  Related Item Recommendations (RIRs) are ubiquitous in most online platforms
today, including e-commerce and content streaming sites. These recommendations
not only help users compare items related to a given item, but also play a
major role in bringing traffic to individual items, thus deciding the exposure
that different items receive. With a growing number of people depending on such
platforms to earn their livelihood, it is important to understand whether
different items are receiving their desired exposure. To this end, our
experiments on multiple real-world RIR datasets reveal that the existing RIR
algorithms often result in very skewed exposure distribution of items, and the
quality of items is not a plausible explanation for such skew in exposure. To
mitigate this exposure bias, we introduce multiple flexible interventions
(FaiRIR) in the RIR pipeline. We instantiate these mechanisms with two
well-known algorithms for constructing related item recommendations --
rating-SVD and item2vec -- and show on real-world data that our mechanisms
allow for a fine-grained control on the exposure distribution, often at a small
or no cost in terms of recommendation quality, measured in terms of relatedness
and user satisfaction.
