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Home/Authors/Jihong Guan

Jihong Guan

3 indexed papers

Recent (6 mo)
3
With code
0
Influential cites
0
Benchmarked
0

Publications per year

3
26

Top categories

Crypto×2Info Retrieval×2AI×1

Frequent co-authors

Shuigeng Zhou3×
Zhenyu Yu2×
Hongxu Ma1×
Han Zhou1×
Chenghou Jin1×
Jie Zhang1×

Research Timeline

2026
FlowTime: Towards Continuous Generative Watch Time Prediction via Flow-based Personalized Priors

FlowTime proposes a novel Continuous Generative Regression framework using a Flow-based Personalized Prior to accurately model the multimodal and heterogeneous nature of user watch time prediction, significantly outperforming existing state-of-the-art methods.

Ghost: Plausible Yet Unlearnable Trajectories via On-Manifold Substitution for Next-POI Privacy

Ghost introduces a manifold-aligned framework to generate plausible yet unlearnable synthetic check-in trajectories, significantly degrading the accuracy of next-POI prediction models without sacrificing realism.

Ghost: Plausible Yet Unlearnable Trajectories via On-Manifold Substitution for Next-POI Privacy

Ghost introduces a manifold-aligned framework to generate plausible, unlearnable synthetic check-in trajectories that significantly degrade an attacker's ability to predict future locations.

Highlighted terms show continued research focus across papers

Papers

cs.CRcs.IRRecentJun 2, 2026

Ghost: Plausible Yet Unlearnable Trajectories via On-Manifold Substitution for Next-POI Privacy

Zhenyu Yu, Jihong Guan, Shuigeng Zhou

Ghost introduces a manifold-aligned framework to generate plausible yet unlearnable synthetic check-in trajectories, significantly degrading the accuracy of next-POI prediction models without sacrific…

View →
cs.CRcs.IRRecentJun 2, 2026

Ghost: Plausible Yet Unlearnable Trajectories via On-Manifold Substitution for Next-POI Privacy

Zhenyu Yu, Jihong Guan, Shuigeng Zhou

Ghost introduces a manifold-aligned framework to generate plausible, unlearnable synthetic check-in trajectories that significantly degrade an attacker's ability to predict future locations.

View →
cs.AIRecentMay 31, 2026

FlowTime: Towards Continuous Generative Watch Time Prediction via Flow-based Personalized Priors

Hongxu Ma, Han Zhou, Chenghou Jin, Jie Zhang +4 more

FlowTime proposes a novel Continuous Generative Regression framework using a Flow-based Personalized Prior to accurately model the multimodal and heterogeneous nature of user watch time prediction, si…

View →