  As we all know, users and item-providers are two main parties of participants
in recommender systems. However, most existing research efforts on
recommendation were focused on better serving users and overlooked the purpose
of item-providers. This paper is devoted to improve the item exposure fairness
for item-providers' objective, and keep the recommendation accuracy not
decreased or even improved for users' objective. We propose to set stock volume
constraints on items, to be specific, limit the maximally allowable recommended
times of an item to be proportional to the frequency of its being interacted in
the past, which is validated to achieve superior item exposure fairness to
common recommenders and thus mitigates the Matthew Effect on item popularity.
With the two constraints of pre-existing recommendation length of users and our
stock volumes of items, a heuristic strategy based on normalized scores and a
Minimum Cost Maximum Flow (MCMF) based model are proposed to solve the optimal
user-item matching problem, whose accuracy performances are even better than
that of baseline algorithm in regular recommendation context, and in line with
state-of-the-art enhancement of the baseline. What's more, our MCMF based
strategy is parameter-free, while those counterpart algorithms have to resort
to parameter traversal process to achieve their best performance.
