Xu-Hua Yang
Biographic Data
| ID | 7184588 |
|---|---|
| NAME | Xu-Hua Yang |
| GIVEN NAMES | Xu-Hua |
| FAMILY NAME | Yang |
| SIGNATURE | YANG X |
| AFFILIATIONS | Zhejiang University of Technology |
| ORCID | 0000-0001-9437-262X |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Knowledge-Aware Prompt-Tuning for Integrated Conversational Recommender System
Conversational recommender systems (CRSs) aim to mine user preferences and recommend appropriate items through natural language dialogue. A complete CRS typically consists of a recommendation module and a conversation module, which generate high-quality recommended items and fluent natural language responses, respectively. Existing research usually constructs and trains the two modules separately, leading to inconsistencies in input information a…
Contrastive Deep Fusion-Based Diffusion Multiview Drug Recommendation
Artificial Intelligence (AI)-driven drug recommendation, a pivotal application in current healthcare, leverages historical electronic health records to provide patients with a suitable and effective range of medications. Nevertheless, current drug recommendation methods fail to thoroughly integrated patients’ clinical data and overlook the handling of noise information. To address these challenges, we propose the contrastive deep fusion-based dif…
Graph Contrastive Learning for Multibehavior Recommendation
Multibehavior collaborative filtering recommendations can significantly alleviate data sparsity issues caused by insufficient single-behavior information, enhancing recommendation performance. However, current multibehavior recommendation methods simply concatenate different behavior representations without further exploring the interactive information between behaviors, thus limiting recommendation effectiveness. To address the limitations, we p…
The influence of disruption on evaluating the scientific significance of papers
Work engagement, tenure, and external opportunities moderate perceived high-performance work systems and affective commitment
We examined the role of our proposed moderators, namely, work engagement, organizational tenure, and perceived external opportunities, in the relationship between perceived high-performance work systems (HPWS) and affective organizational commitment. We conducted a survey with 94 employees of an information technology company in China. The results showed that the effect of perceived HPWS on affective commitment is stronger among employees with lo…
No prominent works on this page.
Work engagement, tenure, and external opportunities moderate perceived high-performance work systems and affective commitment
We examined the role of our proposed moderators, namely, work engagement, organizational tenure, and perceived external opportunities, in the relationship between perceived high-performance work systems (HPWS) and affective organizational commitment. We conducted a survey with 94 employees of an information technology company in China. The results showed that the effect of perceived HPWS on affective commitment is stronger among employees with lo…
The influence of disruption on evaluating the scientific significance of papers
Contrastive Deep Fusion-Based Diffusion Multiview Drug Recommendation
Artificial Intelligence (AI)-driven drug recommendation, a pivotal application in current healthcare, leverages historical electronic health records to provide patients with a suitable and effective range of medications. Nevertheless, current drug recommendation methods fail to thoroughly integrated patients’ clinical data and overlook the handling of noise information. To address these challenges, we propose the contrastive deep fusion-based dif…
Graph Contrastive Learning for Multibehavior Recommendation
Multibehavior collaborative filtering recommendations can significantly alleviate data sparsity issues caused by insufficient single-behavior information, enhancing recommendation performance. However, current multibehavior recommendation methods simply concatenate different behavior representations without further exploring the interactive information between behaviors, thus limiting recommendation effectiveness. To address the limitations, we p…
Knowledge-Aware Prompt-Tuning for Integrated Conversational Recommender System
Conversational recommender systems (CRSs) aim to mine user preferences and recommend appropriate items through natural language dialogue. A complete CRS typically consists of a recommendation module and a conversation module, which generate high-quality recommended items and fluent natural language responses, respectively. Existing research usually constructs and trains the two modules separately, leading to inconsistencies in input information a…
Recommender Systems and Techniques (3 works) · Artificial Intelligence (2 works) · Computer Science (2 works) · Graph (2 works) · Advanced Technologies in Various Fields (1 work) · Advanced Text Analysis Techniques (1 work) · Affective events theory (1 work) · Artificial Intelligence (1 work) · Benchmark (surveying) (1 work) · Biomedical Text Mining and Ontologies (1 work)