Research map / Reinforcement learning / Visual and representation RL
Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use RIRL instead of IMUP, adding adversarial training and a gated profile update (ablation) | IMUP · New York and Beijing | Prec_Cat | Helped |
| Use RIRL instead of IMUP, adding adversarial training and a gated profile update (ablation) | IMUP · New York and Beijing | Rec_Cat | Helped |
| Use RIRL instead of IMUP, adding adversarial training and a gated profile update (ablation) | IMUP · New York and Beijing | Avg_Sim | Helped |
| Use RIRL instead of IMUP, adding adversarial training and a gated profile update (ablation) | IMUP · New York and Beijing | Avg_Dist | Helped |
| Use TD-based rather than reward-based experience sampling (ablation) | RIRL · New York and Beijing | Prec_Cat | Helped |
| Use TD-based rather than reward-based experience sampling (ablation) | RIRL · New York and Beijing | Rec_Cat | Helped |
| Use TD-based rather than reward-based experience sampling (ablation) | RIRL · New York and Beijing | Avg_Sim | Helped |
| Use TD-based rather than reward-based experience sampling (ablation) | RIRL · New York and Beijing | Avg_Dist | Helped |
Closest papers by meaning
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- Drift-Aware Multimodal User Representation Learning via Multi-Scale Temporal Modeling and Sparse Mixture-of-Experts (NeurIPS 2026 · similarity 0.80)
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