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.

翻译：多地域个性化推荐系统中自然出现的各种数据不平衡现象，可能导致全局流行物品产生显著的项目偏差。本地流行物品可能被全局流行物品所掩盖。此外，用户在不同地理区域的观看模式/统计数据可能存在显著差异，这提示需要学习特定的用户嵌入。本文提出了一种多任务学习（MTL）技术，结合自适应上采样方法，以减少多地域推荐中的流行度偏差。我们提出的框架旨在通过上采样丰富训练样本中活跃用户的表征，并利用多任务学习能力学习基于地理的用户嵌入。通过实验，我们证明了该框架在多个地域中相较于未采用所提技术的基线方法的有效性。值得注意的是，在PR-AUC指标上，我们实现了最高达65.27%的相对增益提升。文中还通过案例研究展示了我们的方法在缓解全局物品流行度偏差方面的优势。