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Multi-criteria-Based Page Ranking Using Metaheuristic Swarm Optimization

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Advanced Computational and Communication Paradigms (ICACCP 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 535))

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Abstract

The size of data on the internet has grown enormously and also growing continuously. Finding any information for a user in response to query has become a challenging task. In this, search engines are playing a vital role. Yet, these are unable to a retrieve the relevant information. These search engines use some limited criteria to fetch the results in response to the user's query after ranking and indexing the pages. In this paper, a multi-criteria-based approach has been proposed to rank web pages. These criteria include keywords, user access count, unique visitors, stay time, hubs, and authority values. The proposed approach uses particle swarm optimization to find the optimal results. And, this algorithm has been compared with some existing algorithms based on iterative improvement and genetic algorithm. It has been observed that the proposed approach is able to find the most relevant top-ranked pages.

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References

  1. Kumar R, Kumar S (2021) A study on web mining classification and approaches. In: International conference on recent trends in communication and electronics recent trends in communication and electronics, 1st edn. CRC Press, pp 72–75. https://doi.org/10.1201/9781003193838-20. (First Published: 2021)

  2. Agarwal A, Nanavati N (2016) Association rule mining using hybrid GA-PSO for multi-objective optimization. In: 2016 IEEE international conference on computational intelligence and computing research (ICCIC), pp 1–7. https://doi.org/10.1109/ICCIC.2016.7919571.n

  3. Basari ASH, Hussin B, Ananta IG, Zeniarja J (2013) Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization. Procedia Eng 53:453–462. ISSN 1877-7058. https://doi.org/10.1016/j.proeng.2013.02.059

  4. Liangtu S, Xiaoming Z (2007) Web text feature extraction with particle swarm optimization summary

    Google Scholar 

  5. Kundra K, Kaur U, Singh D (2015) Efficient web log mining and navigational prediction with EHPSO and scaled markov model. Smart Innov Syst Technol 33:529–543. https://doi.org/10.1007/978-81-322-2202-6_48

    Article  Google Scholar 

  6. Chen J, Zhang H (2007) Research on application of clustering algorithm based on PSO for the web usage pattern. In: 2007 International conference on wireless communications, networking and mobile computing, WiCOM 2007, pp 3705–3708. https://doi.org/10.1109/WICOM.2007.916

  7. Rajdeepa B, Sumathi P (2014) Web structure mining for users based on a hybrid GA/PSO approach. J Theor Appl Inf Technol 70:573–578

    Google Scholar 

  8. Zhan HQ (2010) A improved topics search algorithm based on PSO strategy for web mining. KEM.https://doi.org/10.4028/www.scientific.net/kem.439-440.1481

  9. Ghorpade-Aher J, Bagdiya R (2014) A review on clustering web data using PSO. Int J Comput Appl 108:31–36

    Google Scholar 

  10. Mosa MA (2020) A novel hybrid particle swarm optimization and gravitational search algorithm for multi-objective optimization of text mining. Appl Soft Comput 90:106189. ISSN 1568-4946. https://doi.org/10.1016/j.asoc.2020.106189

  11. Wang Z, Zhang Q, Zhang D (2007) A PSO-based web document classification algorithm, pp 659–664. https://doi.org/10.1109/SNPD.2007.72

  12. Alam S, Dobbie G, Riddle P (2008) Particle swarm optimization based clustering of web usage data, pp 451–454. https://doi.org/10.1109/WIIAT.2008.292

  13. Liu Z, Zhu P, Chen W, Yang R-J (2015) Improved particle swarm optimization algorithm using design of experiment and data mining techniques. Struct Multidiscip Optim 52. https://doi.org/10.1007/s00158-015-1271-7

  14. Panda AK, Dehuri SN, Patra MR, Mitra A (2011) A survey on swarm and evolutionary algorithms for web mining applications, vol7077, pp 9–16. https://doi.org/10.1007/978-3-642-27242-4_2

  15. Kothari V, AnuradhaJ, Shah S, Mittal P (2012) A survey on particle swarm optimization in feature selection. https://doi.org/10.1007/978-3-642-29216-3_22

