Himan Abdollahpouri, Zahra Nazari, Alex Gain, Clay Gibson, Maria Dimakopoulou, Jesse Anderton, Benjamin Carterette, Mounia Lalmas, Tony Jebara (WSDM 2023)
Calibration in recommender systems has recently gained significant attention. In the recommended
list of items, calibration ensures that the various (past) areas of interest of a user are reflected
with their corresponding proportions. For instance, if a user has watched, say, 80 romance movies and
20 action movies, then it is reason-able to expect the recommended list of movies to be comprised of
about 80% romance and 20% action movies as well. In this paper, we
propose a novel approach based on the minimum-cost flow problem for generating calibrated recommendations.
We demonstrate the superior performance of our proposed approach compared to the state-of-the-art in generating relevant and
calibrated recommendation lists.
[Paper] [Blog Post]