YongHeng Chen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
YongHeng Chen has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
Chunyan Yin
Business School, Lingnan Normal University, Zhanjiang, China
2 shared publications- Chunyan Yin
Business School, Lingnan Normal University, Zhanjiang, China
2 shared publications Wanli Zuo
College of Computer Science and Technology, Jilin University, Changchun, China
2 shared publications- Wanli Zuo
College of Computer Science and Technology, Jilin University, Changchun, China
2 shared publications Hao Yue
College of Computer Science Minnan Normal University 363000 Zhangzhou China
1 shared publicationYaojin Lin
College of Computer Science Minnan Normal University 363000 Zhangzhou China
1 shared publication
Latest Publications
- Open Access
- 19 October 2021
- Published by Springer Nature in International Journal of Computational Intelligence Systems
- Vol. 14 (1) , 1-10
Evolutionary Social Poisson Factorizationfor Temporal Recommendation
Preference-based recommendation systems analyze user-item interactions to reveal latent factors that explain our latent preferences for items and form personalized recommendations based on the behavior of others with similar tastes. Most of the works in the recommendation systems literature have been developed under the assumption that user preference is a static pattern, although user preferences and item attributes may be changed through time. To achieve this goal, we develop an Evolutionary Social Poisson Factorization (EPF $$\_$$ Social) model, a new Bayesian factorization model that can effectively model the smoothly drifting latent factors using Conjugate Gamma–Markov chains. Otherwise, EPF $$\_$$ Social can obtain the impact of friends on social network for user’ latent preferences. We studied our models with two large real-world datasets, and demonstrated that our model gives better predictive performance than state-of-the-art static factorization models.- 19 October 2021
- Published by Springer Nature in International Journal of Computational Intelligence Systems
- Vol. 14 (1) , 1-10
Open AccessEvolutionary Social Poisson Factorizationfor Temporal Recommendation
Preference-based recommendation systems analyze user-item interactions to reveal latent factors that explain our latent preferences for items and form personalized recommendations based on the behavior of others with similar tastes. Most of the works in the recommendation systems literature have been developed under the assumption that user preference is a static pattern, although user preferences and item attributes may be changed through time. To achieve this goal, we develop an Evolutionary Social Poisson Factorization (EPF $$\_$$ Social) model, a new Bayesian factorization model that can effectively model the smoothly drifting latent factors using Conjugate Gamma–Markov chains. Otherwise, EPF $$\_$$ Social can obtain the impact of friends on social network for user’ latent preferences. We studied our models with two large real-world datasets, and demonstrated that our model gives better predictive performance than state-of-the-art static factorization models. - 1 October 2021
- Published by Pleiades Publishing Ltd in Pattern Recognition and Image Analysis
- Vol. 31 (4) , 722-729
An Attention Hierarchical Topic Modeling
Probabilistic topic models have been used to detect topic-based content presentations when facing a collection of documents. However, topic models capture the semantic information according to reasonable simplifying hypotheses, which ignore the worthwhile word-order information. This paper proposes an attention hierarchical topic modeling, which adopts attention mechanism to unify topic embedding and word embedding together into a framework to enhance the clustering effect of hierarchical Dirichlet process. Otherwise, the multi-information integration Chinese restaurant franchise is adopted to construct this model, which further combines timestamp, user, and topic label to optimize topic modeling. Extensive experiments on real-life applications show that our model outperforms several strong baselines on document modeling and classification.
- 1 October 2021
- Published by Pleiades Publishing Ltd in Pattern Recognition and Image Analysis
- Vol. 31 (4) , 722-729
An Attention Hierarchical Topic Modeling
Probabilistic topic models have been used to detect topic-based content presentations when facing a collection of documents. However, topic models capture the semantic information according to reasonable simplifying hypotheses, which ignore the worthwhile word-order information. This paper proposes an attention hierarchical topic modeling, which adopts attention mechanism to unify topic embedding and word embedding together into a framework to enhance the clustering effect of hierarchical Dirichlet process. Otherwise, the multi-information integration Chinese restaurant franchise is adopted to construct this model, which further combines timestamp, user, and topic label to optimize topic modeling. Extensive experiments on real-life applications show that our model outperforms several strong baselines on document modeling and classification.
