Survey on Deep Learning-Driven Personalized Recommendation Systems in E-Commerce Websites

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 4

Year of Publication : 2025

Authors : D. Rajya Lakshmi, Siva Jyothi Barla, Gullipalli Neelima

10.14445/23488549/IJECE-V12I4P108

How to Cite?

D. Rajya Lakshmi, Siva Jyothi Barla, Gullipalli Neelima, "Survey on Deep Learning-Driven Personalized Recommendation Systems in E-Commerce Websites," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 4, pp. 84-98, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I4P108

Abstract:

A product recommendation system is essentially a filter that identifies and displays the things a shopper is most likely to purchase. Currently, e-commerce websites are growing as a new market, allowing users to purchase millions of products. Choosing a product from millions of options requires a second tool called a recommendation system. It essentially acts as a filtering mechanism that attempts to anticipate and provide the products a user wants to purchase. Companies can choose which product to launch in the marketplace to gain more benefits by researching user preferences. In order to recommend appropriate customer retention techniques, it is more important to recognize the limitations of existing methods. Therefore, this review article discusses numerous approaches to extracting product recommendation and prediction information from various websites and their advantages and disadvantages for the years 2017 to 2023. This study examines web content capture methods, including machine learning, fuzzy models, deep learning and data mining. This article briefly discusses the difficulties of obtaining information from the internet, possible uses for product recommendations, and helpful future advice to increase effectiveness. The best methods for product recommendation systems can be demonstrated for future use, according to this review article.

Keywords:

Product recommendation, Deep learning model, LSTM, E-commerce websites, Principal Component Analysis (PCA), Machine learning method, Gaussian Mixture Model (GMM).

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