Towards human-centric personalized expertise ranking in community-based question answering

Search engine has been the major source for discovering user-generated content with authority, not only on content-centric multimedia but also on human-centric social networks. Many studies have demonstrated the power of graph-based ranking algorithms to propagate reputation and expertise along social graph composed of users' links for information search, to promote experts and demote spams. However, these existing works shed little light on personalized expertise ranking algorithm from view of the topic-relevance between users' interests. In this study, we demonstrated the existence of homophily in users' interests measured by social annotations using AskMeFi, a large scale community-driven question and answering (CQA) system. We discovered that best answers as rated by questioners (users posting questions), are inclined to arrive promptly from co-interest users with authority and topic-relevance after the questions are posted. We proposed Human-centric Personalized Expertise Ranking, a graph-based algorithm which takes the topic-relevance and authority among co-interest users and time traits into the computation of the expertise level of users. The experimental results revealed that our proposed algorithm significantly outperforms other non-personalized expertise ranking algorithms.