Learning Hierarchical Preferences for Recommendation with Mixture Intention Neural Stochastic Processes

Computer Science

• Deep Learning Method 100%
• User Preference 100%
• User Behavior 100%
• Approximation (Algorithm) 100%
• Decision-Making 50%
• Cold Start Problem 50%
• Performance Model 50%
• Learning Mechanism 50%
• Generalization Error 50%
• Meta-Learning 50%
• Behavior Data 50%
• Structured Representation 50%

Keyphrases

• Stochastic Processes 100%
• Hierarchical Preference 100%
• User Preference 33%
• User Intention 33%
• Recommendation Performance 16%
• Decision-making Process 16%
• User Behavior 16%
• Cold-start Problem 16%
• User Decision-making 16%
• Rapid Adaptation 16%
• New View 16%
• Preference Modeling 16%
• Model Performance 16%
• Approximation Error 16%
• Recommendation Process 16%
• Learning Mechanisms 16%
• User Response 16%
• Meta-learning 16%
• User Behavior Data 16%
• Generalization Error Bound 16%
• Mixture Strategies 16%
• Structured Representation 16%
• Higher-level Concept 16%
• Robust Recommendation 16%
• Induced Forest 16%
• Backfitting 16%
• Gist 16%
• Process Family 16%