In the recommended list of items, calibration ensures that the various (past) areas of interest of a user are reflected with their corresponding proportions.
When a user has watched, say, 70 romance movies and 30 action movies, then it is reasonable to expect the personalized list of recommended movies to be comprised of about 70% romance and 30% action movies as well.
이 논문에서 제시하는 것
metrics for quantifying the degree of calibration
a simple yet effective re-ranking algorithm for post-processing the output of recommender systems