M
Mingdong Ou
Researcher at Alibaba Group
Publications - 12
Citations - 1643
Mingdong Ou is an academic researcher from Alibaba Group. The author has contributed to research in topics: Hash function & Universal hashing. The author has an hindex of 7, co-authored 12 publications receiving 1293 citations. Previous affiliations of Mingdong Ou include Tsinghua University.
Papers
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Proceedings ArticleDOI
Asymmetric Transitivity Preserving Graph Embedding
TL;DR: A novel graph embedding algorithm, High-Order Proximity preserved Embedding (HOPE for short), is developed, which is scalable to preserve high-order proximities of large scale graphs and capable of capturing the asymmetric transitivity.
Proceedings ArticleDOI
Who should share what?: item-level social influence prediction for users and posts ranking
TL;DR: This paper proposes a Hybrid Factor Non-Negative Matrix Factorization (HF-NMF) approach for item-level social influence modeling, and devise an efficient projected gradient method to solve the HF- NMF problem.
Proceedings Article
Deep multimodal hashing with orthogonal regularization
TL;DR: This paper proposes a novel deep multimodal hashing method, namely Deep Multimodal Hashing with Orthogonal Regularization (DMHOR), which fully exploits intra- modality and inter-modality correlations and finds that a better representation can be attained with different numbers of layers for different modalities, due to their different complexities.
Journal ArticleDOI
Learning Compact Hash Codes for Multimodal Representations Using Orthogonal Deep Structure
TL;DR: A hashing-based orthogonal deep model is proposed to learn accurate and compact multimodal representations and it is theoretically proved that, in this case, the learned codes are guaranteed to be approximately Orthogonal.
Proceedings ArticleDOI
Comparing apples to oranges: a scalable solution with heterogeneous hashing
TL;DR: This paper proposes a novel Relation-aware Heterogeneous Hashing (RaHH), which provides a general framework for generating hash codes of data entities sitting in multiple heterogeneous domains and encodes both homogeneous and heterogeneous relationships between the data entities to design hash functions with improved accuracy.