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.

通过提出多任务学习技术和自适应上采样方法来减少多领域推荐中的受欢迎度偏差，提出的方法通过上采样来丰富训练样例，并通过利用多任务学习来学习基于地理位置的用户嵌入，实验证明了方法在多个地域上相对于不采用提出的技术的基准模型的有效性，显著改进了PR-AUC指标高达65.27%，并通过案例研究展示了方法在减轻全局物品的受欢迎度偏差方面的优势。

减少多领域视频推荐中的流行度倾向的多任务学习