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An Effective Clustering-Based Web Page Recommendation Framework for E-Commerce Websites

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Abstract

The burgeoning e-commerce market has presented companies with the opportunity to grow their businesses through online platforms. But, the researchers have concluded that just 2.86% of e-commerce website visits lead to a purchase and one of the reasons for this missed opportunity is an unpleasant website browsing experience. Therefore, a pleasant browsing experience is the need of the hour whereby the web page recommendation systems (WPRS) provide high-quality navigation experience by providing suggestions about the web pages of interest and by taking the website users to their desired web pages in fewer clicks. In this context, this paper presents a method to improve the browsing experience of the website users by proposing two hybrid algorithms based on clustering for web page recommendation systems, namely a hybrid partitioning-based heuristic sequence clustering (HSC) algorithm inspired from K-medoid and DBSCAN algorithms and a hybrid tree-based sequence clustering (TSC) algorithm inspired from B-Trees and BIRCH algorithm. The testing has been performed using CTI, BMSWebView1, BMSWebView2 and MSNBC datasets. To measure the performance, the algorithm considered for the study has been evaluated using parameters like precision, recall, F1 measures and execution time. Also, an in-depth comparative analysis of state-of-the-art web page recommendation systems with the recommendation system considered for the study has been done. The results indicate that the proposed clustering-based framework was able to generate superior results than the other classes of algorithms.

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References

  1. Adeniyi DA, Wei Z, Yongquan Y. Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method. Appl Comput Inform. 2016;12(1):90–108.

    Google Scholar 

  2. Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng. 2005;17(6):734–49.

    Google Scholar 

  3. Avazpour I, Pitakrat T, Grunske, L, Grundy J. Dimensions and metrics for evaluating recommendation systems. In: Recommendation systems in software engineering. Berlin: Springer; 2014. pp. 245–73.

  4. Baraglia R, Silvestri F. Dynamic personalization of web sites without user intervention. Commun ACM. 2007;50(2):63–7.

    Google Scholar 

  5. Bharti PM, Raval TJ. Improving web page access prediction using web usage mining and web content mining. In: 2019 third international conference on electronics, communication and aerospace technology (ICECA); 2019. pp. 1268–73.

  6. Bhatta R, Ezeife CI, Butt MN. Mining sequential patterns of historical purchases for e-commerce recommendation. In: International conference on big data analytics and knowledge discovery. Champaign: Springer; 2019. pp. 57–72.

  7. Bhavithra J, Saradha A. Personalized web page recommendation using case-based clustering and weighted association rule mining. Clust Comput. 2019;22(3):6991–7002.

    Google Scholar 

  8. Bobadilla J, Ortega F, Hernando A, Gutiérrez A. Recommender systems survey. Knowl-Based Syst. 2013;46:109–32.

    Google Scholar 

  9. Carmona CJ, Ramírez-Gallego S, Torres F, Bernal E, del Jesús MJ, García S. Web usage mining to improve the design of an e-commerce website: OrOliveSur.com. Expert Syst Appl. 2012;39(12):11243–9.

    Google Scholar 

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

    Google Scholar 

  11. Dai H, Mobasher B. Integrating semantic knowledge with web usage mining for personalization. In: Scime A, editor. Web mining: application and techniques. IGI Global: Hershey, PA, USA; 2005. pp. 205–32.

  12. Ezeife, C. I., & Lu, Y. (2005). Mining web log sequential patterns with position coded pre-order linked wap-tree. Data Mining and Knowledge Discovery, 10(1), 5–38.

    Article  MathSciNet  Google Scholar 

  13. Facca FM, Lanzi PL. Mining interesting knowledge from weblogs: a survey. Data Knowl Eng. 2005;53(3):225–41.

    Google Scholar 

  14. Forsati R, Moayedikia A, Shamsfard M. An effective Web page recommender using binary data clustering. Inform Retriev J. 2015;18(3):167–214.

    Google Scholar 

  15. Fournier-Viger P, Gomariz A, Soltani A, Gueniche T, Wu CW, Tseng VS. SPMF: a Java open source pattern mining library. J Mach Learn Res. 2014;15:3389–93.

    MATH  Google Scholar 

  16. Fournier-Viger P, Wu CW, Tseng VS, Cao L, Nkambou R. Mining partially-ordered sequential rules common to multiple sequences. IEEE Trans Knowl Data Eng. 2015;27(8):2203–16.

    Google Scholar 

  17. Göksedef M, Gündüz-Öğüdücü Ş. Combination of Web page recommender systems. Expert Syst Appl. 2010;37(4):2911–22.

    Google Scholar 

  18. Han J, Kamber M, Pei J. Data mining: concepts and techniques. Amsterdam: Elsevier; 2011.

    MATH  Google Scholar 

  19. Iváncsy R, Vajk I. Frequent pattern mining in web log data. Acta Polytech Hungarica. 2006;3(1):77–90.

    Google Scholar 

  20. Jalali M, Mustapha N, Sulaiman MN, Mamat A. WebPUM: a Web-based recommendation system to predict user future movements. Expert Syst Appl. 2010;37(9):6201–12.

