User-level Weibo Recommendation incorporating Social Influence based on
Semi-Supervised Algorithm
release_3slglltwxffwrkzpnkjgvixtt4
by
Daifeng Li, Zhipeng Luo, Golden Guo-zheng Sun, Jie Tang, Jingwei Zhang
2012
Abstract
Tencent Weibo, as one of the most popular micro-blogging services in China,
has attracted millions of users, producing 30-60 millions of weibo (similar as
tweet in Twitter) daily. With the overload problem of user generate content,
Tencent users find it is more and more hard to browse and find valuable
information at the first time. In this paper, we propose a Factor Graph based
weibo recommendation algorithm TSI-WR (Topic-Level Social Influence based Weibo
Recommendation), which could help Tencent users to find most suitable
information. The main innovation is that we consider both direct and indirect
social influence from topic level based on social balance theory. The main
advantages of adopting this strategy are that it could first build a more
accurate description of latent relationship between two users with weak
connections, which could help to solve the data sparsity problem; second
provide a more accurate recommendation for a certain user from a wider range.
Other meaningful contextual information is also combined into our model, which
include: Users profile, Users influence, Content of weibos, Topic information
of weibos and etc. We also design a semi-supervised algorithm to further reduce
the influence of data sparisty. The experiments show that all the selected
variables are important and the proposed model outperforms several baseline
methods.
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