Hao Zhang

Orcid: 0000-0003-0877-2681

Affiliations:
  • University of Science and Technology of China, State Key Laboratory of Cognitive Intelligence, Hefei, China


According to our database1, Hao Zhang authored at least 14 papers between 2023 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
Cross-Domain Pre-training with Language Models for Transferable Time Series Representations.
Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining, 2025

2024
Molar: Multimodal LLMs with Collaborative Filtering Alignment for Enhanced Sequential Recommendation.
CoRR, 2024

Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation.
CoRR, 2024

Learning Transferable Time Series Classifier with Cross-Domain Pre-training from Language Model.
CoRR, 2024

Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform.
Proceedings of the Companion Proceedings of the ACM on Web Conference 2024, 2024

Optimizing Code Retrieval: High-Quality and Scalable Dataset Annotation through Large Language Models.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Learning Recommender Systems with Soft Target: A Decoupled Perspective.
Proceedings of the Database Systems for Advanced Applications, 2024

Unlocking the Potential of Large Language Models for Explainable Recommendations.
Proceedings of the Database Systems for Advanced Applications, 2024

Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling.
Proceedings of the Database Systems for Advanced Applications, 2024

Empowering Sequential Recommendation from Collaborative Signals and Semantic Relatedness.
Proceedings of the Database Systems for Advanced Applications, 2024

Learning the Dynamics in Sequential Recommendation by Exploiting Real-time Information.
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

2023
Unlocking the Potential of Large Language Models for Explainable Recommendations.
CoRR, 2023

Towards Automatic Sampling of User Behaviors for Sequential Recommender Systems.
CoRR, 2023

TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders.
CoRR, 2023


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