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Bolong Zheng
Researcher at Huazhong University of Science and Technology
Publications - 88
Citations - 1412
Bolong Zheng is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Computer science & Trajectory. The author has an hindex of 17, co-authored 75 publications receiving 844 citations. Previous affiliations of Bolong Zheng include University of Queensland & Aalborg University.
Papers
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Journal ArticleDOI
A Context-Aware User-Item Representation Learning for Item Recommendation
TL;DR: CARLiu et al. as mentioned in this paper proposed a context-aware user-item representation learning model for rating prediction, which adopts Factorization Machines to further model higher order feature interactions on the basis of the useritem pair.
Proceedings ArticleDOI
Interactive Top-k Spatial Keyword queries
TL;DR: Empirical study based on real PoI datasets verifies the theoretical observation that the quality of top-k results in spatial keyword queries can be greatly improved through only a few rounds of interactions.
Journal ArticleDOI
A survey of trajectory distance measures and performance evaluation
TL;DR: A comprehensive survey of the trajectory distance measures is conducted, classified into four categories according to the trajectory data type and whether the temporal information is measured.
Proceedings ArticleDOI
Approximate keyword search in semantic trajectory database
TL;DR: An efficient search algorithm and fast evaluation of the minimum value of spatio-textual utility function are proposed and the results of empirical studies based on real check-in datasets demonstrate that the proposed index and algorithms can achieve good scalability.
Proceedings ArticleDOI
Multiple Rumor Source Detection with Graph Convolutional Networks
TL;DR: This paper proposes a deep learning based model, namely GCNSI (Graph Convolutional Networks based Source Identification), to locate multiple rumor sources without prior knowledge of underlying propagation model by adopting spectral domain convolution.