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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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  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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