Retrieval-Augmented Hypergraph for Multimodal Social Media Popularity Prediction-Reference-Cited by-同舟云学术

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Retrieval-Augmented Hypergraph for Multimodal Social Media Popularity Prediction

Published:2024-08-24 Issue: Volume:33 Page:445-455
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
language:
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Author:

Cheng Zhangtao¹^ORCID,Zhang Jienan¹^ORCID,Xu Xovee¹^ORCID,Trajcevski Goce²^ORCID,Zhong Ting³^ORCID,Zhou Fan³^ORCID

Affiliation:

1. University of Electronic Science and Technology of China, Chengdu, Sichuan, China

2. Iowa State University, Ames, Iowa, USA

3. University of Electronic Science and Technology of China & Kash Institute of Electronics and Information Industry, Chengdu, Sichuan, China

Funder

Kashgar Science and Technology Bureau

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3672041

Reference66 articles.

1. Alessia Antelmi, Gennaro Cordasco, Mirko Polato, Vittorio Scarano, Carmine Spagnuolo, and Dingqi Yang. 2023. A Survey on Hypergraph Representation Learning. Comput. Surveys (2023).

2. Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450 (2016).

3. Latent dirichlet allocation;Ng Y;Journal of machine Learning research 3,2003

4. Hypergraph Theory

5. Predicting retweet count using visual cues

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