Open AccessPredicting Ranking for Scientific Research Papers using Scalable Tensor Flow Library and Learning to Rank
Author(s) -
Sarabu Joshna*
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.d1693.029420
Subject(s) - ranking (information retrieval) , rank (graph theory) , computer science , artificial intelligence , scalability , learning to rank , machine learning , sample (material) , data science , natural language processing , information retrieval , class (philosophy) , sentiment analysis , mathematics , chemistry , chromatography , combinatorics , database
Scientific research papers play a vital role for innovation of new technology. It is the future of the development where a novice person can understand the technology and tries to develop a new idea. In this paper, concentrated on relative order for a group of items applied to scientific research paper. In this process we identify how LTR differs from standard supervised learning in the sense that instead of looking at a precise score or class for each sample, it aims to discover the best relative order for a group of items. Firstly we identified the work of ranking of scientific research papers using traditional method know as supervised learning. Secondly we evaluated and made the comparison between the supervised learning and the scalable Tensor flow library for learning to rank. Apart from solving information retrieval problems, Learning to Ranking is mostly used in areas like Natural language processing (NLP), Machine translation, Computational biology or Sentiment analysis.