CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Clustering and Recommendation using WordNet

by Justina G. Nadar
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 112 - Number 14
Year of Publication: 2015
Authors: Justina G. Nadar
10.5120/19732-1520

Justina G. Nadar . Clustering and Recommendation using WordNet. International Journal of Computer Applications. 112, 14 ( February 2015), 9-12. DOI=10.5120/19732-1520

@article{ 10.5120/19732-1520,
author = { Justina G. Nadar },
title = { Clustering and Recommendation using WordNet },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 112 },
number = { 14 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 9-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume112/number14/19732-1520/ },
doi = { 10.5120/19732-1520 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:49:27.161607+05:30
%A Justina G. Nadar
%T Clustering and Recommendation using WordNet
%J International Journal of Computer Applications
%@ 0975-8887
%V 112
%N 14
%P 9-12
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The recommender systems are new type of software tools designed to help users find their way through today's online shops. Due to the increasing number of e-commerce websites, it is necessary to render effective recommendation to the users. Here we present an overview of current recommendation systems and then our proposed system that employs WordNet dictionary in clustering and content based filtration to provide tailored and friendly suggestions to the user. The proposed system is a user-centric approach that analyses the navigation path of the user and clusters the keyword extracted from WordNet to recommend user articles.

References
  1. Christos Bouras, Vassilis Tsogkas. Clustering user preferences based on W-kmeans, 2011, Seventh International Conference IEEE.
  2. Zakaria Elberrichi, Abdelattif Rahmoun, and Mohamed Amine Bentaalah. Using Wordnet for Text Categorization, the International Arab Journal of Information Technology, Vol. 5, January 2008.
  3. Feng-Hsu Wang, Hsiu-Mei Shao. Effective Personalization Recommendation Based On Time Framed Navigation Clustering and Association Mining, Effective Systems with Application 2004, (365-377)
  4. Yanjun Li, Soon M. Chung. Parallel Bisecting K-means, Wright State University USA, 2007.
  5. Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom and John Riedl. GroupLens: An Open Architecture for Collaborative Filtering of Netnews, 1994.
  6. Epimenidis Voutsakis, Giannis Varelas and Paraskevi Raftopoulou. Semantic Similarity Methods in WordNet and their Application to Information Retrieval on the Web, November 5, 2005, Bremen, Germany.
  7. Robert Cooley, Bamshad Mobasher and Jaideep Srivastava. Data preparation for mining World Wide Web, Department of Computer Science and Engineering, University of Minnesota, USA, 2000.
  8. Christos Bouras, Vassilis Poulopoulos and Vassilis Tsogkas. PeRSSonal's core functionality evaluation: Enhancing text labelling through personalized summaries, Data Knowledge and Engineering 64 (2008) 330-345.
  9. Y. Fu, K. Sandhu and M. Shih. Clustering of web users based on access patterns, Computer Science Department, University of Missouri-Rolla, 2001.
  10. Michael J. Pazzani and Daniel Billsus "Content based Recommendation Systems" Palo Alto Laboratory CA 94304.
  11. K A Abdul Naseer, S D Madhu Kumar and M P Sebastian "Enhancing the k-means algorithm by using a O(n logn) heuristic method for finding better initial centroids" 2011 Second International Conference on Emerging Applications of Information Technology, Calicut India.
  12. Lior R. , Oded M. "Clustering Methods" Data Mining and Knowledge Discovery Handbook, Tel Aviv University.
  13. Mahlon Lovett, "http://wordnet. princeton. edu/" Office of Communications, Princeton University, August 2014.
Index Terms

Computer Science
Information Sciences

Keywords

Recommender systems WordNet Clustering Advanced Kmeans

Powered by PhDFocusTM