title: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð´Ð»Ñ ÑÐ°Ð½Ð¶Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾Ð¸ÑÐºÐ¾Ð²Ð¾Ð¹ Ð²ÑÐ´Ð°ÑÐ¸ ÐºÐ¾Ð½ÑÐµÐ½ÑÐ°
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 methods for ranking content in search results
Creators: name: Ð¡Ð°ÑÑÐºÐ¾Ð², ÐÐµÐ²
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: Ð¿Ð¾Ð¸ÑÐº
ÑÐ°Ð½Ð¶Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ½ÑÐ¹ Ð±ÑÑÑÐ¸Ð½Ð³
search
ranking
machine learning
gradient boosting

description: Ð¦ÐµÐ»ÑÑ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÑÐ°Ð½Ð¶Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð¿Ð¾Ð¸ÑÐºÐ° Ð¿ÑÐ±Ð»Ð¸ÐºÐ°ÑÐ¸Ð¹ Ð² ÑÐ¾ÑÐ¸Ð°Ð»ÑÐ½Ð¾Ð¹ ÑÐµÑÐ¸. ÐÑÐµÐ´Ð¼ÐµÂ­ÑÐ¾Ð¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÑÑÑÑ Ð¿Ð¾Ð¸ÑÐºÐ¾Ð²ÑÐµ ÑÐ¸ÑÑÐµÐ¼Ñ, ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÐµ Ð¼ÐµÑÐ¾Ð´Ñ ÑÐ°Ð½Ð¶Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ. Ð ÐµÑÐ°ÐµÐ¼ÑÐµ Ð·Ð°Ð´Ð°ÑÐ¸: Â  A. ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÑÑÑÐ¾Ð¹ÑÑÐ²Ð° ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ Ð¿Ð¾Ð¸ÑÐºÐ¾Ð²ÑÑ ÑÐ¸ÑÑÐµÐ¼. Â  B. Ð¤Ð¾ÑÐ¼Ð°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÑÐ°Ð½Ð¶Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ. Â  C. ÐÐ½Ð°Ð»Ð¸Ð· ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¾Ð² Ð¸ Ð²Ð°ÑÐ¸Ð°Ð½ÑÐ¾Ð² ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÑÐ°Ð½Ð¶Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ. Â  D. ÐÐ²ÐµÐ´ÐµÐ½Ð¸Ðµ ÑÐ°ÐºÑÐ¾ÑÐ¾Ð² ÑÐ»ÐµÐ¼ÐµÐ½ÑÐ° Ð¿Ð¾Ð¸ÑÐºÐ¾Ð²Ð¾Ð¹ Ð²ÑÐ´Ð°ÑÐ¸ Ð¸ ÐµÐ³Ð¾ ÑÑÐ½ÐºÑÐ¸Ð¸ ÑÐµÐ»ÐµÐ²Ð°Ð½ÑÂ­Ð½Ð¾ÑÑÐ¸. Â  E. Ð¡Ð±Ð¾Ñ Ð½ÐµÐ¾Ð±ÑÐ¾Ð´Ð¸Ð¼ÑÑ Ð´Ð°Ð½Ð½ÑÑ Ð´Ð»Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ ÑÐ°Ð½Ð¶Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ. Â  F. ÐÐ½Ð°Ð»Ð¸Ð· ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÑÐ°Ð½Ð¶Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¸ Ð²ÑÐ±Ð¾Ñ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑÐµÐ¹ Ð´Ð»Ñ ÑÐ°Ð½Ð¶Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾Ð¸ÑÐºÐ¾Ð²Ð¾Ð¹ Ð²ÑÐ´Ð°ÑÐ¸. Â  G. ÐÐ±ÑÑÐµÐ½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸. Â  H. ÐÐ·ÑÑÐµÐ½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð° A/B ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð´Ð»Ñ Ð¿ÑÐ¾Ð²ÐµÑÐºÐ¸ Ð³Ð¸Ð¿Ð¾ÑÐµÐ·. Â  I. ÐÑÐ¾Ð²ÐµÑÐºÐ° Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð° Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ° Ð¿Ð¾Ð¸ÑÐºÐ¾Ð²ÑÑ ÑÐ¸ÑÑÐµÐ¼, ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ð° Ð·Ð°Ð´Ð°ÑÐ° ÑÐ°Ð½Ð¶Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¸ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÐµ Ð¼ÐµÑÐ¾Ð´Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÐµÐµ ÑÐµÑÐµÐ½Ð¸Ñ. