Predicting Ranking for Scientific Research Papers using Scalable TensorFlow Library and Learning to Rank
Sarabu Joshna
Sarabu Joshna, Assistant Professor in the Department of Computer Science Engineering ,Bharath Institute of Engineering And Technology, Mangalpally, Ibrahimpatnam, Hyderabad, India.
Manuscript received on January 18, 2020. | Revised Manuscript received on January 26, 2020. | Manuscript published on February 10, 2020. | PP: 2183-2188 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1693029420/2020©BEIESP | DOI: 10.35940/ijitee.D1693.029420
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: TensorFlow, PageRank, Binarization, Heatmap.
Scope of the Article: Operational Research