  Various data imbalances that naturally arise in a multi-territory
personalized recommender system can lead to a significant item bias for
globally prevalent items. A locally popular item can be overshadowed by a
globally prevalent item. Moreover, users' viewership patterns/statistics can
drastically change from one geographic location to another which may suggest to
learn specific user embeddings. In this paper, we propose a multi-task learning
(MTL) technique, along with an adaptive upsampling method to reduce popularity
bias in multi-territory recommendations. Our proposed framework is designed to
enrich training examples with active users representation through upsampling,
and capable of learning geographic-based user embeddings by leveraging MTL.
Through experiments, we demonstrate the effectiveness of our framework in
multiple territories compared to a baseline not incorporating our proposed
techniques.~Noticeably, we show improved relative gain of up to $65.27\%$ in
PR-AUC metric. A case study is presented to demonstrate the advantages of our
methods in attenuating the popularity bias of global items.
