隨著網際網路的快速發展，網路幾乎成為大家尋找資訊、學習知識的主要管道，許多不同領域、不同目的的社群網路(Social Network)逐漸形成，成為人們尋求與分享資訊的主要平台。許多知識性質的社群網路也隨之興起，以互動式問答的方式，提供學習者與專家之間知識交流的媒介，隨者使用者、發文數、回答數的增加，知識平台的知識量也急速增加，但是也造成了資訊過載的問題，反而導致學習者不易在平台中尋得所需資訊，知識提供者(本研究稱之為”專家”)面對過多重複性的問題也可能失去了回答的意願，且許多有能力回答問題的潛在專家並未主動知道有人需要解決問題。因此，如何提供準確的有效答案與專家，以使專家可有效率的回答問題，成了亟需解決的問題。
　　為了解決上述問題，過去研究相繼提出文件推薦系統及專家推薦系統以提供發問者所需資訊，在文字解答上著重於分析使用者所下的Query，並加入個人化技術與本體論(Ontology)進一步改善其推薦準確性，在專家推薦上則著重於分析該Query所屬的領域與使用者資訊以推薦相關專家；然而過去的推薦系統多半只提供單一資訊，並未考量發問者可能需要文字解答並與相關專家諮詢互動才足以解決問題。此外，過去研究在資訊的分析上也未考量發問者的問題可能包含多個領域以及每一位平台的使用者本身的學習效應，造成發問者的問題未能完全解決，而潛在專家未被主動告知導致大部分的問題仍由少部分的專家進行重覆回答，造成少部分專家的負擔。為改善上述缺點，本研究將專家推薦系統結合文件多分類分析以同時推薦平台中的潛在專家與可能答案，以減少過多重複發問的可能。
　　本研究將以知識平台Yahoo!知識+為例，提出一個在使用者發問時，同時推薦解答與專家的個人化服務系統，利用文件多分類、文件分群、投票模型與資訊擷取等技術，分析互動識問答的知識平台-Yahoo!奇摩知識+中的問題與專家資訊，並以MRR評估推薦準確度。

Nowadays, Internet has become an important tool for searching information. Whenever users get problems, they post their questions on the platform and wait for answers from other knowledge providers which are defined as experts in this paper. Experts also shared their knowledge on the platform they are interested in. Some researches regard such platforms as problem solving platforms. However, similar question always be posted repeatedly and knowledge provider may lose their patient to answer the same question. It also faces the information overloading problem. So, how to provide precise answer and consultant to the learner and invite the suitable expert to share their knowledge becomes an urgent problem to be solved.
　　Traditional approach used some data mining technique to analyze the learner’s query and try to find out the trustworthy experts or related document to recommend to the learners. However, single solution can’t totally solve the learner’s problem when there are more than one issue discussed in learner’s question and the learner may need someone to consult if the text information doesn’t meet their requirement. Past expert recommendation system providing consultant without considering the learning effect of each user also lead the unbalances the loading of each expert. Many potential experts doesn’t know they are able to provide solution so that only few experts handle all the questions.
　　In this paper, we present a novel approach combining multipath classification, voting model, and user learning effect into a personalized answer and expert finding task. The experiment result evaluated by MRR shows that our system performance is better than the Yahoo! Knowledge Plus Search Engine.

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