• Saim Ahmed
• Anand Singh Rajavat

: Recommender systems have made significant utility in daily routing life. Online shopping and Social networking sites are playing crucial role in routine life. Over 3.5 billion people uses internet for various purpose. Online shopping retail sales are predicted to grow steadily in upcoming years. Product recommendation is one of the major requirements of e-commerce portals. This feature can help to increase shopping value with minimum shopping time. Logical recommendation not only helps to customer for purchasing but also increases total sales value. Generally, consumers need to search a lot to find a product of interest. Consequently, conventional recommender service systems often suffer from lack of scalability and efficiency problems when processing or analysis of this data on a large scale. To avoid these problems, a novel recommendations system using collaborative filtering algorithm and customer behavior is proposed to implement with Apache Hadoop server for BigData Processing. Proposed solution will attempt to recommend product based on similarity and popularity index of each product with respect to customer review. This project will attempt to develop own customer behavior analysis and classification algorithm to provide more perfect results. Amazon dataset will be used recommendation and evaluation purpose. Computation time for single and multimode cluster will be primary concern for performance measurement

BigData, Amazon, Product Recommendation, Collaborative Filtering, Customized customer review analysis, Content Based Filtering.

"Data classification for improvement of product recommendation system using hybrid algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.446-448, May-2019, Available :http://www.jetir.org/papers/JETIR1905P67.pdf

"Data classification for improvement of product recommendation system using hybrid algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp446-448, May-2019, Available at : http://www.jetir.org/papers/JETIR1905P67.pdf