期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 51, 期 9, 页码 4501-4514出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2019.2951207
关键词
Feature extraction; Kernel; Task analysis; Cybernetics; Gallium nitride; Twitter; Cooperation learning; multiple social networks; source complementarity; source consistency
类别
资金
- National Natural Science Foundation of China [61772310, 61702300, 61702302, 61802231, U1836216]
- Project of Thousand Youth Talents
- Shandong Provincial Natural Science and Foundation [ZR2019JQ23, ZR2019QF001]
- Young Scholars Program of Shandong University
The study revealed that the average Internet user has 5.54 social media accounts, with 2.82 of them being actively used. Different social networks capture overlapping and complementary characteristics of the same user, leading to the proposal of a unified model to improve learning performance.
GWI survey(1) has highlighted the flourishing use of multiple social networks: the average number of social media accounts per Internet user is 5.54, and among them, 2.82 are being used actively. Indeed, users tend to express their views in more than one social media site. Hence, merging social signals of the same user across different social networks together, if available, can facilitate the downstream analyses. Previous work has paid little attention on modeling the cooperation among the following factors when fusing data from multiple social networks: 1) as data from different sources characterizes the characteristics of the same social user, the source consistency merits our attention; 2) due to their different functional emphases, some aspects of the same user captured by different social networks can be just complementary and results in the source complementarity; and 3) different sources can contribute differently to the user characterization and hence lead to the different source confidence. Toward this end, we propose a novel unified model, which co-regularizes source consistency, complementarity, and confidence to boost the learning performance with multiple social networks. In addition, we derived its theoretical solution and verified the model with the real-world application of user interest inference. Extensive experiments over several state-of-the-art competitors have justified the superiority of our model. (1) http://tinyurl.com/zk6kgc9
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据