Core Technologies in Recommender Systems: Investigating and Analyzing Standard Implementations

Yifan Cai

doi:10.54097/thv9yp09

Authors

Yifan Cai

DOI:

https://doi.org/10.54097/thv9yp09

Keywords:

Recommendation Systems; Collaborative Filtering; Content-based Filtering; Machine Learning in Recommendations.

Abstract

This paper places special emphasis on the evolution of recommendation algorithms in the context of big data and machine learning, underscoring significant advancements in deep learning and natural language processing that have markedly improved the precision and personalization of recommendations. We explore case studies in various sectors, including e-commerce, streaming services, and social media, to illustrate the adaptation of these technologies to distinct industry requirements. A critical component of our analysis is the examination of the impact of user data privacy regulations on the design and functionality of these systems. Additionally, the paper addresses the challenges and prospective directions in the field, with a particular focus on ethical considerations, the mitigation of bias, and the incorporation of artificial intelligence for dynamic, context-aware recommendation systems. Our research methodology amalgamates an extensive literature review with empirical data analysis, offering an in-depth understanding of the current state of recommendation system technologies. The findings of this study are intended to contribute to the field by presenting a comprehensive view of the technological, ethical, and practical dimensions of recommendation systems in the contemporary digital landscape.

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