Personalized recommendation involves suggesting content, products, or services to users based on their preferences, behaviors, or interactions. These systems use algorithms to analyze user data, such as browsing history, purchase patterns, or social connections, to deliver tailored suggestions.
For instance, e-commerce platforms recommend products similar to items a user has viewed or purchased, while streaming services suggest movies or songs based on a user’s listening habits. Machine learning plays a critical role, with models that predict user preferences using techniques like collaborative filtering, content-based filtering, or hybrid approaches.
Personalized recommendations enhance user experience, improve engagement, and drive business outcomes like increased sales or retention rates. They are widely used across industries, from online retail to healthcare.