PGRM: Positive-unlabeled enhanced recommendation model based on generative adversarial network

Key Points

• The positive-unlabeled recommendation model shows improved accuracy in recommendations compared to traditional methods.
• Key evidence indicates a 20% increase in accuracy, benefiting areas like personalized content delivery.
• Generative adversarial networks are employed to enhance the performance of the recommendation model against sparse data scenarios.
• The findings suggest significant advancements in recommendation systems, requiring further validation across diverse datasets.