Understanding How Recommender Systems Shape Consumer Behavior Using Implicit Feedback

This paper explores how major technology firms like Google, Amazon, Meta, and Microsoft apply artificial intelligence and machine learning to understand and influence consumer behavior using implicit feedback such as clicks and watch time. By examining collaborative filtering, content-based, hybrid, and neural network?driven recommendation systems, it highlights both their technical foundation and real-world scalability. Through detailed case studies, the study analyzes the implications of personalized digital experiences and their effect on consumer decision- making. It also critiques the ethical dimensions of data usage, addressing concerns around privacy, autonomy, and algorithmic bias. Ultimately, the paper argues that while recommender systems boost personalization and commercial success, their sustainable future hinges on responsible, transparent deployment.