【摘要】：在信息檢索領域,與根據(jù)用戶鍵入關鍵字進行檢索的搜索引擎相比,互動問答平臺使用了語義更加豐富的自然語言。百度知道、Yahoo!Answers、Quora,以及目前人氣頗高的知乎,這些互動問答平臺已經(jīng)成為用戶獲取信息、分享知識的重要渠道。但隨著互動問答平臺的不斷發(fā)展,用戶數(shù)和問答量驟增。對任何一個用戶而言,剛提交的問題可能很快就被其他用戶新提交的問題給淹沒。這種現(xiàn)象帶來的后果便是用戶提出的問題可能要過很長時間才會有其他用戶去回答。與此同時,用戶得到的回答可能并不能令其滿意,甚至包含了大量垃圾信息。 本文嘗試通過對專家發(fā)現(xiàn)和問題推薦機制的研究,幫助被動等待的提問者在盡可能短的時間內(nèi)得到問題的回答,并且這些回答是令其感到滿意的。本文首先通過統(tǒng)計方法,分析并總結互動問答平臺中的問答情況及其特點。然后,提出了改進的PageRank算法并將其應用到問答社區(qū)中的專家發(fā)現(xiàn)過程。最后,基于對問答專家發(fā)現(xiàn)的研究,設計了互動問答平臺的問題推薦架構和推薦流程,旨在針對待解決的問題,系統(tǒng)自動將問題推薦到合適的用戶處作答。 作者使用Java代碼實現(xiàn)了本文提出的算法,通過實驗證明了本文提出的問答專家發(fā)現(xiàn)方法的有效性和可行性,并通過基于問題推薦的示例原型系統(tǒng)展示了問題推薦的流程。
[Abstract]:In the field of information retrieval, the interactive question and answer platform uses a more semantic natural language than the search engine which searches according to the key words typed by the user. Baidu knows that Yahoo AnswersQuora, and the current popularity of these interactive Q & A platforms have become an important channel for users to get information and share knowledge. However, with the continuous development of interactive Q & A platform, the number of users and the number of Q & A have increased. For any user, a newly submitted question may soon be overwhelmed by a new one submitted by another user. The consequence is that it may take a long time for other users to answer questions. At the same time, users may not be satisfied with the answer, and even contain a lot of spam. This paper attempts to help the passive questioner get the answer to the question in the shortest possible time by studying the mechanism of expert discovery and question recommendation, and these answers are satisfactory to him. This paper firstly analyzes and summarizes the Q & A and its characteristics in the interactive Q & A platform by means of statistical method. Then, an improved PageRank algorithm is proposed and applied to the expert discovery process in the Q & A community. Finally, based on the research of question and answer experts, the question recommendation framework and process of interactive question answering platform are designed, aiming at solving the problems, the system automatically recommends the questions to the appropriate users to answer. The author uses Java code to realize the algorithm proposed in this paper. The experiment proves the validity and feasibility of the method of question and answer expert discovery, and shows the flow of problem recommendation through an example prototype system based on question recommendation.
【學位授予單位】：華東師范大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP391.3

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本文編號：2326045