title: ÐÐ»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¸Ð´ÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÐºÑÐ¸ÑÐµÑÐ¸ÐµÐ² Ð¾ÑÐµÐ½ÐºÐ¸ Ð² ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐ¸ÑÑÐµÐ¼Ð°Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¾ÑÐ·ÑÐ²Ð¾Ð² Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»ÐµÐ¹
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: Algorithm for identifying significant criteria in recommendation systems based on user reviews
Creators: name: Ð¡Ð°ÑÐ°ÑÐ³Ð°Ð»ÐµÐµÐ²Ð°, ÐÐ°Ð»ÐµÑÐ¸Ñ
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: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¸Ð·Ð²Ð»ÐµÑÐµÐ½Ð¸Ñ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð²Ð½ÑÑ ÐºÑÐ¸ÑÐµÑÐ¸ÐµÐ²
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° ÐµÑÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐ·ÑÐºÐ°
ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐµÐ»ÑÐ½Ð°Ñ ÑÐ¸ÑÑÐµÐ¼Ð°
machine learning
algorithm for extraction of informative criteria
classification
natural language processing
recommendation system

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description: The subject of the graduate qualification work is: Â« Algorithm for identifying significant criteria in recommendation systems based on user reviews Â»
The bachelor's thesis examines planning and development of an algorithm for extracting information about the criteria that guide the user of online stores when setting ratings in accordance with identified criteria based on user feedback texts. The research set the following goals:

1. Consideration of existing algorithms of recommendation systems that use additional criteria to clarify recommendations to the user.
2. Studying and selection of tools for extracting criteria from text reviews.
3. Planning of stages of the algorithm.
4. Development of an algorithm for extracting criteria from a text review, detailing the user's rating.
5. Prediction of user ratings based on informative criteria.
6. Obtaining an experimental assessment of the quality of the algorithm.
7. Assessment of the informative of the obtained criteria, applying them to the recommendation system.
During the study, various user reviews of several databases were researched. The significant information that users indicate in reviews was analyzed. Patterns were found in the ratings and reviews corresponding to these ratings. Methods and tools of natural language processing were used to implement the algorithm of significant criteria.

As a result, an algorithm for extracting significant criteria from user feedback texts was implemented. The significance and applicability of the identified criteria were tested on the constructed classifier and a real recommendation system. As a result of testing, it was found that the use of criteria detailing the assessment improves the recommendation system's performance.