Yandex upgrades search engine to compare request with page content

MOSCOW, Aug 23 (PRIME) -- Russian Internet giant Yandex has launched a new version of its search engine, which is based on search algorithm Korolyov, comparing the sense of a request with the content of a Web page with the help of a deep neural network, the company said on Tuesday in its blog.

The upgraded version also incorporates Yandex.Toloka, a mass-scale crowd-sourced platform for search assessors into Yandex MatrixNet.

Korolyov, named after a Moscow Region namesake city-center of the space exploration program, is built on Palekh, the company’s first neural network based search algorithm released in late 2016. The update improves how Yandex handles infrequent and complex queries, known as long-tail queries, in two distinct ways.

First, Korolyov is better at understanding user intent than its predecessor because it examines the entirety of Web pages rather than just their headlines. Second, Korolyov can scale to analyze a thousand times more documents in real time than Palekh.

Like all modern artificial intelligence-based systems, Korolyov improves itself with each incremental data point. Korolyov results feed into MatrixNet, Yandex’s proprietary machine learning ranking algorithm, where a number of other ranking factors are considered before results are returned to a user.

Recently, MatrixNet started incorporating data from Yandex.Toloka, Yandex’s crowdsourcing platform, in addition to anonymized user data to train the machine learning algorithms.

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