Real-Time Recommendation Engine: A Hybrid Approach Using Oracle RTD, Polynomial Regression, and Naive Bayes

International Journal of Computer Science and Engineering

© 2021 by SSRG - IJCSE Journal

Volume 8 Issue 3

Year of Publication : 2021

Authors : Radhika Kanubaddhi

10.14445/23488387/IJCSE-V8I3P103

How to Cite?

Radhika Kanubaddhi, "Real-Time Recommendation Engine: A Hybrid Approach Using Oracle RTD, Polynomial Regression, and Naive Bayes," SSRG International Journal of Computer Science and Engineering , vol. 8,  no. 3, pp. 11-16, 2021. Crossref, https://doi.org/10.14445/23488387/IJCSE-V8I3P103

Abstract:

The rapid growth of e-commerce has led to an increased demand for efficient and personalized recommendation systems. This paper presents a comprehensive approach to building a real-time recommendation engine that leverages Oracle RTD, polynomial regression, and Naive Bayes. First, we discuss the challenges of providing high-quality recommendations on sparse data and how to address them using a dynamic personalized recommendation algorithm [1]. We then explore the importance of representation learning for scalable and efficient recommendations, highlighting the need for models that can handle large datasets and deliver low-latency recommendations. Finally, we describe the development of an offers recommendation system using sequential models, emphasizing the necessary components to integrate the model's insights into a production system.

Keywords:

Orale RTD, Regression, Naïve Bayes.

References:

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