Paper Key : IRJ************891
Author: Valluripallysathwik,Juluru Danush
Date Published: 11 Jul 2024
Abstract
Machine learning has completely transformed the way product recommendations are made, thanks to its capability to analyze huge sums of data and predict user preferences. This increases the experience of customer but also revolutionizes the field. At the core of these recommendation systems are algorithms that can process user behavior, , and contextual information, product attributes, enabling them to generate personalized suggestions. The backbone of these systems consists of Techniques like collaborative filtering, content-based Filtering, and hybrid methods are employed.The progress of deep learning and natural language processing has led to improvements., these recommendation engines have become even more accurate and relevant. In this paper, we dive onto the fundamental concepts, methodologies, and recent advancements in machine learning-based product recommendations. We will also explore the applications, challenges, and future directions of these techniques.Keywords: Machine Learning, Product Recommendations, Collaborative Filtering, Content-Based Filtering, Hybrid Methods, Deep Learning, Natural Language Processing, User Preferences
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