  Typical recommendation and ranking methods aim to optimize the satisfaction
of users, but they are often oblivious to their impact on the items (e.g.,
products, jobs, news, video) and their providers. However, there has been a
growing understanding that the latter is crucial to consider for a wide range
of applications, since it determines the utility of those being recommended.
Prior approaches to fairness-aware recommendation optimize a regularized
objective to balance user satisfaction and item fairness based on some notion
such as exposure fairness. These existing methods have been shown to be
effective in controlling fairness, however, most of them are computationally
inefficient, limiting their applications to only unrealistically small-scale
situations. This indeed implies that the literature does not yet provide a
solution to enable a flexible control of exposure in the industry-scale
recommender systems where millions of users and items exist. To enable a
computationally efficient exposure control even for such large-scale systems,
this work develops a scalable, fast, and fair method called
\emph{\textbf{ex}posure-aware \textbf{ADMM} (\textbf{exADMM})}. exADMM is based
on implicit alternating least squares (iALS), a conventional scalable algorithm
for collaborative filtering, but optimizes a regularized objective to achieve a
flexible control of accuracy-fairness tradeoff. A particular technical
challenge in developing exADMM is the fact that the fairness regularizer
destroys the separability of optimization subproblems for users and items,
which is an essential property to ensure the scalability of iALS. Therefore, we
develop a set of optimization tools to enable yet scalable fairness control
with provable convergence guarantees as a basis of our algorithm.
