Runhao JIANG
Portrait
Ph.D Student
Hong Kong Baptist University

Welcome to My Homepage. I’m Runhao Jiang (蒋润浩), a Ph.D student at Department of Computer Science in Hong Kong Baptist University (HKBU), advised by Prof. Renchi Yang starting from Sep. 2024. Before that, I received my B.S. degree in Mathematics and Applied Mathematics from Zhejiang Normal University (ZJNU) in 2024.

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Education
  • Hong Kong Baptist University
    Hong Kong Baptist University
    Department of Computer Science
    Ph.D. Student
    Sep. 2024 - present
  • Zhejiang Normal University
    Zhejiang Normal University
    B.S. in Mathematics and Applied Mathematics
    Sep. 2020 - Jul. 2024
Honors & Awards
  • Silver Medal, ICPC EC-Final
    2023
  • National Scholarship for Undergraduates
    2022
  • Gold Medal, ICPC
    2022
  • Gold Medal, CCPC
    2022
News
2026
1 paper was accepted to SIGIR 2026
Apr 17
1 paper was accepted to SIGMOD 2026
Feb 20
2025
1 paper was accepted to CIKM 2025
Nov 08
Selected Publications (view all )
Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems
Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems

Runhao Jiang, Renchi Yang, Donghao Wu

International ACM SIGIR Conference on Research and Development in Information Retrieval 2026

BACO is presented, a fast and effective framework for compressing embedding tables that is built on the idea of exploiting collaborative signals in user-item interactions for user and item groupings, such that similar users/items share the same embeddings in the codebook.

[Paper] [Code]

Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems

Runhao Jiang, Renchi Yang, Donghao Wu

International ACM SIGIR Conference on Research and Development in Information Retrieval 2026

BACO is presented, a fast and effective framework for compressing embedding tables that is built on the idea of exploiting collaborative signals in user-item interactions for user and item groupings, such that similar users/items share the same embeddings in the codebook.

[Paper] [Code]

Effective Clustering for Large Multi-Relational Graphs
Effective Clustering for Large Multi-Relational Graphs

Xiaoyang Lin, Runhao Jiang, Renchi Yang

International Conference on Management of Data (SIGMOD) 2026

DEMM and DEMM+, two effective MRGC approaches to address the aforementioned limitations of existing solutions, and extend DEMM to handle attribute-less MRGs through non-trivial adaptations.

[Paper] [Code]

Effective Clustering for Large Multi-Relational Graphs

Xiaoyang Lin, Runhao Jiang, Renchi Yang

International Conference on Management of Data (SIGMOD) 2026

DEMM and DEMM+, two effective MRGC approaches to address the aforementioned limitations of existing solutions, and extend DEMM to handle attribute-less MRGs through non-trivial adaptations.

[Paper] [Code]

Community-Aware Social Community Recommendation
Community-Aware Social Community Recommendation

Runhao Jiang, Renchi Yang, Wenqing Lin

International Conference on Information and Knowledge Management (CIKM) 2025

CASO is presented, a novel and effective model specially designed for social community recommendation that includes a community detection loss in the model optimization, thereby producing community-aware embeddings for communities.

[Paper] [Code]

Community-Aware Social Community Recommendation

Runhao Jiang, Renchi Yang, Wenqing Lin

International Conference on Information and Knowledge Management (CIKM) 2025

CASO is presented, a novel and effective model specially designed for social community recommendation that includes a community detection loss in the model optimization, thereby producing community-aware embeddings for communities.

[Paper] [Code]

All publications