Gang Ren

Orcid: 0000-0003-4689-0099

Affiliations:
  • Hefei University of Technology, School of Management, Key Laboratory of Process Optimization and Intelligent Decision-makin, China


According to our database1, Gang Ren authored at least 12 papers between 2019 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
Decomposing preferences with a large language model for accurate and interpretable rating prediction.
Inf. Sci., 2026

Predicting helpfulness of multimodal reviews with customer confirmation bias: A hierarchically trusted multi-view deep learning method.
Eng. Appl. Artif. Intell., 2026

2025
Hierarchically trusted evidential fusion method with consistency learning for multimodal language understanding.
Knowl. Based Syst., 2025

LLM-Enhanced Multi-Task Joint Learning Model for Misinformation Detection.
Inf. Process. Manag., 2025

Temporal-spatial hierarchical contrastive learning for misinformation detection: A public-behavior perspective.
Inf. Process. Manag., 2025

Enhancing the relationship between topic relevance and review helpfulness: the moderating role of negative emotional appeals.
Ind. Manag. Data Syst., 2025

2024
An evidence-based multimodal fusion approach for predicting review helpfulness with human-AI complementarity.
Expert Syst. Appl., March, 2024

A co-attention based multi-modal fusion network for review helpfulness prediction.
Inf. Process. Manag., January, 2024

2023
DMFN: A disentangled multi-level fusion network for review helpfulness prediction.
Expert Syst. Appl., October, 2023

SUDF-RS: A new foreign exchange rate prediction method considering the complementarity of supervised and unsupervised deep representation features.
Expert Syst. Appl., 2023

2019
Examining the relationship between specific negative emotions and the perceived helpfulness of online reviews.
Inf. Process. Manag., 2019

Understanding the topics of export cross-border e-commerce consumers feedback: an LDA approach.
Electron. Commer. Res., 2019


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