| 摘要(英) |
With the ever-changing technology, we humans have to be willing to keep on learning in order to avoid being demoted by the world. Therefore, the reasons above led to the rise of the community question answering websites, such as Stack Overflow, Yahoo Answers, Quora, Zhihu (知乎), and so on and so forth. Users can ask questions, answer questions, exchange and discuss ideas with each other in the above platform.
Although the rise of community question answering websites can surely bring great convenience to users, there is still room for improvement. Due to the large numbers of questions, most questions usually receive no response or get inappropriate answers. It is without doubt to rely on luck and time to get correct answers in time. Therefore, we believe that if we can find experts precisely in CQA websites, we can improve the efficiency of the participation rate by routing right questions to experts.
In this study, we firstly utilize TEM (Topic Expertise Model), which is an unsupervised model published by Yang, Liu, et al. (2013), for capturing the degree of expertise of question and answerer under different topic. Furthermore, we utilize History Post Embedding, which is published in this thesis by using word embedding techniques, to extract semantic meanings to enhance the understanding of question sets. Finally, we combine the vector of topical expertise with History Post Embedding and perform a recommendation formula to rank experts. We target Stack Overflow, which is one of the biggest computer programming field CQA websites in the world, as our research goal and obtain good result. Moreover, we expect the research result to be available on other CQA websites.
The main contribution of this thesis is combining TEM model with distributed representation of user historical information which can solve the problem of low participation rate in CQA websites when social network structure is not so complete.
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