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arXiv · 2310.03148

Multi-Task Learning For Reduced Popularity Bias In Multi-Territory Video Recommendations

Phanideep Gampa·Farnoosh Javadi·Belhassen Bayar·Ainur Yessenalina
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Abstract

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.

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Phanideep Gampa, Farnoosh Javadi, Belhassen Bayar, Ainur Yessenalina. 2023-09-25. Multi-Task Learning For Reduced Popularity Bias In Multi-Territory Video Recommendations. https://arxiv.org/abs/2310.03148

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