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IJSTR >> Volume 8 - Issue 7, July 2019 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Analysing Huge Data Collection And Comparing Through Algorithms: KNN, Naive And Collaborative Filtering & Hybrid

[Full Text]

 

AUTHOR(S)

Pooja Mudgil, Shivani Gautam, Uditta Chhabra, Mansi Jadaun, Paras Jain, Vikas Singh

 

KEYWORDS

Recommendation system, Naïve Bayes, K-Nearest Neighbour (KNN), Collaborative filtering, java, Hadoop tool, Hive.

 

ABSTRACT

Recommendation systems are used to obtain and analyse huge datasets of business organisations and industries thus, helping as well as allowing them to identify the best throughput and optimised options for their increase in efficiency and performance. This technology gains its merits in different other technologies for analysis of data. Organisations are able to gain if they are able to recommend suitable products to variant users by use of correct set of tools. Correct product recommended to customers by companies leads to congeniality for either ends. If, used at wide scale can lead to increase in sale of products, increasing profit margins and satisfied customers. This paper presents the effectiveness of recommendation system and its best suitable algorithm that could be used according to the data set available for the corresponding increase in efficiency and productivity by clubbing results from various other researches with the obtained results from analysing of datasets obtained from Kaggle using three algorithms: Naïve Bayes, KNN, and collaborative filtering. For any business, production and growth are in direct correlation with the user’s usage and requirements which is successful only when a particular user is able to obtain the products correspondingly at the same time and it could be fast and efficient when the results of recommendation system amplify the user’s choices with preferences. Therefore, the studied patterns obtained from researches and through the dataset, implementations of algorithms and comparing them for obtaining an accurate solution for recommendation systems.

 

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