Personalized Ranking Mechanism Using Yandex Dataset on Machine Learning Approaches

Abstract

Web service technology is extensively utilized as data source by every client. As the quantity of Web information develops quickly, search engines are capable to retrieve data based on the customer preferences. Users now rely on the Internet to meet their information needs, but current search engines often return a long list of results despite using sophisticated document indexing algorithms, many of which were not constantly applicable to the needs of the customer. Because a customer has a precise aim in mind when looking for data, personalized exploration will deliver outcomes that precisely match the user’s plan and purpose. Query-based investigate is commonly used by businesses to assist customers in finding information and products on their Websites. We look at how to rank a collection of outcomes returned in reply for a query in the most efficient way possible. Based on a customer search and click record, we propose a personalized ranking mechanism. We present our Yandex personalized Web search challenge solution. The goal of this challenge was to personalize top-N document rankings for a group of test users using historical search logs.