Yandex is actively working on improving the search work. Each change in the search algorithms is aimed at increasing its effectiveness for users.
The main goal of the search is to provide the user with complete, useful and relevant information that will help him solve the problem quickly and without unnecessary effort.
The search results is completely formed by machine-scientific algorithms, which ensures the impartial results. Manual intervention in changes in the procedure for displaying results is impossible.
To search for the most suitable pages, the search takes into account many factors, including the user's request, the content of the pages, the interaction with them, the connection between the pages, as well as the user location. Algorithms automatically evaluate the quality of the system using metrics, which are calculated on the basis of data on interaction with the search results.
For machine learning, data on user interactions with the results of the search, as well as the assessment of specialists (asssorov) are used, who manually check the relevance and quality of the results. To ensure objectivity, assessments of the assessors are controlled by a system that includes hiring, training and tools for working with grades. These estimates help improve ranking algorithms, but are not used directly to influence the results of the search.
All changes in the ranking system go through the automatic introduction of new algorithms, are controlled using metrics based on users (proxima) data and interaction with the search results (surplus).
Each possible change in search algorithms is estimated by two key metrics:
To assess the quality of the search, Yandex actively uses the assessors that help assess the relevance and quality of specific sites and results. These estimates help to understand how useful a change in users is, but they do not affect the ranking directly.
To check the changes, online experiments are held, in which users are randomly divided into two groups: one sees the current version of the search, and the other is a new one. Having collected the data, experts conclude whether the change is positive for users, using key metrics of quality, the main of which is the surplus.
The metric of proxima is based on assessments of the assessors and additional signals about the quality of content. Includes many factors for an accurate assessment of page quality:
The metric of proxima continues to develop, including new signals about the quality of pages and solving user problems.
The metric surplus evaluates the usefulness of issuing, that is, how effectively, users solve their problems through search results.
It takes into account both the quantity and the quality of user interactions with the search results. For the formation of the issuance, those elements that are of the greatest value in the predicted surplus are selected.
The system also optimizes the location of the elements on the page in order to increase the predicted value of the surplus and proxima for the entire issuance. If the actual surplus differs from the expected, the algorithm adjusts the results in the process of retraining.
The surplus is calculated according to the following rules:
The metric takes into account both clicks on links and actions without clicks (for example, if the user has found the necessary information in a factory answer). All these interactions can affect the metric, increasing it.
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