Optimizing webpage relevancy using page ranking and content based ranking

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Systems for web information mining can be isolated into a few classifications as indicated by a sort of mined data and objectives that specif-ic classifications set: Web structure mining, Web utilization mining, and Web Content Mining. This paper proposes another Web Content Mining system for page significance positioning taking into account the page content investigation. The strategy, we call it Page Content Rank (PCR) in the paper, consolidates various heuristics that appear to be critical for breaking down the substance of Web pages. The page significance is resolved on the base of the significance of terms which the page contains. The significance of a term is determined concern-ing a given inquiry q and it depends on its measurable and linguistic elements. As a source set of pages for mining we utilize an arrangement of pages reacted by a web search tool to the question q. PCR utilizes a neural system as its inward order structure. We depict a usage of the proposed strategy and an examination of its outcomes with the other existing characterization framework –page rank algorithm.

     

     


  • Keywords


    Web Content Mining; Web Content Ranking; Page Ranking; Search Engine Optimization; Information Retrieval.

  • References


      [1] J. Singh Chouhan and A. Gadwal, “Improving web search user query relevance using content based page rank”, International Conference on Computer, Communication and Control (IC4), Indore, 2015, pp. 1-5. (2015)

      [2] Sudhakar, P., G. Poonkuzhali, and R. Kishore Kumar, “Content Based Ranking for Search Engines”, Proceedings of International Multi Conference of Engineers and Computer Scientists (IMECS 12).March 14-16, 2012, Hongkong. (2012)

      [3] Shalya, Nidhi, Shashwat Shukla, and Deepak Arora, “An Effective Content Based Web Page Ranking Approach”, International Journal of Engineering Science and Technology, (IJEST), Vol. 4, No. 08 (2012).

      [4] Harish Kumar B T, Vibha Lakshmikantha, Venugopal K R, “Content Based Web Page Re-Ranking Using Relevancy Algorithm” Journal of Electronics and Communication Engineering Research, Vol.2, No.7, pp.1-8. (2014)

      [5] Pokorny, Jaroslav, and Jozef Smizansky, “Page content rank: an approach to the web content mining”, Proceedings of the IADIS International Conference on Applied Computing, Vol. 2, pp. 22-25. (2005)

      [6] Baeza-Yates, Ricardo, and Berthier Ribeiro-Neto, “ACM press”, Modern Information Retrieval, Addison-Wesley (1999).

      [7] Kosala, Raymond, and Hendrik Blockeel, “Web mining research: A survey”, ACM Sigkdd Explorations Newsletter 2, No. 1, pp. 1-15. (2000)

      [8] Frikh, Bouchra, Brahim Ouhbi, and Amine Ameur, “A comparative study of link analysis algorithms for information retrieval”, Next Generation Networks and Services (NGNS), 2012, pp. 54-58. IEEE, (2012)

      [9] Van Meteren, Robin, and Maarten Van Someren, “Using content-based filtering for recommendation”, Proceedings of the Machine Learning in the New Information Age: MLnet/ECML2000 Workshop, pp. 47-56. (2000)

      [10] Cooley, Robert, Bamshad Mobasher, and Jaideep Srivastava, “Web mining: Information and pattern discovery on the world wide web”, Proceedings of Ninth IEEE International Conference on Tools with Artificial Intelligence, 1997, pp. 558-567. IEEE, (1997)

      [11] Selvan, Mercy Paul, A. Chandra Sekar, and A. Priya Dharshini, “Survey on web page ranking algorithms”, International Journal of Computer Applications, Vol. 41, No. 19 (2012).

      [12] Rani, Seema, and Upasana Garg, “A Review Paper on Web Page Ranking Algorithms”, International Journal of Engineering and Computer Science, Vol. 3, No. 8, pp. 7946-7949. (2014).


 

View

Download

Article ID: 12220
 
DOI: 10.14419/ijet.v7i2.7.12220




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.