Ð±ÑÐ¸Ð´Ð½Ð°Ñ ÑÐ¸Ð»ÑÑÑÐ°ÑÐ¸Ñ
Ð´Ð²ÑÑ ÑÑÐ°Ð¿Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ
collaborative filtering
hybrid filtering
two-stage model

description: Ð¢ÐµÐ¼Ð° ÑÐ°Ð±Ð¾ÑÑ: Â«Ð ÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½Ð°Ñ ÑÐ¸ÑÑÐµÐ¼Ð° Ð´Ð»Ñ ÑÐ»ÐµÐºÑÑÐ¾Ð½Ð½Ð¾Ð¹ ÐºÐ¾Ð¼Ð¼ÐµÑÑÐ¸Ð¸ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸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 ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ð¹ ÑÐ»ÐµÐ´ÑÑÑÐµÐ³Ð¾ ÑÐµÐ°Ð½ÑÐ° Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»Ñ. 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¸: 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. ÐÐ½Ð°Ð»Ð¸Ð· Ð¸ Ð¿ÑÐµÐ´Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð½Ð°Ð±Ð¾ÑÐ° Ð´Ð°Ð½Ð½ÑÑ. 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° TIFU-KNN, Ð¿Ð¾Ð´Ð±Ð¾Ñ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½ÑÑ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð². Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð´Ð²ÑÑÑÑÐ°Ð¿Ð½Ð¾Ð¹ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½Ð¾Ð¹ ÑÐµÑÐ¸ Ð½Ð° Ð±Ð°Ð·Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° TIFU-KNN Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿ÐµÑÐ²Ð¾Ð³Ð¾ ÑÑÐ°Ð¿Ð° Ð¸ Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ½Ð¾Ð³Ð¾ Ð±ÑÑÑÐ¸Ð½Ð³Ð° Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð²ÑÐ¾ÑÐ¾Ð³Ð¾ ÑÑÐ°Ð¿Ð°. 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¸. 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 topic of the final qualifying work: Â«A recommender system for e-commerce based on machine learning techniquesÂ». The subject of the study is creating the most accurate algorithm for recommending the next user session. Tasks that were solved during the work: Review and comparison of approaches to solving the problem of recommendations, identifying the problems of recommendation systems.Analysis and preprocessing of the data set. Construction of recommendation network based on TIFU-KNN algorithm, selection of optimal parameters. Development of a two-stage recommendation network based on the TIFU-KNN algorithm as a first-stage model and gradient boosting as a second-stage model. Implementation of a recommendation network based on deep learning, selection of the optimal optimization method, neural network training.Analysis of the results, choosing the best approach to the construction of recommendation systems.