A Review on Deep Learning Powered Online Product Recommender System in the Digital Market Place
Challa Sri Nikitha, C. Sushama · 2024
The rapid growth of digital marketplaces has transformed the way consumers discover and purchase products, necessitating advanced recommender systems to enhance the user experience and drive business success. This paper presents a comprehensive review of personalized online product recommender systems leveraging deep learning techniques in the dynamic landscape of digital marketplaces. One promising use of deep learning is the development of intelligent and tailored recommendation systems, thanks to its capacity to automatically learn complex patterns and representations from large datasets. The review begins by elucidating the fundamental concepts of recommender systems and the evolution of recommendation algorithms over time. Subsequently, it delves into the role of deep learning in revolutionizing personalized recommendations, emphasizing its capability to capture latent user preferences, item features, and intricate user-item interactions. Different deep learning models used in personalized recommender systems are talked about in detail, along with their pros and cons. These models include neural collaborative filtering, recurrent neural networks, and deep autoencoders. This paper critically evaluates methodologies in product recommender systems using deep learning, starting with an exploration of fundamental concepts and the evolution of recommendation algorithms. It examines the benefits and drawbacks of various strategies, addressing potential challenges faced in real-world digital marketplaces, including data sparsity, scalability, and interpretability. Strategies to enhance the robustness of personalized recommendations are discussed, with a spotlight on hybrid models combining deep learning and traditional methods. The last part of the paper gives a thoughtful analysis and outlook on the future of deep learning-based product recommender systems. It stresses the ongoing need for research and new ideas to solve problems and make recommendations more accurate in digital marketplaces that are always changing.