An Efficient Recommendation System Using Collaborative Filtering and Big Data Analytics
Bolishetti Ananya Varma,
Vanaparthi Kiranmai,
Bhaskar K
The rapid growth of e-commerce platforms, online streaming services, social media applications, and
digital marketplaces has resulted in an enormous increase in user-generated data and digital content.
As users interact with millions of products, movies, books, music tracks, and online services, identifying
personalized content has become increasingly challenging. Recommendation systems play a crucial
role in filtering relevant information and improving user experience by suggesting items that match
individual preferences. Traditional recommendation techniques often experience limitations when
processing massive datasets due to scalability issues, sparse user ratings, and increasing computational
complexity. Big Data Analytics has emerged as an effective solution by enabling efficient processing of
large-scale user interactions while improving recommendation quality and computational efficiency.
This paper proposes an efficient recommendation system that integrates Collaborative Filtering with Big
Data Analytics to generate personalized recommendations for large-scale applications. The proposed
framework utilizes Apache Spark for distributed data processing, collaborative filtering algorithms for
preference prediction, and big data analytics techniques for scalable recommendation generation. User
interactions, historical ratings, demographic information, and behavioral patterns are processed to
identify similarities among users and items. The framework employs distributed computation to reduce
processing time while maintaining high recommendation accuracy. Experimental evaluation was
conducted using large-scale recommendation datasets containing millions of user-item interactions
collected from e-commerce and multimedia platforms. Comparative analysis demonstrates that the
proposed framework significantly improves recommendation accuracy, scalability, throughput, and
execution efficiency compared with conventional recommendation approaches. The proposed system
provides an intelligent and scalable solution suitable for modern big data recommendation environments.
