@inproceedings{7724787bbd6b4250aa403109c1a3e1ef,
title = "DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion Prediction",
abstract = "This paper describes a novel diffusion model, DyDiff-VAE, for information diffusion prediction on social media. Given the initial content and a sequence of forwarding users, DyDiff-VAE aims to estimate the propagation likelihood for other potential users and predict the corresponding user rankings. Inferring user interests from diffusion data lies the foundation of diffusion prediction, because users often forward the information in which they are interested or the information from those who share similar interests. Their interests also evolve over time as the result of the dynamic social influence from neighbors and the time-sensitive information gained inside/outside the social media. Existing works fail to model users' intrinsic interests from the diffusion data and assume user interests remain static along the time. DyDiff-VAE advances the state of the art in two directions: (i) We propose a dynamic encoder to infer the evolution of user interests from observed diffusion data. (ii) We propose a dual attentive decoder to estimate the propagation likelihood by integrating information from both the initial cascade content and the forwarding user sequence. Extensive experiments on four real-world datasets from Twitter and Youtube demonstrate the advantages of the proposed model; we show that it achieves 43.3%relative gains over the best baseline on average. Moreover, it has the lowest run-time compared with recurrent neural network based models.",
keywords = "attention, deep learning, diffusion prediction, social networks, variational autoencoder",
author = "Ruijie Wang and Zijie Huang and Shengzhong Liu and Huajie Shao and Dongxin Liu and Jinyang Li and Tianshi Wang and Dachun Sun and Shuochao Yao and Tarek Abdelzaher",
note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021 ; Conference date: 11-07-2021 Through 15-07-2021",
year = "2021",
month = jul,
day = "11",
doi = "10.1145/3404835.3462934",
language = "English (US)",
series = "SIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval",
publisher = "Association for Computing Machinery",
pages = "163--172",
booktitle = "SIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval",
address = "United States",
}