  In many recommendations, a handful of popular items (e.g., movies /
television shows, news, etc.) can be dominant in recommendations for many
users. However, we know that in a large catalog of items, users are likely
interested in more than what is popular. The dominance of popular items may
mean that users will not see items that they would probably enjoy. In this
paper, we propose a technique to overcome this problem using adversarial
machine learning. We define a metric to translate the user-level utility metric
in terms of an advantage/disadvantage over items. We subsequently used that
metric in an adversarial learning framework to systematically promote
disadvantaged items. Distinctly, our method integrates a small-capacity model
to produce semantically meaningful weights, leading to an algorithm that
identifies and promotes a semantically similar item within the learning
process. In the empirical study, we evaluated the proposed technique on three
publicly available datasets and seven competitive baselines. The result shows
that our proposed method not only improves the coverage, but also,
surprisingly, improves the overall performance.
