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Found 3 papers, 1 papers with code
CROLoss: Towards a Customizable Loss for Retrieval Models in Recommender Systems
In this paper, we proposed the Customizable Recall@N Optimization Loss (CROLoss), a loss function that can directly optimize the Recall@N metrics and is customizable for different choices of N. This proposed CROLoss formulation defines a more generalized loss function space, covering most of the conventional loss functions as special cases.
Soft Retargeting Network for Click Through Rate Prediction
In this paper, we focus on a new type of user interest, i. e., user retargeting interest.
Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction
Rich user behavior information is of great importance for capturing and understanding user interest in click-through rate (CTR) prediction.
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