1 code implementation • 21 Oct 2023 • Chuang Zhao, Hongke Zhao, HengShu Zhu, Zhenya Huang, Nan Feng, Enhong Chen, Hui Xiong
One prevalent solution is the bi-discriminator domain adversarial network, which strives to identify target domain samples outside the support of the source domain distribution and enforces their classification to be consistent on both discriminators.
no code implementations • 15 Aug 2023 • Likang Wu, Junji Jiang, Hongke Zhao, Hao Wang, Defu Lian, Mengdi Zhang, Enhong Chen
However, the multi-faceted semantic orientation in the feature-semantic alignment has been neglected by previous work, i. e. the content of a node usually covers diverse topics that are relevant to the semantics of multiple labels.
no code implementations • 14 Jun 2023 • Likang Wu, Zhi Li, Hongke Zhao, Zhefeng Wang, Qi Liu, Baoxing Huai, Nicholas Jing Yuan, Enhong Chen
Zero-Shot Learning (ZSL), which aims at automatically recognizing unseen objects, is a promising learning paradigm to understand new real-world knowledge for machines continuously.
no code implementations • 26 Jan 2023 • Chuang Zhao, Hongke Zhao, Ming He, Jian Zhang, Jianping Fan
Specifically, we first construct a unified cross-domain heterogeneous graph and redefine the message passing mechanism of graph convolutional networks to capture high-order similarity of users and items across domains.
no code implementations • 18 Apr 2022 • Likang Wu, Hao Wang, Enhong Chen, Zhi Li, Hongke Zhao, Jianhui Ma
To that end, we propose a novel framework to promote cascade size prediction by enhancing the user preference modeling according to three stages, i. e., preference topics generation, preference shift modeling, and social influence activation.
no code implementations • Findings (ACL) 2022 • Kai Zhang, Kun Zhang, Mengdi Zhang, Hongke Zhao, Qi Liu, Wei Wu, Enhong Chen
Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a specific aspect in the given sentence.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2
no code implementations • 27 May 2021 • Likang Wu, Zhi Li, Hongke Zhao, Qi Liu, Enhong Chen
Usually, this prediction is always with great challenges to making a comprehensive understanding of both the start-up project and market environment.
1 code implementation • 16 Jan 2021 • Likang Wu, Zhi Li, Hongke Zhao, Qi Liu, Jun Wang, Mengdi Zhang, Enhong Chen
Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole, while the implicit semantic associations behind highly complex interactions of graphs are largely unexploited.
no code implementations • 8 Jun 2020 • Zhi Li, Bo Wu, Qi Liu, Likang Wu, Hongke Zhao, Tao Mei
Towards this end, in this paper, we propose a novel Content Attentive Neural Network (CANN) to model the comprehensive compositional coherence on both global contents and semantic contents.
no code implementations • 14 Dec 2019 • Likang Wu, Zhi Li, Hongke Zhao, Zhen Pan, Qi Liu, Enhong Chen
In the crowdfunding market, the early fundraising performance of the project is a concerned issue for both creators and platforms.
no code implementations • 1 Jun 2019 • Binbin Jin, Enhong Chen, Hongke Zhao, Zhenya Huang, Qi Liu, HengShu Zhu, Shui Yu
Existing solutions mainly exploit the syntactic or semantic correlation between a question and its related answers (Q&A), where the multi-facet domain effects in CQA are still underexplored.
no code implementations • 23 May 2019 • Qi Liu, Shiwei Tong, Chuanren Liu, Hongke Zhao, Enhong Chen, Haiping Ma, Shijin Wang
Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e. g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items.
no code implementations • 3 Aug 2018 • Zhi Li, Hongke Zhao, Qi Liu, Zhenya Huang, Tao Mei, Enhong Chen
In this paper, we propose a novel Behavior-Intensive Neural Network (BINN) for next-item recommendation by incorporating both users' historical stable preferences and present consumption motivations.