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Reseach Article

Novel Recommender System Design using Supervised and Unsupervised Techniques

by Sherica Lavinia Menezes, Geeta Varkey
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 68 - Number 12
Year of Publication: 2013
Authors: Sherica Lavinia Menezes, Geeta Varkey
10.5120/11633-7109

Sherica Lavinia Menezes, Geeta Varkey . Novel Recommender System Design using Supervised and Unsupervised Techniques. International Journal of Computer Applications. 68, 12 ( April 2013), 28-33. DOI=10.5120/11633-7109

@article{ 10.5120/11633-7109,
author = { Sherica Lavinia Menezes, Geeta Varkey },
title = { Novel Recommender System Design using Supervised and Unsupervised Techniques },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 68 },
number = { 12 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 28-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume68/number12/11633-7109/ },
doi = { 10.5120/11633-7109 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:27:40.679530+05:30
%A Sherica Lavinia Menezes
%A Geeta Varkey
%T Novel Recommender System Design using Supervised and Unsupervised Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 68
%N 12
%P 28-33
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recommender systems have been designed using association rule mining. However the rule generation complexity of ARM proves to be disadvantageous when dealing with huge amounts of data. Taking this disadvantage into consideration this paper proposes predicting missing items using associative classification techniques. To accomplish this task either a classifier or a clustering approach is chosen. This paper proposes classifying the items prior to prediction process using Naïve Bayes Classifier or hierarchical clustering approach. The advantages of the proffered approach are that the complexity of rule generation is lowered to a great extent and the prediction is done at a higher level of abstraction. The prediction algorithm chosen is associative classification mining using ComboMatrix. The classifier or the clustering mechanism maps huge datasets to a set of classes the size of which in most classes is smaller than the size of the dataset. Therefore this approach greatly reduces the size of the dataset and the overall complexity. This paper lists out the literature survey carried out in the field and the design of the proposed system. The experiment carried out shows that the performance and memory requirements of the proposed approach are more efficient than the method using only associative classification mining.

References
  1. Kasun Wickramaratna, Miroslav Kubat and Kamal Premaratne, "Predicting Missing Items in Shopping Carts", IEEE Trans. Knowledge and Data Eng. , vol. 21, no. 7, July 2009.
  2. Srivatsan. M, Sunil Kumar. M, Vijayshankar. V, Leela Rani P, "Predicting Missing Items in Shopping Carts using Fast Algorithm", International Journal of Computer Applications (0975 – 8887) Volume 21– No. 5, May 2011
  3. Faustina Johnson and Santosh Kumar Gupta, "Web Content Mining Techniques: A Survey", International Journal of Computer Applications (0975 – 888) Volume 47– No. 11, June 2012.
  4. MENG Xiaofeng, LU Hongjun, WANG Haiyan and GU Mingzhe, "Data Extraction from the Web Based on Pre-Defined Schema", J. Comput. Sci. & Technol. , Vol. 17 No. 4, July 2002.
  5. David F. Barrero and David Camacho and Maria D. R-Moreno, "Automatic Web Data Extraction based on Genetic Algorithms and Regular Expressions".
  6. Yanhong Zhai and Bing Liu, "Web Data Extraction Based on Partial Tree Alignment", WWW 2005, May 10-14, 2005.
  7. Emilio Ferrara, Pasquale De Meo, Giacomo Fiumara and Robert Baumgartner, "Web Data Extraction, Applications and Techniques: A Survey", ACM Computing Surveys, Vol. V, No. N, July 2012.
  8. Alberto H. F. Laender, Berthier A. Ribeiro Neto, Altigran S. da Silva and Juliana S. Teixeira, "A Brief Survey of Web Data Extraction Tools".
  9. J. Ben Schafer, Dan Frankowski, Jon Herlocker, and Shilad Sen, "Collaborative Filtering Recommender Systems".
  10. Hemalatha Chandrashekhar and Bharat Bhasker, "Personalized Recommender System Using Entropy Based Collaborative Filtering Technique".
  11. Benjamin C. M. Fung, Ke Wang, and Martin Ester, "Hierarchical Document Clustering"
  12. Ila Padhi , Jibitesh Mishra, Sanjit Kumar Dash, "Predicting Missing Items in Shopping Cart using Associative Classification Mining", International Journal of Computer Applications (0975 – 8887) Volume 50 – No. 14, July 2012.
  13. http://nlp. stanford. edu/IR-book/html/htmledition/naive-bayes-text-classification-1. html.
Index Terms

Computer Science
Information Sciences

Keywords

ComboMatrix Graph based prediction Hierarchical Clustering Naïve Bayes classifier Recommender systems