  16. Grosan C, Abraham A, Chis M (2007) Swarm intelligence in data mining. https://doi.org/10.1007/978-3-540-34956-3_1

  17. Kumar S, Kumar R (2021) WDPMA: an MA-based model for web documents prioritization. Int J Inf Technol Web Eng 16:1–24. https://doi.org/10.4018/IJITWE.2021040101

    Article  Google Scholar 

  18. Chaudhary K, Gupta SK (2014) Prioritizing web links based on web usage and content data. In: 2014 International conference on issues and challenges in intelligent computing techniques (ICICT), pp 546–551. https://doi.org/10.1109/ICICICT.2014.6781340

  19. Gupta SK, Singh D, Doegar A (2016) Web documents prioritization using genetic algorithm. In: 2016 3rd International conference on computing for sustainable global development (INDIACom), pp 3042–3047

    Google Scholar 

  20. Sarzaeim P, Bozorg-Haddad O, Chu X (2018) Teaching-learning-based optimization (TLBO) algorithm. https://doi.org/10.1007/978-981-10-5221-7_6

  21. Zou F, Chen D, Xu Q (2018) A survey of teaching-learning-based optimization. Neurocomputing335. https://doi.org/10.1016/j.neucom.2018.06.076

  22. Kumar S, Anand C (2020) Comparative study of web page ranking algorithms. Int J Adv Sci Technol 29(5s):322–331

    Google Scholar 

  23. Lawrence P, Sergey B, Rajeev M, Terry W (1998) The PageRank citation ranking: bringing order to the web

    Google Scholar 

  24. Xing W, Ghorbani A (2004) Weighted PageRank algorithm. In: Proceedings second annual conference on communication networks and services research, pp 305–314

    Google Scholar 

  25. Egri G, Bayrak C (2014) The role of search engine optimization on keeping the user on the site. Procedia Comput 36:335–342

    Article  Google Scholar 

  26. Fujimura K, Inoue T, Sugisaki M (2005) The Eigen Rumor algorithm for ranking blogs. In: 2nd Annual workshop on the weblogging ecosystem: aggregation, analysis and dynamics

    Google Scholar 

  27. Alhaidari F, Alwarthan S, Alamoudi A (2020) User preference based weighted page ranking algorithm. In: 3rd International conference on computer applications & information security (ICCAIS), pp 1–6

    Google Scholar 

  28. Hao Z, Qiumei P, Hong Z, Zhihao S (2015) An improved pagerank algorithm based on web content. In: 14th International symposium on distributed computing and applications for business engineering and science (DCABES), pp 284–287

    Google Scholar 

  29. Attia M, Abdel-Fattah MA, Khedr AE (2022) A proposed multi criteria indexing and ranking model for documents and web pages on large scale data. J King Saud Univ-Comput Inf Sci 34(10):8702–8715

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India

    Santosh Kumar

  2. ABES Engineering College, Ghaziabad, Uttar Pradesh, 201009, India

    Sanjai Mohan Verma

Authors
  1. Santosh Kumar
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  2. Sanjai Mohan Verma
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Corresponding author

Correspondence to Santosh Kumar .

Editor information

Editors and Affiliations

  1. Department of Computer Applications, Sikkim Manipal Institute of Technology, Rangpo, Sikkim, India

    Samarjeet Borah

  2. Department of Electrical Engineering, IIT Delhi, Delhi, India

    Tapan K. Gandhi

  3. Department of Computer Science, University of Milan, Milano, Milano, Italy

    Vincenzo Piuri

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Kumar, S., Verma, S.M. (2023). Multi-criteria-Based Page Ranking Using Metaheuristic Swarm Optimization. In: Borah, S., Gandhi, T.K., Piuri, V. (eds) Advanced Computational and Communication Paradigms . ICACCP 2023. Lecture Notes in Networks and Systems, vol 535. Springer, Singapore. https://doi.org/10.1007/978-981-99-4284-8_3

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