Open AccessInformation Retrieval using Machine learning for Ranking: A Review
Author(s) -
Sushilkumar Chavhan,
M. M. Raghuwanshi,
R. C. Dharmik
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1913/1/012150
Subject(s) - ranking (information retrieval) , computer science , learning to rank , information retrieval , ranking svm , rank (graph theory) , machine learning , field (mathematics) , artificial intelligence , mathematics , combinatorics , pure mathematics
The Ranking is one of the big issues in various information retrieval applications (IR). Various approaches to machine learning with various ranking applications have new dimensions in the field of IR. Most work focuses on the various strategies for enhancing the efficiency of the information retrieval system as a result of how related questions and documents also provide a ranking for successful retrieval. By using a machine learning approach, learning to rank is a frequently used ranking mechanism with the purpose of organizing the documents of different types in a specific order consistent with their ranking. An attempt has been made in this paper to position some of the most widely used algorithms in the community. It provides a survey of the methods used to rank the documents collected and their assessment strategies.