發(fā)布時(shí)間：2018-11-11 20:24

【摘要】：在信息檢索領(lǐng)域,與根據(jù)用戶(hù)鍵入關(guān)鍵字進(jìn)行檢索的搜索引擎相比,互動(dòng)問(wèn)答平臺(tái)使用了語(yǔ)義更加豐富的自然語(yǔ)言。百度知道、Yahoo!Answers、Quora,以及目前人氣頗高的知乎,這些互動(dòng)問(wèn)答平臺(tái)已經(jīng)成為用戶(hù)獲取信息、分享知識(shí)的重要渠道。但隨著互動(dòng)問(wèn)答平臺(tái)的不斷發(fā)展,用戶(hù)數(shù)和問(wèn)答量驟增。對(duì)任何一個(gè)用戶(hù)而言,剛提交的問(wèn)題可能很快就被其他用戶(hù)新提交的問(wèn)題給淹沒(méi)。這種現(xiàn)象帶來(lái)的后果便是用戶(hù)提出的問(wèn)題可能要過(guò)很長(zhǎng)時(shí)間才會(huì)有其他用戶(hù)去回答。與此同時(shí),用戶(hù)得到的回答可能并不能令其滿(mǎn)意,甚至包含了大量垃圾信息。 本文嘗試通過(guò)對(duì)專(zhuān)家發(fā)現(xiàn)和問(wèn)題推薦機(jī)制的研究,幫助被動(dòng)等待的提問(wèn)者在盡可能短的時(shí)間內(nèi)得到問(wèn)題的回答,并且這些回答是令其感到滿(mǎn)意的。本文首先通過(guò)統(tǒng)計(jì)方法,分析并總結(jié)互動(dòng)問(wèn)答平臺(tái)中的問(wèn)答情況及其特點(diǎn)。然后,提出了改進(jìn)的PageRank算法并將其應(yīng)用到問(wèn)答社區(qū)中的專(zhuān)家發(fā)現(xiàn)過(guò)程。最后,基于對(duì)問(wèn)答專(zhuān)家發(fā)現(xiàn)的研究,設(shè)計(jì)了互動(dòng)問(wèn)答平臺(tái)的問(wèn)題推薦架構(gòu)和推薦流程,旨在針對(duì)待解決的問(wèn)題,系統(tǒng)自動(dòng)將問(wèn)題推薦到合適的用戶(hù)處作答。 作者使用Java代碼實(shí)現(xiàn)了本文提出的算法,通過(guò)實(shí)驗(yàn)證明了本文提出的問(wèn)答專(zhuān)家發(fā)現(xiàn)方法的有效性和可行性,并通過(guò)基于問(wèn)題推薦的示例原型系統(tǒng)展示了問(wèn)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(xué)位授予單位】：華東師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前4條

1 邢春曉;高鳳榮;戰(zhàn)思南;周立柱;;適應(yīng)用戶(hù)興趣變化的協(xié)同過(guò)濾推薦算法[J];計(jì)算機(jī)研究與發(fā)展;2007年02期

2 費(fèi)洪曉;蔣，

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