Core Technologies in Recommender Systems: Investigating and Analyzing Standard Implementations

This paper examines the development of recommendation algorithms in the context of big data and machine learning, highlighting the role of deep learning and natural language processing in improving recommendation accuracy and personalization. Through case studies in e-commerce, streaming services, and social media, we analyze how recommendation systems adapt to diverse application scenarios. The study further discusses the impact of data privacy regulations, ethical considerations, and bias mitigation on system design. By combining a literature review with empirical analysis, this work provides a concise overview of current challenges and future directions in modern recommendation systems.

In Proceedings of the 2024 2nd International Conference on Computer, Machine Learning and Artificial Intelligence (CMLAI 2024). Highlights in Science, Engineering and Technology, 72, 123-135.