Attention-guided Multi-step Fusion: A Hierarchical Fusion Network for Multimodal Recommendation

Author:

Zhou Yan1ORCID,Guo Jie1ORCID,Sun Hao1ORCID,Song Bin1ORCID,Yu Fei Richard2ORCID

Affiliation:

1. Xidian University, Xi'an, China

2. Shenzhen University, Shenzhen, China

Funder

High-Performance Computing Platform of Xidian University

ISN State Key Laboratory

National Natural Science Foundation of China

Key Research and Development Program of Shaanxi

Publisher

ACM

Reference26 articles.

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2. Attentional Feature Fusion

3. Xavier Glorot and Yoshua Bengio . 2010 . Understanding the difficulty of training deep feedforward neural networks . In Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 249--256 . Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 249--256.

4. Trust-aware recommendation based on heterogeneous multi-relational graphs fusion

5. Wei Guo , Yang Yang , Yaochen Hu , Chuyuan Wang , Huifeng Guo , Yingxue Zhang , Ruiming Tang , Weinan Zhang , and Xiuqiang He . 2021 a. Deep graph convolutional networks with hybrid normalization for accurate and diverse recommendation . In Proceedings of 3rd Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD. Wei Guo, Yang Yang, Yaochen Hu, Chuyuan Wang, Huifeng Guo, Yingxue Zhang, Ruiming Tang, Weinan Zhang, and Xiuqiang He. 2021a. Deep graph convolutional networks with hybrid normalization for accurate and diverse recommendation. In Proceedings of 3rd Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD.

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