Feedback system

Learn about how Affective Computing (powered by Virtue) performs better over time with its feedback system.

Affective Computing (powered by Virtue)'s feedback system is a crucial component that evaluates the effectiveness of actions selected by the system.

It operates on a self-rewarding mechanism and uses feature selection for data categorization and clustering. It is established as a result of action delivery and works on only the action mode.

This system, which is integral to the strategy engine, leverages reinforcement learning in its Recurrent Neural Network (RNN) to understand and automate user preferences and the impact of various actions on user profiles.

Advantages

• The feedback system assures you of sustained high performance and accuracy in their solutions. By continuously self-assessing and optimizing, the system ensures that the quality and effectiveness of your solutions consistently improve over time.

• The feedback system allows Affective Computing (powered by Virtue) to deliver the best possible personalization given a specific experience, logic, and pool of actions without requiring your direct involvement. You don’t have to think about how to make sure that your users get the best personalization as Affective Computing handles this out-of-the-box.

Use cases

• When monitoring long-term projects, you can observe a steady increase in the accuracy of personalized actions, reflecting the system's learning and adapting capabilities.

• In applications involving complex user interactions, such as personalized playlists, the feedback system assesses the effectiveness of each action, ensuring tailored and relevant user experiences.