  To help their users to discover important items at a particular time, major
websites like Twitter, Yelp, TripAdvisor or NYTimes provide Top-K
recommendations (e.g., 10 Trending Topics, Top 5 Hotels in Paris or 10 Most
Viewed News Stories), which rely on crowdsourced popularity signals to select
the items. However, different sections of a crowd may have different
preferences, and there is a large silent majority who do not explicitly express
their opinion. Also, the crowd often consists of actors like bots, spammers, or
people running orchestrated campaigns. Recommendation algorithms today largely
do not consider such nuances, hence are vulnerable to strategic manipulation by
small but hyper-active user groups.
  To fairly aggregate the preferences of all users while recommending top-K
items, we borrow ideas from prior research on social choice theory, and
identify a voting mechanism called Single Transferable Vote (STV) as having
many of the fairness properties we desire in top-K item (s)elections. We
develop an innovative mechanism to attribute preferences of silent majority
which also make STV completely operational. We show the generalizability of our
approach by implementing it on two different real-world datasets. Through
extensive experimentation and comparison with state-of-the-art techniques, we
show that our proposed approach provides maximum user satisfaction, and cuts
down drastically on items disliked by most but hyper-actively promoted by a few
users.
