International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Informative Article | Data & Knowledge Engineering | India | Volume 11 Issue 9, September 2022 | Popularity: 5 / 10


     

Enhancing Customer Experience through Personalized Recommendations: A Machine Learning Approach

Sai Kalyana Pranitha Buddiga, Siddhartha Nuthakki


Abstract: This paper explores how machine learning algorithms can be leveraged to enhance customer experience through personalized recommendations. In today's competitive market landscape, businesses strive to deliver tailored recommendations to their customers to increase engagement, retention, and revenue. By analyzing customer behavior and preferences, machine learning models can predict individualized recommendations for products, services, and content. This paper discusses various machine learning techniques, such as collaborative filtering, content-based filtering, hybrid approaches, and their applications in recommendation systems. Additionally, it examines challenges, best practices, and emerging trends in the field of personalized recommendations, offering insights for businesses seeking to implement effective recommendation systems.


Keywords: Recommender Systems, Machine Learning, Collaborative Filtering, Content-Based Filtering, Customer Experience


Edition: Volume 11 Issue 9, September 2022


Pages: 1265 - 1267


DOI: https://www.doi.org/10.21275/SR24531130722



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Sai Kalyana Pranitha Buddiga, Siddhartha Nuthakki, "Enhancing Customer Experience through Personalized Recommendations: A Machine Learning Approach", International Journal of Science and Research (IJSR), Volume 11 Issue 9, September 2022, pp. 1265-1267, https://www.ijsr.net/getabstract.php?paperid=SR24531130722, DOI: https://www.doi.org/10.21275/SR24531130722



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