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Multi-Task Learning For Reduced Popularity Bias In Multi-Territory Video 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.
Design Principles of Robust Multi-Armed Bandit Framework in Video Recommendations
Current multi-armed bandit approaches in recommender systems (RS) have focused more on devising effective exploration techniques, while not adequately addressing common exploitation challenges related to distributional changes and item cannibalization.
Exploring Heterogeneous Metadata for Video Recommendation with Two-tower Model
Online video services acquire new content on a daily basis to increase engagement, and improve the user experience.
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