Recommender Systems on the Online Entertainment Industry

This article examines the evolution and impact of recommender systems within the online entertainment industry. Recommender systems, which are algorithms designed to analyse user preferences and deliver personalised content, play a crucial role in helping users discover relevant media. The chapter traces technological advancements in these systems, from foundational collaborative filtering models to advanced deep neural networks, underscoring their contribution to user experience through tailored content recommendations. Additionally, it addresses pressing ethical challenges, including issues of privacy, transparency, bias, and the potential manipulation of user behaviour. Through a critical analysis, the chapter explores the complex balance between technological innovation and ethical responsibility, highlighting the growing need for regulatory frameworks and increased algorithmic literacy. Concluding with recommendations for future research, the article advocates for interdisciplinary approaches to better understand the societal implications of recommender systems.