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Web Page Recommendations Based Web Navigation Prediction

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Web Recommendations Systems

Abstract

A huge amount of user request data is generated in Web log. Predicting users’ future requests based on previously visited pages is important for Web page recommendation, reduction of latency and on-line advertising. These applications compromise with prediction accuracy and modelling complexity. In this chapter, a Web Navigation Prediction Framework for Web page Recommendation (WNPWR) which creates and generates a classifier based on sessions as training examples is proposed. As sessions are used as training examples, they are created by calculating the average time on visiting Web pages rather than the traditional method which uses 30 min as default time-out. The proposed method uses standard benchmark datasets to analyse and compare our framework with two-tier prediction framework. Simulation results show that our generated classifier framework WNPWR outperforms two-tier prediction framework in prediction accuracy and time.

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References

  1. S. Chimphlee, W. Chimphlee, N. Salim, M.S.B. Ngadiman, Using hybrid markov model for web access prediction. J. Inf. Technol. 3(3), 86–91 (2012)

    Google Scholar 

  2. J. Borges, M. Levene, Evaluating variable-length markov chain models for analysis of user web navigation sessions. IEEE Trans. Knowl. Data Eng. 19(4), 441–452 (2007)

    Article  Google Scholar 

  3. M. Deshpande, G. Karypis, Selective markov models for predicting web-page accesses. ACM Trans. Internet Technol. 4(2), 163–184 (2004)

    Article  Google Scholar 

  4. A. Guerbas, O. Addam, O. Zaarour, M. Nagi, A. Elhajj, M. Ridley, R. Alhajj, Effective web log mining and online navigational pattern prediction. J. Knowl. Based Syst. 49(2), 50–62 (2013)

    Article  Google Scholar 

  5. C. Dimopoulos, C. Makris, Y. Panagis, E. Theodoridis, A. Tsakalidis, A web page usage prediction scheme using sequence indexing and clustering techniques. J. Data Knowl. Eng. 69(4), 371–382 April (2010)

    Google Scholar 

  6. C.-H. Lee, Y.-L. Lo, Y.-H. Fu, A novel prediction model based on hierarchical characteristics of web site. Int. J. Expert. Syst. Appl. 38(4), 3422–3430 (2011)

    Article  Google Scholar 

  7. K. Dembczyński, W. Kotłowski, M. Sydow, Effective prediction of web user behavior with user-level models. J. Fundam. Inform. 89(3), 189–206 (2008)

    MATH  Google Scholar 

  8. C. Liu, R.W. White, S. Dumais, Understanding web browsing behaviors through Weibull analysis of dwell time, in The Proceedings of 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’10 (2010), pp. 379–386

    Google Scholar 

  9. F.K.H. Phoa, J. Sanchez, Modelling the browsing behavior of world wide web users. Open J. Stat. 3(2), 145–154 (2013)

    Article  Google Scholar 

  10. R.W. White, P. Bailey, L. Chen, Predicting user interests from contextual information, in The Proceedings of International ACM SIGIR Conference on Research and Development in Information Retrieval (2009), pp. 363–370

    Google Scholar 

  11. P. Thwe, Proposed approach for web page access prediction using popularity and similarity based pagerank algorithm. Int. J. Sci. Technol. Res. 2(3), 240–246 (2013)

    Google Scholar 

  12. F. Khali, J. Li, H. Wang, Integrating recommendation models for improved web page pediction accuracy, in The Proceedings of the 31st Australasian Conference on Computer Science, ACSC ’08 (2008), pp. 91–100

    Google Scholar 

  13. M.A. Awad, L.R. Khan, Web navigation prediction using multiple evidence combination and domain knowledge. IEEE Trans. Syst., Man Cybern.-Part A: Syst. Hum. 37(6), 1054–1062 (2007)

    Google Scholar 

  14. R. Dutta, A. Kundu, D. Mukhopadhyay, Clustering-based web page prediction. Int. J. Knowl. Web Intell. 2(4), 257–271 (2011)

