Understanding a one-to-one tutoring video requires more than recognizing its events. A model must connect a visible teacher intervention to the learner response it elicits and to the follow-up adjusted to that response. Existing video and educational-dialogue benchmarks typically emphasize event recognition, temporal r...
Recommender systems deployed at scale are predominantly organized around platform-centric architectures in which each service independently constructs and optimizes an internal representation of the user. As large language models (LLMs) become upstream entry points to digital services, personalization may be reorganize...
Jia-Hao Liu, Ming-Zhe Han, Guan Liu et al.· Proceedings of the 20th ACM...· 0 citations
A user-controlled intent layer is defined as a user-addressable representation and governance layer between a user/assistant and downstream services, through which personalization intents can be inspected, edited, scoped, authorized, operationalized, revoked, and audited.
Jia-Hao Liu, Ming-Zhe Han, Guan Liu et al.· Proceedings of the 20th ACM...· 0 citations
This work introduces Fast-Slow Thinking Reward Models (F/S-RM), a hybrid RM architecture inspired by Dual Process Theory that achieves state-of-the-art accuracy with an average score of 84.3 across benchmarks, while reducing token consumption by 22.5%.
FedHUR is a federated recommendation framework for learning hierarchical utility-guided client relations that consistently outperforms existing federated recommendation baselines, demonstrating the effectiveness of hierarchical utility-guided client relation learning.
Ming-Zhe Han, Jia-Hao Liu, Dong-Sheng Li et al.· 0 citations
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