title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¿Ð¾Ð¸ÑÐºÐ° ÑÐµÐ¼Ð°Ð½ÑÐ¸ÑÐµÑÐºÐ¸ ÑÑ Ð¾Ð¶Ð¸Ñ Ð·Ð°Ð¿ÑÐ¾ÑÐ¾Ð² Ð·Ð°ÐºÐ°Ð·ÑÐ¸ÐºÐ°
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: Application of machine learning methods to search for semantically similar customer requests
Creators: name: Ð¡Ð¾ÐºÐ¾Ð»Ð¾Ð²Ð°, ÐÐ»ÐµÐºÑÐ°Ð½Ð´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: 2020
Subjects: Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ doc2vec
ÑÐµÐ¼Ð°Ð½ÑÐ¸ÑÐµÑÐºÐ¸ ÑÑ Ð¾Ð¶Ð¸Ðµ Ð·Ð°Ð¿ÑÐ¾ÑÑ
ÐºÐ¾ÑÐ¸Ð½ÑÑÐ½Ð°Ñ Ð±Ð»Ð¸Ð·Ð¾ÑÑÑ
Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¸Ð¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð·Ð°Ð¿ÑÐ¾ÑÐ¾Ð²
doc2vec classification algorythm
semantically similar issues
cosine similarity
automatic request analysis

description: ÐÑ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° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ¾ÐºÑÐ°ÑÐµÐ½Ð¸Ñ ÑÑÑÐ´Ð¾ÐµÐ¼ÐºÐ¾ÑÑÐ¸ ÑÐ°Ð·Ñ ÑÐ¾Ð¿ÑÐ¾Ð²Ð¾Ð¶Ð´ÐµÐ½Ð¸Ñ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¿ÑÐ¾Ð´ÑÐºÑÐ°. ÐÑÐ¾Ð²ÐµÐ´ÐµÐ½ Ð¾Ð±Ð·Ð¾Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð²ÐµÐºÑÐ¾ÑÐ½Ð¾Ð³Ð¾ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð´Ð°Ð½Ð½ÑÑ. ÐÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ ÑÐ¿Ð¾ÑÐ¾Ð±Ð¾Ð² Ð¾ÑÐµÐ½ÐºÐ¸ ÑÐµÐ¼Ð°Ð½ÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ ÑÑÐ¾Ð´ÑÑÐ²Ð° ÑÐµÐºÑÑÐ¾Ð². ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ñ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ðµ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½ÑÑ Ð´Ð»Ñ Ð¿Ð¾Ð¸ÑÐºÐ° Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð·Ð°Ð¿ÑÐ¾ÑÐ¾Ð² Ð·Ð°ÐºÐ°Ð·ÑÐ¸ÐºÐ°. 
Ð ÑÐ°Ð¼ÐºÐ°Ñ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½ Ð¼ÐµÑÐ¾Ð´ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð½ÐµÑÐµÑÐµÐ½Ð½ÑÑ Ð·Ð°Ð¿ÑÐ¾ÑÐ¾Ð² Ð¸ Ð¿Ð¾Ð¸ÑÐºÐ° ÑÐµÐ¼Ð°Ð½ÑÐ¸ÑÐµÑÐºÐ¸ ÑÑÐ¾Ð¶Ð¸Ñ. ÐÐ¿Ð¸ÑÐ°Ð½ Ð¿ÑÐ¾ÑÐµÑÑ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ Ð½Ð°Ð±Ð¾ÑÐ° Ð´Ð°Ð½Ð½ÑÑ Ð¸Ð· Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ Ð·Ð°Ð¿ÑÐ¾ÑÐ¾Ð² Ð·Ð°ÐºÐ°Ð·ÑÐ¸ÐºÐ°, ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½ Ð¿Ð¾Ð´ÑÐ¾Ð´ Ðº Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Doc2Vec Ð´Ð»Ñ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð¸Ñ Ð²ÐµÐºÑÐ¾ÑÐ½Ð¾Ð³Ð¾ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ ÑÐµÐºÑÑÐ°. ÐÐ¾ÐºÐ°Ð·Ð°Ð½ ÑÑÐµÐ½Ð°ÑÐ¸Ð¹ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ ÑÐ¾Ð·Ð´Ð°Ð½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð·Ð°Ð¿ÑÐ¾ÑÐ°, Ð° ÑÐ°ÐºÐ¶Ðµ Ð¿ÑÐ¸Ð²ÐµÐ´ÐµÐ½ Ð¼ÐµÑÐ¾Ð´ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð·Ð°Ð¿ÑÐ¾ÑÐ¾Ð² Ð·Ð°ÐºÐ°Ð·ÑÐ¸ÐºÐ° Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¿ÑÐ°Ð²Ð¸Ð». Ð ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð²ÑÑÐ¾Ð´Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ Ð¾Ð¿Ð¸ÑÐ°Ð½Ð¾ ÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¾ÑÑÐµÑÐ° Ñ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÐ¼Ð¸ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸ÑÐ¼Ð¸. 
ÐÐµÑÐ¾Ð´ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½ Ð² Ð¾Ð¿Ð¸ÑÐ°Ð½Ð½Ð¾Ð¼ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð¼ ÑÑÐµÐ´ÑÑÐ²Ðµ, Ð² ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ°Ñ Ð¿ÑÐ¸Ð²ÐµÐ´ÐµÐ½Ð° Ð¸Ð½ÑÑÑÑÐºÑÐ¸Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½ÑÐ°, Ð° ÑÐ°ÐºÐ¶Ðµ ÐµÐ³Ð¾ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð½Ð° Ð´Ð°Ð½Ð½ÑÑ Ð¿ÑÐ¾ÐµÐºÑÐ° Apache KAFKA. Ð¡Ð´ÐµÐ»Ð°Ð½Ñ Ð²ÑÐ²Ð¾Ð´Ñ Ð¾Ð± ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ Ð¿Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ Ñ ÑÑÑÐ½ÑÐ¼ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¾Ð¼ Ðº Ð°Ð½Ð°Ð»Ð¸Ð·Ñ Ð·Ð°Ð¿ÑÐ¾ÑÐ¾Ð² Ð·Ð°ÐºÐ°Ð·ÑÐ¸ÐºÐ°.
description: The bachelorâs thesis is concerned with research in reducing the complexity of software maintenance phase. The review of the algorithms for the vector representation of data is carried out. The comparison of different ways to evaluate the semantic similarity of texts is presented. The existing tools for searching and analyzing customer requests are researched. 
Within the framework of the work, a method for analyzing unresolved requests and searching for semantically similar ones is proposed. The process of creating a dataset from collected customer requests is described, an approach to using the Doc2Vec algorithm to obtain a vector representation of text is considered. A scenario of using the created model for classifying a request is shown, and a method for analyzing customer requests with the help of rules is presented. As an output, the generation of a report with the received recommendations is described. 
The method is implemented in the described software, the results contain instructions for using the tool, as well as its use on Apache KAFKA project data. Conclusions are made about the effectiveness of the developed system compared to the manual approach to analysis of customer requests.