Bingjie Huang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Bingjie Huang has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Ao Xiang
School of Computer Science & Engineering (School of Cybersecurity) University of Electronic Science and Technology of China Digital Media Technology Chengdu Sichuan China
1 shared publication - Haowei Yang
Cullen College of Engineering Industrial Engineering University of Houston Houston USA
1 shared publication - Tianyao Zheng
Independent Researcher, China
1 shared publication Xinyu Guo
1 shared publication
Latest Publications
- Open Access
A Neural Matrix Decomposition Recommender System Model based on the Multimodal Large Language Model
Recommendation systems have become an important solution to information search problems. This article proposes a neural matrix factorization recommendation system model based on the multimodal large language model called BoNMF. This model combines BoBERTa's powerful capabilities in natural language processing, ViT in computer in vision, and neural matrix decomposition technology. By capturing the potential characteristics of users and items, and after interacting with a low-dimensional matrix composed of user and item IDs, the neural network outputs the results. recommend. Cold start and ablation experimental results show that the BoNMF model exhibits excellent performance on large public data sets and significantly improves the accuracy of recommendations.
Open AccessA Neural Matrix Decomposition Recommender System Model based on the Multimodal Large Language Model
Recommendation systems have become an important solution to information search problems. This article proposes a neural matrix factorization recommendation system model based on the multimodal large language model called BoNMF. This model combines BoBERTa's powerful capabilities in natural language processing, ViT in computer in vision, and neural matrix decomposition technology. By capturing the potential characteristics of users and items, and after interacting with a low-dimensional matrix composed of user and item IDs, the neural network outputs the results. recommend. Cold start and ablation experimental results show that the BoNMF model exhibits excellent performance on large public data sets and significantly improves the accuracy of recommendations.