Calibrated Recommendations as a Minimum-Cost Flow Problem
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. Calibration is particularly important given that
optimizing towards accuracy often leads to the user’s minority interests being dominated by their main
interests, or by a few overall popular items, in the recommendations they receive. In this paper, we
propose a novel approach based on the minimum-cost flow problem for generating calibrated recommendations.
In a series of experiments using two publicly available datasets, we demonstrate the superior
performance of our proposed approach compared to the state-of-the-art in generating relevant and
calibrated recommendation lists.
[Paper]