Hutashan V. Bhagat; Shashi B.; Sachin M. Baad

MF-NB Learning Based Approach for Recommendation System

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Author(s)

Hutashan V. Bhagat ^1,* Shashi B. ¹ Sachin M. Baad ¹

1. Chandigarh Engineering College/Department of I.T, Landran (Mohali), 140307, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2018.10.04

Received: 1 Jul. 2018 / Revised: 5 Aug. 2018 / Accepted: 12 Aug. 2018 / Published: 8 Oct. 2018

Index Terms

Data Mining, Web Usage Data Mining, Classification, Naïve Bayes Classification, KNN Classifier

Abstract

The Multi Factor-Naive Bayes classifier based recommendation system is analyzed with respect to the traditional KNN classifier based recommendation system. The classification of the web usage data is done on the basis of the keyword name, keyword count, inbound links and age group of the users. Whereas, in traditional KNN the URL was the only factor that was considered for the purpose of classification. The performance evaluation is done in the terms of RMSE, Error Rate, Accuracy Rate and Precision. The MF-NB is observed to be outperforming the KNN classifier in all respective terms.

Cite This Paper

Hutashan V. Bhagat, Shashi B. and Sachin M., "MF-NB Learning Based Approach for Recommendation System", International Journal of Information Technology and Computer Science(IJITCS), Vol.10, No.10, pp.31-37, 2018. DOI:10.5815/ijitcs.2018.10.04

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International Journal of Information Technology and Computer Science (IJITCS)