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð° Ð²Ð²ÐµÐ´ÐµÐ½Ð° ÑÑÐ½ÐºÑÐ¸Ñ ÑÐµÐ»ÐµÐ²Ð°Ð½ÑÐ½Ð¾ÑÑÐ¸ ÑÐ»ÐµÐ¼ÐµÐ½ÑÐ° Ð¿Ð¾Ð¸ÑÐºÐ¾Â­Ð²Ð¾Ð¹ Ð²ÑÐ´Ð°ÑÐ¸, Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ ÑÐ±Ð¾Ñ Ð½ÐµÐ¾Ð±ÑÐ¾Ð´Ð¸Ð¼ÑÑ Ð´Ð°Ð½Ð½ÑÑ Ð·Ð° Ð·Ð°Ð´Ð°Ð½Ð½ÑÐ¹ Ð¿ÐµÑÐ¸Ð¾Ð´, Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½ Ð°Ð½ÑÐ°Ð¼Ð±Ð»Ñ Ð´ÐµÑÐµÐ²ÑÐµÐ² Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð° Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ½Ð¾Ð³Ð¾ Ð±ÑÑÑÐ¸Ð½Ð³Ð°, ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½Ð¾Ð³Ð¾ Ð² Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐºÐµ XGBoost, Ð° ÑÐ°ÐºÐ¶Ðµ Ð´Ð¾ÐºÐ°Ð·Ð°Ð½Ð° ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð´Ð¾Ð²ÐµÑÐ¸ÑÐµÐ»ÑÐ½ÑÐ¼ A/B ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸ÐµÐ¼. ÐÐ¿Ð¸ÑÐ°Ð½Ð½ÑÐµ Ð² Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð¼ÐµÑÐ¾Ð´Ñ Ð¸ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÑÐ½Ð¸Ð²ÐµÑÑÐ°Ð»ÑÐ½Ñ Ð¸ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑ Ð´Ð»Ñ Ð¼Ð½Ð¾Ð³Ð¸Ñ Ð¿Ð¾Ð¸ÑÐºÐ¾Ð²ÑÑ ÑÐ¸ÑÑÐµÐ¼, Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÑ Ð¸Ñ, Ð¼Ð¾Ð¶Ð½Ð¾ ÑÐ»ÑÑÑÐ¸ÑÑ Ð¿Ð¾Ð¸ÑÐºÐ¾Ð²Ð¾Ðµ ÑÐ°Ð½Ð¶Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¸ Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»ÑÑÐºÐ¸Ð¹ Ð¾Ð¿ÑÑ ÐºÐ»Ð¸ÐµÐ½ÑÐ¾Ð² ÑÐµÑÐ²Ð¸ÑÐ°.
description: The purpose of this work is to develop a machine learning model for ranking the search results of content. The subject of this work is search engines, modern methods of ranking search results. Tasks to be solved: Â  Â  A. Research modules of modern search engines and their work. Â  Â  B. Formalization of the ranking problem. Â  Â  C. Analysis of different approaches and options for solving the ranking problem. Â  Â  D. Introduction of SERP element factors and its relevance function. Â  Â  E. Gathering the necessary data for ranking training. Â  Â  F. Analysis of existing machine learning models for ranking and selection of suitable search results for ranking. Â  Â  G. Model Training. Â  Â  H. Learning the A/B testing method to test the built model. Â  Â  I. Checking the built model. In the course of the work, the architecture of search engines was investigated, the ranking problem and various machine learning methods for its solution were considered. As a result of the work, the relevance function of the SERP element was introduced, the necessary data was collected for a given period, an ensemble of trees was built using the gradient boosting method implemented in the XGBoost library, and the effectiveness of the resulting model was proved by confidence A/B testing. The methods described in this paper and the results obtained are universal and suitable for many search engines, using them you can improve the search ranking and user experience of service customers.