A Novel Web Page Recommender System for Anonymous Users Based on Clustering of Web Pages

  • Rajnikant Wagh
  • Jayantrao Patil


Information overload is major problem of
today’s internet use. Users frequently get much more
information than needed. Web Personalization and
recommender systems are becoming popular now days to
overcome this problem. We have proposed a novel web page
recommender system to improve browsing experience of
anonymous users. We have used web usage mining technique
for personalizing a web site and recommendation of web pages.
This technique uses preprocessing, analysis for finding the
relationship among web pages, clustering and classification
phases of data mining. The preprocessing step aims at
maintaining consistency in dataset. We have modelled the
relationship among web pages with novel measures of distance
matrix, occurrence frequency matrix and relationship matrix.
A virtual graph is created corresponding to the relationship
matrix to show the relationship among web pages. The
proposed method partitions the virtual graph into various
clusters i.e. navigation patterns by proposing an enhanced
depth first search algorithm. It is a graph based partitioning
algorithm. We classify the active user under consideration into
one of the cluster by using LCS algorithm. Finally, we used a
threshold value to recommend only optimum number of web
pages. Use of novel measures for finding the relationship, use of
threshold values at the time of formation of clusters as well as
at the time of recommendation of web pages gives us better
results in term of improved visit coherence, accuracy, coverage
and F1 measures. We get max. 61% accuracy, 49.2% avg.
coverage and 28.87% avg. F1 values in the recommendation of
web pages. Similarly, we get 57.8% avg. visit coherence in the
formation of clusters and a minimum of 15 % outliers.

Keywords: Web Personalization, Recommender Systems, Web Usage Mining, Clustering, Classification


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Wagh, R., & Patil, J. (2019). A Novel Web Page Recommender System for Anonymous Users Based on Clustering of Web Pages. Asian Journal For Convergence In Technology (AJCT). Retrieved from http://www.asianssr.org/index.php/ajct/article/view/735