Editor/Reviewer loginFrequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
IJCATR Volume 6 Issue 2
Machine Learning Algorithms for Recommender System - a comparative analysis
Satya Prakash Sahu Anand Nautiyal Mahendra Prasad
10.7753/IJCATR0602.1005
keywords : Recommender System, Classifier, Content Based, Collaborative Based, Cluster, Correlation.
Recommendation system is one of the most popular applications of Artificial Intelligence which attracts many researchers all over the globe. The advent of the Internet era has brought wide implementation of recommendation system in our everyday lives. There are many machine learning techniques which can be used to realize the recommendation system. Among all these techniques we are dealing with Content Based Filtering, Collaborative Based Filtering, Hybrid Content-Collaborative Based Filtering, k-mean clustering and Naive Bayes classifier. We have exploited these algorithms to their extreme in order to achieve the best possible precision and have presented a comprehensive comparative analysis. The strength of all these algorithms can be clearly realized by the significant enhancement in the accuracy, depicted by the experimental analysis taking cold start problem into consideration.
@artical{s622017ijcatr06021005,
Title = "Machine Learning Algorithms for Recommender System - a comparative analysis",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "6",
Issue ="2",
Pages ="97 - 100",
Year = "2017",
Authors ="Satya Prakash Sahu Anand Nautiyal Mahendra Prasad"}
Looking at large junk of dumped data as an opportunity with challenges.
Using machine learning techniques to come out of the challenges and converting them into an opportunity.
Deriving the hidden pattern which can be used in solving new age challenges.
Analyzing the machine learning techniques in various environments.