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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.
Distantly Supervised Transformers For E-Commerce Product QA
Training with CQA pairs helps our model learning semantic QA relevance and distant supervision enables learning of syntactic features as well as the nuances of user querying language.
Constrained Convolutional Neural Networks: A New Approach Towards General Purpose Image Manipulation Detection
Furthermore, forensic analysts need ‘general purpose’ forensic algorithms capable of detecting multiple different image manipulations.