- 1 January 2021
- Published by Pleiades Publishing Ltd in Pattern Recognition and Image Analysis
- Vol. 31 (1) , 103-108
Multi-Task Deep Neural Networks for Joint Sarcasm Detection and Sentiment Analysis
Sentiment analysis and sarcasm detection are specialized areas in the field of information retrieval and natural language processing. Sentiment classification is closely correlated with sarcasm detection, where people usually adopt sarcasm to highlight their negative feeling. This paper proposes a novel multi-task deep neural networks for joint sarcasm detection and sentiment analysis (MT_SS). MT_SS train both tasks jointly using bidirectional gated recurrent unit with attention network module to obtain task-specific local feature representation while using convolutional neural networks to obtain global feature representation. The experiments on two datasets show that our proposed model outperforms the state-of-the-art approaches.
- 1 January 2021
- Published by Pleiades Publishing Ltd in Pattern Recognition and Image Analysis
- Vol. 31 (1) , 103-108
Multi-Task Deep Neural Networks for Joint Sarcasm Detection and Sentiment Analysis
Sentiment analysis and sarcasm detection are specialized areas in the field of information retrieval and natural language processing. Sentiment classification is closely correlated with sarcasm detection, where people usually adopt sarcasm to highlight their negative feeling. This paper proposes a novel multi-task deep neural networks for joint sarcasm detection and sentiment analysis (MT_SS). MT_SS train both tasks jointly using bidirectional gated recurrent unit with attention network module to obtain task-specific local feature representation while using convolutional neural networks to obtain global feature representation. The experiments on two datasets show that our proposed model outperforms the state-of-the-art approaches.
- 1 July 2020
- Published by Pleiades Publishing Ltd in Pattern Recognition and Image Analysis
- Vol. 30 (3) , 566
Erratum to: Jointly Image Annotation and Classification Based on Supervised Multi-Modal Hierarchical Semantic Model
- 1 July 2020
- Published by Pleiades Publishing Ltd in Pattern Recognition and Image Analysis
- Vol. 30 (3) , 566
Erratum to: Jointly Image Annotation and Classification Based on Supervised Multi-Modal Hierarchical Semantic Model
- Open Access
- 10 September 2015
- Published by Bentham Science Publishers Ltd. in The Open Cybernetics & Systemics Journal
- Vol. 9 (1) , 1170-1176
On-Line Labeled Topic Model
A large number of electronic documents are labeled using human-interpretable annotations. High-efficiency text mining on such data set requires generative model that can flexibly comprehend the significance of observed labels while simultaneously uncovering topics within unlabeled documents. This paper presents a novel and generalized on-line labeled topic model (OLT) tracking the time development of extracted topics through a structured multi-labeled data set. Our topic model has an incrementally updated principle based on time slices in an on-line fashion, and can detect dynamic trending for labeled topics in parallel. Empirical results are presented to demonstrate lower perplexity and high performance of our proposed model when compared with other models.
- 10 September 2015
- Published by Bentham Science Publishers Ltd. in The Open Cybernetics & Systemics Journal
- Vol. 9 (1) , 1170-1176
Open AccessOn-Line Labeled Topic Model
A large number of electronic documents are labeled using human-interpretable annotations. High-efficiency text mining on such data set requires generative model that can flexibly comprehend the significance of observed labels while simultaneously uncovering topics within unlabeled documents. This paper presents a novel and generalized on-line labeled topic model (OLT) tracking the time development of extracted topics through a structured multi-labeled data set. Our topic model has an incrementally updated principle based on time slices in an on-line fashion, and can detect dynamic trending for labeled topics in parallel. Empirical results are presented to demonstrate lower perplexity and high performance of our proposed model when compared with other models.