    Google Scholar 

  21. Karaboga D, Basturk B. On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput. 2008;8(1):687–97.

    Google Scholar 

  22. Katarya R. Movie recommender system with metaheuristic artificial bee. Neural Comput Appl. 2018;30(6):1983–90.

    Google Scholar 

  23. Katarya R, Verma OP. An effective web page recommender system with fuzzy c-mean clustering. Multimedia Tools Appl. 2017;76(20):21481–96.

    Google Scholar 

  24. Kauffman R, Wood C. Revolutionary research strategies for e-business: A philosophy of science view in the age of the Internet. Minnesota: University of Minnesota; 2006.

  25. Guerbas, A., Addam, O., Zaarour, O., Nagi, M., Elhajj, A., Ridley, M., & Alhajj, R. (2013). Effective web log mining and online navigational pattern prediction. Knowledge-Based Systems, 49, 50-62.

    Google Scholar 

  26. Kazienko P, Kolodziejski P. Personalized integration of recommendation methods for e-commerce. Int J Comput Sci Appl. 2006;3(3):12–26.

    Google Scholar 

  27. Kim KJ, Ahn H. A recommender system using GA K-means clustering in an online shopping market. Expert Syst Appl. 2008;34(2):1200–9.

    Google Scholar 

  28. Kim Y. Streaming association rule (SAR) mining with a weighted order-dependent representation of Web navigation patterns. Expert Syst Appl. 2009;36(4):7933–46.

    Google Scholar 

  29. Kumar KM, Reddy ARM. A fast DBSCAN clustering algorithm by accelerating neighbor searching using Groups method. Pattern Recogn. 2016;58:39–48.

    Google Scholar 

  30. Law TJ. 19 Powerful e-commerce statistics that will guide your strategy in 2020. 2019. https://www.oberlo.in/blog/ecommerce-statistics-guide-your-strategy.

  31. Lazcorreta E, Botella F, Ferna´ndez-Caballero A. Towards personalized recommendation by two-step modified Apriori data mining algorithm. Expert Syst Appl. 2008;35:1422–9.

    Google Scholar 

  32. Lee JH, Shiu WK. An adaptive website system to improve efficiency with web mining techniques. Adv Eng Inform. 2004;18(3):129–42.

    Google Scholar 

  33. Lee KS, Geem ZW. A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng. 2005;194(36):3902–33.

    MATH  Google Scholar 

  34. Liao CL, Lee SJ. A clustering based approach to improving the efficiency of collaborative filtering recommendation. Electron Commer Res Appl. 2016;18:1–9.

    Google Scholar 

  35. Liao SH, Chen YJ, Lin YT. Mining customer knowledge to implement online shopping and home delivery for hypermarkets. Expert Syst Appl. 2011;38:3982–91.

    Google Scholar 

  36. Li J, Zaïane OR. Combining usage, content, and structure data to improve web site recommendation. In: Proceedings of fifth international conference on electronic commerce and web, 2004. pp. 305–15.

  37. Lipschutz S, Pai GAV. Data Structures. Noida: Tata McGraw-Hill; 2006.

    Google Scholar 

  38. Lin CC. Optimal web site reorganization considering information overload and search depth. Eur J Oper Res. 2006;173:839–48.

    MATH  Google Scholar 

  39. Liu H, Kešelj V. Combined mining of Web server logs and web contents for classifying user navigation patterns and predicting users’ future requests. Data Knowl Eng. 2007;61(2):304–30.

    Google Scholar 

  40. Lorbeer B, Kosareva A, Deva B, Softić D, Ruppel P, Küpper A. Variations on the clustering algorithm BIRCH. Big Data Res. 2018;11:44–53.

    Google Scholar 

  41. Lu L, Dunham M, Meng Y. Discovery of significant usage patterns from clusters of clickstream data. In: Proceedings of WebKDD, 2005. pp. 21–4.

  42. Lu J, Wu D, Mao M, Wang W, Zhang G. Recommender system application developments: a survey. Decis Support Syst. 2015;74:12–32.

    Google Scholar 

  43. Mabroukeh NR, Ezeife CI. A taxonomy of sequential pattern mining algorithms. ACM Comput Surv (CSUR). 2010;43(1):3.

    Google Scholar 

  44. Markov Z, Larose DT. Data mining the web. In: Uncovering the patterns in web content, structure and usage. New York: Wiley-Interscience, 2007.

  45. Masseglia F, Teisseire M, Poncelet P. Sequential pattern mining. Encycl Data Wareh Min. 2005;46:1028–32.

    Google Scholar 

  46. Minjing P, Xinglin L, Ximing L, Mingliang Z, Xianyong Z, Xiangming D, Mingfen W. Recognizing intentions of E-commerce consumers based on ant colony optimization simulation. J Intell Fuzzy Syst. 2017;33(5):2687–97.

    Google Scholar 

  47. Mirjalili S. Genetic algorithm. In: Evolutionary algorithms and neural networks, Champaign: Springer. pp. 43–55.

  48. Monfared FD. A novel web page recommender using data automatic clustering and Markov process. SN Appl Sci. 2019;1(12):1719.

