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Found 3 papers, 1 papers with code
Scenario-Adaptive Fine-Grained Personalization Network: Tailoring User Behavior Representation to the Scenario Context
To enhance the model's capacity to capture user interests from historical behavior sequences in each scenario, we develop a ranking framework named the Scenario-Adaptive Fine-Grained Personalization Network (SFPNet), which designs a kind of fine-grained method for multi-scenario personalized recommendations.
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.
The Reconfiguration Pattern of Individual Brain Metabolic Connectome for Parkinson's Disease Identification
We aim to establish an individual metabolic connectome method to characterize the aberrant connectivity patterns and topological alterations of the individual-level brain metabolic connectome and their diagnostic value in PD.
