Locating Query-oriented Experts in Microblog Search.
Qin Chen, Yan T. Yang, Qinmin Hu, Liang He · 2014
In this paper, we propose an approach to locating queryoriented experts in Microblog. We first define the experts by social influence and content relevance. Then, we adopt the BM25 model to calculate the content relevance of each account. For the social influence, we present a global-ranking algorithm as GUserRank and a topic-ranking algorithm as TUserRank after applying the LDA topic model. After that, we output the ranking expertise degree of each candidate for evaluation. Our experimental results show that the proposed approach is effective and promising. Especially, the topic-ranking algorithm achieves an improvement with 40.11% over the baseline. Furthermore, our approach does not rely on the data sets such that it can be duplicated in many fields.