A Survey on Multi-objective Recommender Systems
Dietmar Jannach and Himan Abdollahpouri
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 reasonable 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 max 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.
Dietmar Jannach and Himan Abdollahpouri
Athanasios Vlontzos, Bernhard Kainz, Ciarán M. Gilligan-Lee
Yu Liang, Aditya Ponnada, Paul Lamere, Nediyana Daskalova