Publication Type:

Journal Article

Source:

International Journal of Applied Engineering Research, Research India Publications, Volume 10, Number 9, p.24529-24540 (2015)

URL:

http://www.scopus.com/inward/record.url?eid=2-s2.0-84934918125&partnerID=40&md5=a66fd3fefb368002205c7fc627a4c3e0

Abstract:

Machine learning is a method which is used to learn from data without any human involvement. Recommendation systems come under Machine learning technique which has become one of the essential systems in our day to day e-commerce internet interaction. Many algorithms are proposed to effectively capture the taste of the users and to provide recommendations accurately. Collaborative filtering is one such successful method to provide recommendation to the users. Classification which also falls under Machine learning technique contains many algorithms which can classify text, numerical data, etc. In this paper, we demonstrate two Collaborative Filtering algorithms viz, User based and Item based recommender systems; and three Classification algorithms viz, Naive-Bayes, Logistic Regression and Random Forest Classification. We analysed the results based on evaluation metrics. Our experiment suggests that in Recommender systems, Item based scores over User based; and in Classification, Naive-Bayes emerges superior. © Research India Publications.

Notes:

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Cite this Research Publication

S. Jaysri, Priyadharshini, J., P. Subathra, and Kumar, P. N., “Analysis and performance of collaborative filtering and classification algorithms”, International Journal of Applied Engineering Research, vol. 10, pp. 24529-24540, 2015.