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- Peng Cui, Fei Wang, Shaowei Liu, Mingdong Ou, Shiqiang Yang, Lifeng Sun
- SIGIR
- 2011

People and information are two core dimensions in a social network. People sharing information (such as blogs, news, albums, etc.) is the basic behavior. In this paper, we focus on predicting item-level social influence to answer the question Who should share What, which can be extended into two information retrieval scenarios: (1) Users ranking: given an… (More)

- Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, Wenwu Zhu
- KDD
- 2016

Graph embedding algorithms embed a graph into a vector space where the structure and the inherent properties of the graph are preserved. The existing graph embedding methods cannot preserve the asymmetric transitivity well, which is a critical property of directed graphs. Asymmetric transitivity depicts the correlation among directed edges, that is, if… (More)

- Mingdong Ou, Peng Cui, Fei Wang, Jun Wang, Wenwu Zhu, Shiqiang Yang
- KDD
- 2013

Although hashing techniques have been popular for the large scale similarity search problem, most of the existing methods for designing optimal hash functions focus on homogeneous similarity assessment, i.e., the data entities to be indexed are of the same type. Realizing that heterogeneous entities and relationships are also ubiquitous in the real world… (More)

- Daixin Wang, Peng Cui, Mingdong Ou, Wenwu Zhu
- IJCAI
- 2015

Hashing is an important method for performing efficient similarity search. With the explosive growth of multimodal data, how to learn hashing-based compact representations for multimodal data becomes highly non-trivial. Compared with shallowstructured models, deep models present superiority in capturing multimodal correlations due to their high… (More)

- Mingdong Ou, Peng Cui, Jun Wang, Fei Wang, Wenwu Zhu
- AAAI
- 2015

Due to the simplicity and efficiency, many hashing methods have recently been developed for large-scale similarity search. Most of the existing hashing methods focus on mapping low-level features to binary codes, but neglect attributes that are commonly associated with data samples. Attribute data, such as image tag, product brand, and user profile, can… (More)

- Mingdong Ou, Peng Cui, Fei Wang, Jun Wang, Wenwu Zhu
- KDD
- 2015

Approximating the semantic similarity between entities in the learned Hamming space is the key for supervised hashing techniques. The semantic similarities between entities are often non-transitive since they could share different latent similarity components. For example, in social networks, we connect with people for various reasons, such as sharing… (More)

- Daixin Wang, Peng Cui, Mingdong Ou, Wenwu Zhu
- IEEE Trans. Multimedia
- 2015

As large-scale multimodal data are ubiquitous in many real-world applications, learning multimodal representations for efficient retrieval is a fundamental problem. Most existing methods adopt shallow structures to perform multimodal representation learning. Due to a limitation of learning ability of shallow structures, they fail to capture the correlation… (More)

- Daixin Wang, Peng Cui, Mingdong Ou, Wenwu Zhu
- 2015

Hashing is an important method for performing efficient similarity search. With the explosive growth of multimodal data, how to learn hashing-based compact representations for multimodal data becomes highly non-trivial. Compared with shallowstructured models, deep models present superiority in capturing multimodal correlations due to their high… (More)

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