    Article  Google Scholar 

  15. M.A. Awad, I. Khalil, Prediction of user’s web-browsing behavior: application of markov model. IEEE Trans. Syst., Man Cybern.-Part B: Cybern. 42(4), 1131–1142 (2012)

    Google Scholar 

  16. V.S. Tseng, K.W. Lin, Efficient mining and prediction of user behavior patterns in mobile web systems. J. Inf. Softw. Technol. 48(6), 357–369 (2006)

    Article  Google Scholar 

  17. G. Zhao, W. Lai, Predicting user behavior in mobile internet based on random walk. J. Comput. Inf. Syst. 9(22), 9157–9164 (2013)

    Google Scholar 

  18. C.-M. Huang, J.J.-C. Ying, V.S. Tseng, Mining users’ behaviors and environments for semantic place prediction, in Mobile Data Challenge Workshop (2012)

    Google Scholar 

  19. M. Silic, G. Delac, I. Krka, S. Srbljic, Scalable and accurate prediction of availability of atomic web services. IEEE Trans. Serv. Comput. 7(2), 252–264 (2014)

    Article  Google Scholar 

  20. J. Huang, R.W. White, Parallel browsing behavior on the web, in The Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, HT’10 (2010), pp. 13–18

    Google Scholar 

  21. S. Goel, J.M. Hofman, M.I. Sirer, Who does what on the web: a large-scale study of browsing behavior, in The Proceedings of 6th AAAI International Conference on Weblogs and Social Media, AAAI’ 12 (2012), pp. 130–137

    Google Scholar 

  22. Z. Cheng, B. Gao, T.-Y. Liu, Actively predicting diverse search intent from user browsing behaviors, in The Proceedings of 19th International Conference on World Wide Web, WWW ’10 (2010), pp. 221–230

    Google Scholar 

  23. Y. Zhang, W. Chen, D. Wang, Q. Yang, User-click modeling for understanding and predicting search-behavior, in The Proceedings of 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’11 (2011), pp. 1388–1396

    Google Scholar 

  24. X. Tian, Y. Lu, L. Yang, Query difficulty prediction for web image search. IEEE Trans. Multimed. 14(4), 951–962 (2012)

    Article  MathSciNet  Google Scholar 

  25. J. Yu, Y. Rui, D. Tao, Click prediction for web image reranking using multimodal sparse coding. IEEE Trans. Image Process. 23(5), 2019–2032 (2014)

    Article  MathSciNet  Google Scholar 

  26. R. Cooley, B. Mobasher, J. Srivastava, Data preparation for mining world wide web browsing patterns. J. Knowl. Inf. Syst. 1(1), 5–32 (1999)

    Article  Google Scholar 

  27. C.E. Dinuca, D. Ciobanu, Improving the session identification using the mean time. Int. J. Math. Model. Methods Appl. Sci. 6(2), 265–272 (2012)

    Google Scholar 

  28. S. Brin, L. Page, The anatomy of a large-scale hypertextual web search engine. J. Comput. Netw. 56(18), 3825–3833 (2012)

    Article  Google Scholar 

  29. Internet Traffic Archive, http://ita.ee.lbl.gov/html/contrib/Sask-HTTP.html

  30. C.-C. Chang, C.-J. Lin, LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–39 (2011)

    Article  Google Scholar 

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Authors and Affiliations

  1. Bangalore University, Jnana Bharathi, Bengaluru, 560056, India

    K. R. Venugopal

  2. BNM Institute of Technology, Banashankari, Bengaluru, 560070, India

    Sejal Santosh Nimbhorkar

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  1. K. R. Venugopal
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  2. Sejal Santosh Nimbhorkar
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Correspondence to K. R. Venugopal .

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Venugopal, K.R., Santosh Nimbhorkar, S. (2020). Web Page Recommendations Based Web Navigation Prediction. In: Web Recommendations Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-2513-1_7

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  • DOI: https://doi.org/10.1007/978-981-15-2513-1_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2512-4

  • Online ISBN: 978-981-15-2513-1

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