Recommendation under Capacity Constraints

In this paper, we investigate the common scenario where every candidate item for recommendation is characterized by a maximum capacity, i.e., number of seats in a Point-of-Interest or size of an item's inventory. Despite the prevalence of the task of recommending items under capacity constraints in a variety of settings, to the best of our knowledge, none of the known recommender methods is designed to respect capacity constraints. To close this gap, we extend two state-of-the art latent factor recommendation approaches: probabilistic matrix factorization (PMF) and geographical matrix factorization (GeoMF), to optimize for both prediction accuracy and expected item usage that respects the capacity constraints. We introduce the useful concepts of user propensity to listen and item capacity. Our experimental results in public datasets, both for the domain of item recommendation and Point-of-Interest recommendation, highlight the benefit of our method for the setting of recommendation under capacity constraints.