Performance Assessment of Iterative, Optimization and Non-Optimization Methods for Page Rank Aggregation
Shabnam Parveen1, R. K.Chauhan2
1Shabnam Parveen*, Research Scholar in Department of Computer Science and Application in Kurukshetra University, Kurukshetra, India.
2Dr. R.K Chauhan, Professor in the Department of Computer Science and Application in Kurukshetra University, Kurukshetra, India.
Manuscript received on December 16, 2019. | Revised Manuscript received on December 21, 2019. | Manuscript published on January 10, 2020. | PP: 1884-1888 | Volume-9 Issue-3, January 2020. | Retrieval Number: A5091119119/2020©BEIESP | DOI: 10.35940/ijitee.A5091.019320
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© 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: The annoyance of combining the ranked possibilities of many experts is an antique and particularly deep hassle that has won renewed importance in many machine getting to know, statistics mining, and information retrieval applications. Powerful rank aggregation turns into hard in actual-international situations in which the ratings are noisy, incomplete, or maybe disjoint. We cope with those difficulties by extending numerous standard methods of rank aggregation to do not forget similarity between gadgets within the diverse ranked Lists, further to their ratings. The intuition is that comparable items must obtain similar scores, given the right degree of similarity for the domain of hobby.
Keywords: Rank Aggregation, Particle Swarm Optimization, Genetic Algorithm, Robust Rank Aggregatio
Scope of the Article: Algorithm Engineering