Gang-Feng Ma
Biographic Data
| ID | 10055503 |
|---|---|
| NAME | Gang-Feng Ma |
| GIVEN NAMES | Gang-Feng |
| FAMILY NAME | Ma |
| SIGNATURE | MA G |
| AFFILIATIONS | Zhejiang University of Technology |
| ORCID | 0009-0004-8398-2923 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| 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…
No prominent works on this page.
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) · Graph (2 works) · Advanced Technologies in Various Fields (1 work) · Advanced Text Analysis Techniques (1 work) · Artificial Intelligence (1 work) · Computer Science (1 work) · Conversation (1 work) · Deep learning (1 work) · Dialog system (1 work) · Encoder (1 work)