The week's pick
Reseach Article
Improved Weight based Web Page Ranking Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 182 - Number 29 |
| Year of Publication: 2018 |
| Authors: Megha Bhawsar, Shraddha Kumar |
10.5120/ijca2018918080
|
Megha Bhawsar, Shraddha Kumar . Improved Weight based Web Page Ranking Algorithm. International Journal of Computer Applications. 182, 29 ( Nov 2018), 1-5. DOI=10.5120/ijca2018918080
Abstract
Web page ranking is a technique to optimize the search engines for finding the more relevant content according to the user search query. In this context the web pages are evaluated in such manner by which the appropriate position of a web page is decided in a World Wide Web graph. In literature several web page ranking techniques are available but most of them requires significant amount of time and memory resources for evaluation of web page rank. Therefore, the proposed work is motivated for designing and development of the efficient technique of web page rank. The proposed web page rank evaluation technique is a weight-based page rank technique. The weights are basically the page rank value based on which the web pages are organized on web graph. To compute the web page rank in the proposed technique the web page TF (Term Frequency), IDF (Inverse Document Frequency), Inbound and Outbound links are considered. Therefore, the proposed technique utilizes the techniques of web structure mining and web content mining for developing web page rank of a given web page. After computation of the considered factors the combined weight for all web pages are computed and most higher weight-based page is ranked first for any given query. The implementation of the proposed web page rank computation technique is performed on visual studio technology. After implementation of the proposed technique the performance of system is measured in terms of time and space complexity. In addition of that the experimentation is extended for finding the optimal weighted factor therefore it is concluded that the weighting factors 0.25, 0.25, 0.25, 0.25 is the most suitable weighting factor for web page rank calculation.
References
- Claudia Elena Dinuca, “Web Structure Mining”, Annals of the University of Petroşani, Economics, 11(4), 2011, 73-84
- Claudia Elena Dinuca, Dumitru Ciobanu, “Web Content Mining”, Annals of the University of Petroşani, Economics, 12(1), 2012, 85-92
- Rini John, Sharvari S. Govilkar, “Survey of Information Retrieval Techniques for Web using NLP”, International Journal of Computer Applications (0975 – 8887) Volume 135 – No.8, February 2016
- Renu Gupta, Ankita Shah, Amit Thakkar, Kamlesh Makvana, “A Survey on Various Web Page Ranking Algorithms”, COMPUSOFT, An international journal of advanced computer technology, 5 (1), January - 2016 (Volume-V, Issue-I)
- Wanying Chiu, Kun Lu, “Random Walk on Co-word Network: Ranking Terms Using Structural Features”, ASIST 2015, November 6-10, 2015, St. Louis, MO, USA.
- Chengxiang Zhai, John Lafferty, “A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval”, SIGIR’01, September 9-12, 2001, New Orleans, Louisiana, USA, Copyright 2001 ACM 1-58113-331-6/01/0009
- R. Campos, G. Dias, A. M. Jorge, A. Jatowt, “Survey of Temporal Information Retrieval and Related Applications”, ACM Computing Surveys, Vol. 6, No. 3, Article 9, Pub. date: April 2014.
- Yue Wang, Dawei Yin, Luo Jie, Pengyuan Wang, Makoto Yamada, Yi Chang, Qiaozhu Mei, “Beyond Ranking: Optimizing Whole-Page Presentation”, WSDM’16, February 22–25, 2016, San Francisco, CA, USA. c 2016 ACM. ISBN 978-1-4503-3716-8/16/02
- Maria Pershina, Yifan He, Ralph Grishman, “Personalized Page Rank for Named Entity Disambiguation”, The 2015 Annual Conference of the North American Chapter of the ACL, pages 238–243, Denver, Colorado, May 31 – June 5, 2015. c 2015 Association for Computational Linguistics
- Turgay Celik, “Spatial Mutual Information and PageRank-Based Contrast Enhancement and Quality-Aware Relative Contrast Measure”, IEEE Transactions on Image Processing, Vol 25, No. 10, October 2016
- Peter Lofgren, Siddhartha Banerjee, Ashish Goel, “Personalized PageRank Estimation and Search: A Bidirectional Approach”, WSDM’16, February 22–25, 2016, San Francisco, CA, USA. c 2015 Copyright held by the owner/author(s). Publication rights licensed to ACM. ISBN 978-1-4503-3716-8/16/02.c.
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