Influential user recommendation through SVD based topic diversification

Lun Wang, Tun Lu, Hansu Gu, Xianghua Ding, Ning Gu

Abstract: With the development of web 2.0, online community websites have become one of the most interactive ways for disabilities to communicate with others and understand the world. However, new disabled users may sometimes find it difficult to learn about existing communities and the topics they feel interested in are drastically different from normal users. On the other hand, influential users have already developed their impact in different topics within communities and are hence able to help disabilities quickly adapt to the communities. As the result, a method that can recommend influential users in diverse topics is necessary. However, traditional approaches to identify influential users are not very comprehensive and accurate without the consideration of topic diversity. In this paper, we present an evaluation index system and a SVD based influence model which can evaluate influence comprehensively. The discussions in online communities are then classified by the topics that attract disabilities most. Finally, a combined influential users recommendation with diverse topics is proposed. We conduct experiments on an online disability community dataset and results show that our approach significantly outperforms other traditional methods.