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Reseach Article

An Efficient Web Recommender System for Web Logs

by B. M. Vidyavathi, Haseena Begum
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 152 - Number 3
Year of Publication: 2016
Authors: B. M. Vidyavathi, Haseena Begum
10.5120/ijca2016911795

B. M. Vidyavathi, Haseena Begum . An Efficient Web Recommender System for Web Logs. International Journal of Computer Applications. 152, 3 ( Oct 2016), 9-12. DOI=10.5120/ijca2016911795

@article{ 10.5120/ijca2016911795,
author = { B. M. Vidyavathi, Haseena Begum },
title = { An Efficient Web Recommender System for Web Logs },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2016 },
volume = { 152 },
number = { 3 },
month = { Oct },
year = { 2016 },
issn = { 0975-8887 },
pages = { 9-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume152/number3/26297-2016911795/ },
doi = { 10.5120/ijca2016911795 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:57:09.779436+05:30
%A B. M. Vidyavathi
%A Haseena Begum
%T An Efficient Web Recommender System for Web Logs
%J International Journal of Computer Applications
%@ 0975-8887
%V 152
%N 3
%P 9-12
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nowadays web sites have become an important means for communication. Every application is converted to internet based application. A huge amount of the data is added daily and huge amount of information is accessed from the web. The millions to billions of web users are accessing the interested data from the web making the network traffic. To overcome these problems, recommender systems which are the part of machine learning are introduced. At present, recommender systems play an important role in the e-commerce field. The shopping sites where the users are recommended with the interested products and resources based on their navigation behavior, profile and their interest on the products. The recommender systems are basically divided into three types, they are content based, collaborative and hybrid recommender systems. As the web users are increasing the network performance is affected. Thus it is required to enhance the performance of the network by using the mining and machine learning classification techniques. In this paper, we propose an efficient web recommender system, which uses the concept of preprocessing, clustering technique and expectation maximization naïve bayes as predictive model.

References
  1. Priyansha Bangar and Kedar Nath Singh. 2015. Investigation and Performance Improvement of Web Cache Recommender System, International Conference on Futuristic trend in Computational Analysis and Knowledge Management.
  2. Nanhay Singh, Arvind Panwar and Ram Shringar Raw. 2013. Enhancing the Performance of Web Proxy Server through Cluster Based Prefetching Techniques, International Conference on Advances in Computing, Communications and Informatic.
  3. Saritha Vemulapalli and M. Shashi. 2012. Design and Implementation of an Effective Web Server Log Preprocessing System, Proceedings of the InConINDIA Springer Verlag Berlin Heidelberg.
  4. V.Chitraa and Dr. Antony Selvdoss Davamani. 2010. A Survey on Preprocessing Methods for Web Usage Data, International Journal of Computer Science and Information Security.
  5. Sheetal A Raiyani and Shailendra jain. 2012. Efficient Preprocesing technique using Web log mining, International Journal of Advancements in Research & Technology.
  6. Preeti Gupta. 2014. Pre-Processsing E-Commerce Web Log Files for Web usage Mining, International Journal of Advanced Research in Computer Science and Software Engineering.
  7. P. Julian Benadit, F. Sagayaraj Francis and U. Muruganantham. 2015. Improving the Performance of a Proxy Cache Using Expectation Maximization with Naïve Bayes Classifier, Computational Intelligence in Data Mining, Springer India.
  8. Supreet Dhillon and Kamaljit Kaur. 2014. Comparative Study of Classification Algorithms for Web Usage Mining, International Journal of Advanced Research in Computer Science and Software Engineering.
  9. A. K. Santra and S. Jayasudha. 2012. Classification of Web Log Data to Identify Interested Users Using Naïve Bayesian Classification, International Journal of Computer Science Issues.
  10. Parth Suthar and Prof. Bhavesh Oza. 2015. A Survey of Web Usage Mining Techniques, International Journal of Computer Science and Information Technologies.
Index Terms

Computer Science
Information Sciences

Keywords

Web access logs Preprocessing K-Means clustering Expectation Maximization Naïve Bayes Classifier.