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Neural Contextual Bandits for Personalized Recommendation
Yikun Ban
, Yunzhe Qi
,
Jingrui He
School of Information Sciences
Siebel School of Computing and Data Science
National Center for Supercomputing Applications (NCSA)
Informatics
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Keyphrases
Advanced Algorithms
20%
Business Recommender System
20%
Collaborative Strategies
20%
Contextual Bandits
100%
Dynamic Landscape
20%
Emerging Challenges
20%
Increasing Complexity
20%
Linear Contextual Bandits
20%
Matthew Effect
20%
Modelling User Preference
20%
Network Benefits
20%
Neural Contextual Bandits
100%
Neural Model
40%
Neural Network
20%
Online Business
20%
Performance Guarantee
20%
Personalized Recommendation
100%
Recommendation Approach
20%
Recommendation System
40%
Rich-get-richer
20%
Supervised Learning
20%
User Correlation
20%
User Experience
20%
User Heterogeneity
20%
User-centric
20%
Computer Science
Collaboration
66%
Neural Network
33%
Performance Guarantee
33%
Recommender Systems
100%
Supervised Learning
33%
User Experience
33%
User Preference
33%