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Efficiency measures for ranked pages by Markov Chain Principle

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Abstract

More often, the user likes to visit the web documents that appear in few top excellent responses to the list of links provided by the search engine and these results are the most likely accurate results to the search query. The Information Retrieval by Search Engine helps in retrieving the most relevant pages for query. In this paper, we propose an ideal technique for link analysis by taking web graph structure and we focus around the ranking of such links. The relevancy of the links is evaluated by using Markov Chain Principle and also query keyword occurrence is given a weight-age to the overall ranking of the links. The term proximity and discounted cumulative gain are used to simulate results and the scores show that the proposed methodology efficiently enhances the ranking of the web pages.

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References

  1. L. Page, S. Brin, R. Motwani, T. Wino grad (1999) The PageRank Citation Ranking: Bringing order to the Web. Tech Rep, Stanford Digital Libraries SIDL-WP1999–0120

  2. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 33(1–7):107–117

    Article  Google Scholar 

  3. Haveliwala TH (2003) Topic-sensitive pagerank: a context-sensitive ranking algorithm for web search. IEEE Trans Knowl Data Eng 15(4):784–796

    Article  Google Scholar 

  4. Nandnee Jain, Upendra Dwivedi (2015) Ranking web pages based on user interaction time”, International Conference on Advances in Computer Engineering and Applications, IEEE Xplore, pp. 35–41, March 19–20

  5. Kollias G, Gallopoulos E, Grama A (2014) Surfing the network for ranking by multidamping. IEEE Trans Knowl Data Eng 26(9):2323–2336

    Article  Google Scholar 

  6. Harmunish Taneja, Richa Gupta (2010) Web Information Retrieval using Query Independent Page Rank Algorithm, IEEE Computer Society Proceedings International Conference on Advances in Computer Engineering, pp. 178–182, June 20–21

  7. M. Usha, N. Nagadeepa (2018) Combined Two Phase Page Ranking Algorithm for Sequencing the Web Pages, Proceedings of the Second International Conference on Inventive Systems and Control, IEEE Xplore Compliant, pp. 876–880, January 19–20

  8. Ishii H, Tempo R (2014) The pagerank problem, multiagent consensus, and web aggregation: a systems and control viewpoint. IEEE Control Syst Mag 34(3):34–53

    Article  MathSciNet  Google Scholar 

  9. Chakrabarti S, Dom B, Gibson D, Kleinberg J, Kumar R, Raghavan P, Rajagopalan S, Tomkins A (1999) Mining the link structure of the world wide web. IEEE Comput Soc Press 32(8):60–67

    Article  Google Scholar 

  10. Vojnovic M, Cruise J, Gunawardena D, Marbach P (2009) Ranking and suggesting popular items. IEEE Trans Knowl Data Eng 21(8):1133–1146

    Article  Google Scholar 

  11. Manning CD, Raghavan P, Schütze H (2009) An introduction to information retrieval. Cambridge University Press, Cambridge, pp 465–469

    MATH  Google Scholar 

  12. Bruce Croft W, Metzler D, Strohman T (2015) Search engines informational retrieval in practice. Pearson Education, London, pp 25–26

    Google Scholar 

  13. Peng Lu, Cong X (2015) The research on webpage ranking algorithm based on topic-expert documents. Recent Adv Inf Commun Technol Adv Intell Syst Comput 361:195–204

    Google Scholar 

  14. Guo C, Zhonglian Du, Kou X (2018) Products ranking through aspect-based sentiment analysis of online heterogeneous reviews. J Syst Sci Syst Eng 27(5):542–558

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Meerut Institute of Engineering and Technology, Dr. A P J Abdul Kalam Technical University, Meerut, 250005, Uttar Pradesh, India

    Swati Jain & Mukesh Rawat

Authors
  1. Swati Jain
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  2. Mukesh Rawat
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Correspondence to Swati Jain.

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Jain, S., Rawat, M. Efficiency measures for ranked pages by Markov Chain Principle. Int. j. inf. tecnol. 14, 1099–1106 (2022). https://doi.org/10.1007/s41870-020-00549-y

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  • DOI: https://doi.org/10.1007/s41870-020-00549-y

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