FairCDSR: Fairness-Aware Cross-Domain Sequential Recommendation via Multi-Interest Transfer and Contrastive Learning

Keyphrases

• Transfer Learning 100%
• Contrastive Learning 100%
• Interest Transfer 100%
• Fairness-aware 100%
• Multi-interest 100%
• Cross-domain Recommendation 100%
• Cross-domain 50%
• Knowledge Transfer 50%
• Disadvantaged Groups 50%
• Prediction Accuracy 25%
• User Groups 25%
• Bandwidth Limitation 25%
• Training Data 25%
• Fixed Bandwidth 25%
• Learning Approaches 25%
• Recommendation Accuracy 25%
• Recommendation System 25%
• Overall System Performance 25%
• Transfer Learning Algorithm 25%
• Source Domain 25%
• Target Domain 25%
• Fair Ranking 25%
• Learning Methods 25%
• Fairness Concern 25%
• Data Sparsity 25%
• Interest Level 25%
• Interest-driven Learning 25%
• Augmentation Techniques 25%
• Demographic Groups 25%
• Fair Recommendation 25%
• Interaction History 25%
• Recommendation Fairness 25%
• Hard Negative Sampling 25%
• Advantaged Groups 25%

Computer Science

• Transfer Learning 100%
• Knowledge Transfer 100%
• Contrastive Learning 100%
• Disadvantaged Group 100%
• Systems Performance 50%
• Training Data 50%
• Prediction Accuracy 50%
• Unfairness 50%
• Sparsity 50%
• Learning Approach 50%
• Recommendation Accuracy 50%
• Domain Information 50%
• Advantaged Group 50%

Engineering

• Contrastive Learning 100%