Massachusetts Undergraduate Research Conference (MassURC)

Of the many common applications of Artificial Intelligence (AI), none are interacted with recreationally as often as recommender systems. Recommender systems, also known as recommendation systems or engines, are software applications that provide personalized suggestions or recommendations to users. These systems are widely used in various online platforms to enhance user experience, increase user engagement, and help users discover relevant content or products. This form of AI is designed and built to filter information and provide suggestions for a user based on their activity data. Recommender systems have become standard in e-commerce, digital media, search engines, and other online domains. In this work, I dive into the impact of AI-driven recommender systems and hyper-targeted user personalization. I research and analyze the methods and models behind them, their effects on business practices, individual behavior, and cultural dynamics. I explore the benefits of personalized user experiences and the challenges they pose, such as privacy concerns, the potential for consumer manipulation, and questions surrounding regulation. Through a meta-analysis of the current literature and research, case studies, and statistical data, this work offers a nuanced understanding of how recommender systems are reshaping business and society in the digital age. The evidence presented in this work advocates for a progression toward decentralization of future recommender systems utilizing blockchain technology for business competition, personal data privacy, and the democratization of predictive personal data analysis.

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