    Google Scholar 

  49. Muralikrishnan V, Janakiraman B. Firefly based optimization in web page recommendation system. In: 2018 international conference on communication, computing and internet of things, 2018. pp. 96–101.

  50. Nguyen TTS, Lu HY, Lu J. Web-page recommendation based on web usage and domain knowledge. IEEE Trans Knowl Data Eng. 2014;26(10):2574–87.

    Google Scholar 

  51. Pabarskaite Z, Raudys A. A process of knowledge discovery from web log data: systematization and critical review. J Intell Inform Syst. 2007;28(1):79–104.

    Google Scholar 

  52. Park DH, Kim HK, Choi IY, Kim JK. A literature review and classification of recommender systems research. Expert Syst Appl. 2012;39(11):10059–72.

    Google Scholar 

  53. Perkowitz M, Etzioni O. Towards adaptive web sites: conceptual framework and case study. Artif Intell. 2000;118(1):245–75.

    MATH  Google Scholar 

  54. Pudi V, Radha-Krishna P. Data mining. Cambridge: Oxford University Press; 2012.

    Google Scholar 

  55. Real EM, do Carmo Nicoletti M, de Oliveira OL. A closer look into sequential clustering algorithms and associated post-processing refinement strategies. Int J Innov Comput Appl. 2014;6(1):1–12.

    Google Scholar 

  56. Sarwar B, Karypis G, Konstan JA, Riedl J. Analysis of recommendation algorithms for e-commerce. In: Proceedings of ACM E-commerce 2000 conference. Minneapolis, MN. pp. 158–67

  57. Silva JA, Faria ER, Barros RC, Hruschka ER, de Carvalho AC, Gama J. Data stream clustering: a survey. ACM Comput Surv (CSUR). 2013;46(1):13.

    MATH  Google Scholar 

  58. Singh H, Kaur M, Kaur P. Web page recommendation system based on partially ordered sequential rules. J Intell Fuzzy Syst. 2017;32(4):3009–15.

    Google Scholar 

  59. Song Q, Shepperd M. Mining web browsing patterns for E-commerce. Comput Ind. 2006;57(7):622–30.

    Google Scholar 

  60. Srivastava J, Cooley R, Deshpande M, Tan PN. Web usage mining: discovery and applications of usage patterns from web data. ACM SIGKDD Explor Newsl. 2000;1(2):12–23.

    Google Scholar 

  61. Tang L, Wang A, Xu Z, Li J. Online-purchasing behavior forecasting with a firefly algorithm-based SVM model considering shopping cart use. Eurasia J Math Sci Technol Educ. 2017;13(12):7967–83.

    Google Scholar 

  62. Tao YH, Hong TP, Su YM. Web usage mining with intentional browsing data. Expert Syst Appl. 2008;34(3):1893–904.

    Google Scholar 

  63. Tseng VS, Lin KW. Efficient mining and prediction of user behavior patterns in mobile web systems. Inf Softw Technol. 2006;48(6):357–69.

    Google Scholar 

  64. Wang Y, Dai W, Yuan Y. Website browsing aid: a navigation graph-based recommendation system. Decis Support Syst. 2008;45(3):387–400.

    Google Scholar 

  65. Wang YT, Lee AJ. Mining Web navigation patterns with a path traversal graph. Expert Syst Appl. 2011;38(6):7112–22.

    Google Scholar 

  66. Yadav S, Nagpal S. An improved collaborative filtering based recommender system using bat algorithm. Proc Comput Sci. 2018;132:1795–803.

    Google Scholar 

  67. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Berlin: Springer. pp. 65–74.

  68. Yang XS. Multiobjective firefly algorithm for continuous optimization. Eng Comput. 2013;29(2):175–84.

    Google Scholar 

  69. Zaki MJ. SPADE: an efficient algorithm for mining frequent sequences. Mach Learn. 2001;42(1):31–60.

    MATH  Google Scholar 

  70. Zdravko M, Daniel TL. Data mining the web, Uncovering patterns in Web content, structure, and usage. New Jersey: John Wiley & Sons Inc.; 2007. p. 115–32.

    MATH  Google Scholar 

  71. Zhou B, Hui SC, Chang K. An intelligent recommender system using sequential web access patterns. In: Proceedings of IEEE conference on cybernetics and intelligent systems, 2004, vol. 1. pp. 393–98.

  72. Zhou B, Hui SC, Fong ACM. Efficient sequential access pattern mining for web recommendations. Int J Knowl-Based Intell Eng Syst. 2006;10(2):155–68.

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, DAV University, Jalandhar, India

    Harpreet Singh

  2. Department of Computer Science, Guru Nanak Dev University, Amritsar, India

    Parminder Kaur

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  1. Harpreet Singh
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Singh, H., Kaur, P. An Effective Clustering-Based Web Page Recommendation Framework for E-Commerce Websites. SN COMPUT. SCI. 2, 339 (2021). https://doi.org/10.1007/s42979-021-00736-z

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