AI models generate personalized humor by analyzing user preferences, interests, and even their sense of humor to create tailored jokes [1]. One approach involves collaborative filtering techniques, identifying users with similar tastes and recommending jokes enjoyed by those users [1]. User preferences can be represented in a matrix, which is then factorized using singular value decomposition (SVD) to estimate user preferences for new jokes and generate personalized recommendations [1].

By analyzing demographics, interests, and facial expressions, AI robo-comedians can adapt their material in real-time to maximize laughter [1]. AI models can also be fine-tuned on relevant datasets to generate jokes for a specific context, such as a sitcom [1]. Furthermore, an AI system called HumorSkills expands on joke topics to find narratives related to the image to grow the number of relatable joke targets [2].


How do AI models generate personalized humor?

Related Content You May Like