STM Journals Peer-reviewed research from Consortium e-Learning Network

E-Commerce for Future & Trends

Impact of ML in Evolution of Recommender Systems: A Review

  1. Vedulla Varshini
  2. Gullapudi Sai Pramod
  3. Allam Sirisha
  4. Manas Kumar Yogi

Abstract

Recommender systems have become indispensable tools in today's digital era, catering to the overwhelming demand for personalized recommendations in various domains such as e-commerce, content streaming, and social media. The continuous growth of data and user interactions has necessitated the integration of advanced machine learning (ML) techniques to enhance the accuracy and efficiency of these systems. This study provides a thorough analysis of current developments in machine learning methods used in recommender systems. The paper starts by providing an overview of traditional collaborative filtering methods, content-based filtering, and hybrid approaches, highlighting their limitations in handling the challenges posed by sparse data, cold start problem, and scalability issues. It then delves into how modern ML techniques, such as deep learning, matrix factorization, and ensemble methods, have emerged as powerful solutions to address these challenges. Incorporating deep learning architectures, such as neural networks and recurrent neural networks (RNNs), into recommender systems has shown remarkable success in capturing complex patterns in user-item interactions. Additionally, the scalability and interpretability of recommender systems have been enhanced by the incorporation of matrix factorization techniques like Singular Value Decomposition (SVD) and Alternating Least Squares (ALS). Furthermore, the paper explores the application of reinforcement learning for sequential recommendation tasks, enabling systems to learn optimal recommendation policies over time. Reinforcement learning has proven effective in scenarios where user preferences dynamically evolve, such as in news article recommendation and sequential product recommendation. Ensemble methods, which combine multiple individual recommenders, have demonstrated superior performance by leveraging the diverse strengths of different algorithms. The paper presents various ensemble techniques, including stacking, blending, and weighted models, showcasing their ability to achieve state-of-the-art results in accuracy and robustness.

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