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
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
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.
条目包含的文件 下载所有文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
IJCNN 2017.pdf(315KB)会议论文 开放获取CC BY-NC-SA浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Can Wang]的文章
[Qiudan Li]的文章
[Lei Wang]的文章
百度学术
百度学术中相似的文章
[Can Wang]的文章
[Qiudan Li]的文章
[Lei Wang]的文章
必应学术
必应学术中相似的文章
[Can Wang]的文章
[Qiudan Li]的文章
[Lei Wang]的文章
相关权益政策
暂无数据
收藏/分享
文件名: IJCNN 2017.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。