An Approach for Recommending Group Experts on Question and Answering Sites
Question-and-answer (Q&A) sites can be understood as information systems where users generate and answer questions. Also, they can determine the top answers using the number of positive and negative votes from crowd knowledge and experts. Knowledge sharing sites have been rapidly growing in recent years. It is difficult for a user to find experts who can write great answers to their questions. Recent approaches have focused on recommendations from a single expert. However, a question may contain several topics. Thus, finding the experts group to answer the questions is the best solution. In this paper, we propose a new expert group-recommendation method for Q&A systems. First, the users’ profiles are built to determine experts and non-experts. Second, a topic modeling method is used to identify the topic of the question and matches it to corresponding experts. Third, a social graph is generated to find expert groups. In order to increase knowledge and avoid following the crowd, we require that the members of expert groups not only match the skill requirements to answer the questions but also be diverse. Diversity is an essential factor to promote the development of Q&A sites. Experimenting on Quora dataset shows that the method achieves promising results.
KeywordsRecommending-expert Question-answering Quora-expert-finding
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2017R1A2B4009410).
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