Incorporating message embedding into co-factor matrix factorization for retweeting prediction
Can Wang1,2; Qiudan Li1; Lei Wang1; Daniel Dajun Zeng1,2,3
2017
Conference Name2017 International Joint Conference on Neural Networks, IJCNN 2017
Pages1265-1272
Conference Date2017 May 14-19
Conference PlaceAnchorage, AK, USA
Abstract
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.
KeywordRetweeting Prediction Co-factor Matrix Factorization Word2vec Low-dimensional Representation
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15396
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Corresponding AuthorQiudan Li
Affiliation1.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
Recommended Citation
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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