Learning User Preferences Without Feedbacks

Recommending relevant data is vital for helping users to navigate through the ocean of data. We developed a service that learns user preferences through natural user interactions, without asking for user feedbacks, so users are not distracted from their regular workflow. Our approach has few parameters and very low time and space complexities, making it suitable for large scale applications. We demonstrate through experiments how it converges to user preferences and adapts to user behavior changes.