Content Recommendation Using Exploration Scores for Novelty Balance

The patent applies local quality by differentiating content recommendation strategies based on user segments. Users are classified into exploration-prone and exploration-averse groups, and each group receives customized content mixes - exploration-prone users get more novel content while exploration-averse users get more familiar content, thus optimizing both relevance and exploration for different user locales

The system dynamically adjusts the balance between familiar and novel content based on real-time user behavior signals. The machine learning model continuously updates user preferences and exploration tendencies, modifying content recommendations adaptively rather than using static rules, enabling the system to respond to changing user states

The system changes the parameter of content novelty by introducing an exploration score that quantifies the balance between familiarity and novelty. This parameter is dynamically adjusted based on user characteristics and behavior, allowing the system to optimize the mix of familiar and novel content to maintain both engagement and interest

The system implements feedback loops where user interactions with recommended content are continuously monitored and fed back into the machine learning model. This feedback mechanism allows the system to learn from user responses and adjust future recommendations to better balance engagement and exploration, preventing user fatigue from overly familiar content