Incorporating message embedding into co-factor matrix factorization for retweeting prediction

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	Incorporating message embedding into co-factor matrix factorization for retweeting prediction
	Can Wang1,2 ; Qiudan Li1 ; Lei Wang1 ; Daniel Dajun Zeng1,2,3
	2017
会议名称	2017 International Joint Conference on Neural Networks, IJCNN 2017
页码	1265-1272
会议日期	2017 May 14-19
会议地点	Anchorage, AK, USA
摘要	With the rapid growth of Web 2.0, social media has become a prevalent information sharing and spreading platform, where users can retweet interesting messages. To better understand the propagation mechanism for information diffusion, it is necessary to model the user retweeting behavior and predict future retweets. Some existing work in retweeting prediction based on matrix factorization focuses on using user-message interaction information, user information and social influence information, etc. The challenge of improving prediction performance is how to jointly perform deep representation of these information to solve the sparsity problem and then learn a more comprehensive retweeting behavior model. Inspired by word2vec and co-factor matrix factorization model, this paper proposes a hybrid model, called HCFMF, for learning users’ retweeting behavior, it first computes the message content similarity by considering the message co-occurrence, the author information and word2vec based low-dimensional representation of content, then, jointly decomposes the user-message matrix and message-message similarity matrix based on a co-factorization model. We empirically evaluate the performance of the proposed model on real world weibo datasets. Experimental results show that taking the dense representation of author and content information into consideration could allow us make more accurate analysis of users’ retweeting patterns. The mined patterns could serve as a feedback channel for both consumers and management departments.
关键词	Retweeting Prediction Co-factor Matrix Factorization Word2vec Low-dimensional Representation
收录类别	EI
语种	英语
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/15396
专题	多模态人工智能系统全国重点实验室_互联网大数据与信息安全
通讯作者	Qiudan Li
作者单位	1.The State Key Laboratory of Management and Control for Complex Systems Institute of Automation, Chinese Academy of Sciences Beijing 100190, China 2.University of Chinese Academy of Sciences, Beijing, China 3.Department of Management Information Systems University of Arizona Tucson, Arizona, USA
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Can Wang,Qiudan Li,Lei Wang,et al. Incorporating message embedding into co-factor matrix factorization for retweeting prediction[C],2017:1265-1272.

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