Multi-Task Learning For Reduced Popularity Bias In Multi-Territory Video Recommendations
This addresses data imbalances in personalized recommender systems for users across different geographic territories, representing an incremental improvement with specific gains.
The paper tackles the problem of popularity bias in multi-territory video recommendations, where globally prevalent items overshadow local ones, and proposes a multi-task learning framework with adaptive upsampling, achieving up to 65.27% relative gain in PR-AUC metric.
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.