An improved hybrid distributed collaborative filtering model for recommender engine using apache spark

An improved hybrid distributed collaborative filtering model for recommender engine using apache spark

Автор: Rakesh K. Lenka, Rabindra K. Barik, Sasmita Panigrahi, Sai S. Panda

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 7 vol.10, 2018 года.

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The present scenario there is a serious need of scalability for efficient analytics of big data. In order to achieve this, technology like MapReduce, Pig and HIVE came into action but when the question comes to scalability; Apache Spark maintains a great position far ahead. In this research paper, it has designed and developed an improved hybrid distributed collaborative model for filtering recommender engine. Execution time, scalability and robustness of the engine are the three evaluation parameters; has been considered for this present study. The present work keeps an eye on recommender system built with help of Apache Spark. Apart from this, it has been proposed and implemented the bisecting KMeans clustering algorithms. It has discussed about the comparative analysis between KMeans and Bisecting KMeans clustering algorithms on Apache Spark environment.

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Apache Spark;Recommendation Engine;Collaborative Filtering;Machine learning;KMeans;Bisecting KMeans;Bigdata

Короткий адрес: https://sciup.org/15016510

IDR: 15016510   |   DOI: 10.5815/ijisa.2018.07.08

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