本文選題：用戶交互式問(wèn)答系統(tǒng) + 問(wèn)題推薦機(jī)制　； 參考：《中國(guó)科學(xué)技術(shù)大學(xué)》2012年博士論文

【摘要】：在計(jì)算機(jī)網(wǎng)絡(luò)技術(shù)迅速發(fā)展的今天,互聯(lián)網(wǎng)應(yīng)用得到迅速普及。用戶交互式問(wèn)答系統(tǒng)作為Web2.0時(shí)代的典型應(yīng)用已經(jīng)成為現(xiàn)今最流行的社交網(wǎng)絡(luò)應(yīng)用之一,它為互聯(lián)網(wǎng)用戶提供了一個(gè)搜索信息和共享知識(shí)的平臺(tái)。相較于從搜索引擎獲取信息的方式,用戶在交互式問(wèn)答系統(tǒng)中通過(guò)簡(jiǎn)單的提問(wèn)和回答方式快速準(zhǔn)確的獲取所需信息,而不是從搜索引擎返回的大量相關(guān)文檔中繁瑣地去查找信息。用戶交互式問(wèn)答系統(tǒng)雖然為人們提供了獲取信息的便捷服務(wù),但是依然存在著各種各樣的問(wèn)題,用戶等待答案時(shí)間長(zhǎng)和答案質(zhì)量差是其中最顯著的兩個(gè)問(wèn)題。在用戶交互式問(wèn)答系統(tǒng)中,提問(wèn)者有時(shí)候需要等待幾個(gè)小時(shí)甚至是幾天的時(shí)間來(lái)等待其他用戶提供答案。此外,一些用戶為了獲取交互式問(wèn)答系統(tǒng)中的積分等級(jí),提供很多不相關(guān)答案甚至是垃圾答案,這些問(wèn)題都大大降低了用戶獲取所需信息的效率。 為了提高交用戶互式問(wèn)答系統(tǒng)的性能,解決系統(tǒng)中存在大量零回答問(wèn)題和低質(zhì)量答案的問(wèn)題,本文提出了在用戶交互式問(wèn)答系統(tǒng)中建立問(wèn)題推薦的機(jī)制。將系統(tǒng)中尚未被人回答的問(wèn)題,通過(guò)推薦機(jī)制將其推送給合適的專家用戶去回答,以提高回答效率和答案質(zhì)量。本文首先對(duì)于用戶交互式問(wèn)答系統(tǒng)中的問(wèn)題推薦機(jī)制給出了定義并詳細(xì)描述了問(wèn)題推薦的模型。在此基礎(chǔ)上,本文隨后提出了兩種不同的問(wèn)題推薦策略,分別是將未解決的問(wèn)題推薦給領(lǐng)域問(wèn)答專家和將問(wèn)題推薦給特定的問(wèn)答專家來(lái)回答。 在第一種問(wèn)題推薦策略中,同一類別下的問(wèn)題將會(huì)被推薦給該類別領(lǐng)域中的問(wèn)答專家用戶。本文分別提出了語(yǔ)義鏈接分析方法和語(yǔ)義語(yǔ)言模型方法來(lái)發(fā)現(xiàn)領(lǐng)域問(wèn)答專家。在語(yǔ)義鏈接分析方法中,我們首先根據(jù)在用戶交互式問(wèn)答系統(tǒng)中各個(gè)用戶之間的問(wèn)答關(guān)系構(gòu)建用戶問(wèn)答關(guān)系圖。在這個(gè)關(guān)系圖中,每一個(gè)結(jié)點(diǎn)代表一個(gè)用戶,結(jié)點(diǎn)之間的每一條連接邊代表用戶之間的問(wèn)答關(guān)系。其次,我們從用戶所參與問(wèn)題會(huì)話的交互行為和問(wèn)答內(nèi)容中抽取出不同類型的語(yǔ)義信息,并將這些語(yǔ)義信息結(jié)合到傳統(tǒng)鏈接分析方法中,衍生出新的語(yǔ)義鏈接分析方法。在該新方法中,用戶之間的問(wèn)答鏈接關(guān)系融入了諸如問(wèn)題難度、答案相關(guān)性、答案質(zhì)量、隱性鏈接等語(yǔ)義信息,從而產(chǎn)生出具有不同權(quán)重的鏈接關(guān)系。最后,我們?cè)趲в姓Z(yǔ)義信息的用戶問(wèn)答關(guān)系圖上執(zhí)行一個(gè)名為繁殖計(jì)算的鏈接分析方法,來(lái)計(jì)算每一個(gè)用戶的專家程度值,用戶獲得較高計(jì)算值的將會(huì)被認(rèn)為更加專家,獲得最高值的前1%用戶將會(huì)被認(rèn)為是領(lǐng)域問(wèn)答專家。在語(yǔ)義語(yǔ)言模型方法中,我們?cè)趥鹘y(tǒng)語(yǔ)言模型的方法中融入抽取出的各種語(yǔ)義信息,將其作為權(quán)重因素考慮到傳統(tǒng)語(yǔ)言模型中。通過(guò)計(jì)算用戶在某一問(wèn)題類別下是否為問(wèn)答專家的條件概率來(lái)查找出領(lǐng)域問(wèn)答專家。通過(guò)在用戶交互式問(wèn)答系統(tǒng)Yahoo! Answers中獲取的數(shù)據(jù)上進(jìn)行的實(shí)驗(yàn),驗(yàn)證了我們提出的語(yǔ)義鏈接分析方法和語(yǔ)義語(yǔ)言模型方法在領(lǐng)域問(wèn)題專家發(fā)現(xiàn)問(wèn)題上較傳統(tǒng)方法有了顯著的提高。此外,實(shí)驗(yàn)也同樣驗(yàn)證了抽取出的語(yǔ)義信息對(duì)于提高領(lǐng)域?qū)＜野l(fā)現(xiàn)方法性能的有效性。 相較于第一種問(wèn)題推薦策略的粗粒度性,第二種問(wèn)題推薦策略旨在發(fā)現(xiàn)能回答某一未解決問(wèn)題的特定專家用戶,并將該問(wèn)題推薦給特定問(wèn)答專家回答。在此問(wèn)題推薦策略中,我們首先根據(jù)用戶回答過(guò)的問(wèn)題信息建立用戶問(wèn)答檔案文件,在此基礎(chǔ)上建立起一個(gè)基于主題的用戶興趣模型,在該模型中用戶問(wèn)答檔案被認(rèn)為是在不同主題上的一個(gè)分布,通過(guò)吉布斯抽樣的方法,我們可以有效的獲得用戶興趣在主題上的準(zhǔn)確分布。然后,根據(jù)已經(jīng)獲取的用戶興趣主題模型,我們可以有效地計(jì)算出用戶是否為某一問(wèn)題的特定問(wèn)答專家的概率值。概率值計(jì)算結(jié)果越高的用戶將會(huì)被認(rèn)為在回答該問(wèn)題上專家程度越高。通過(guò)在用戶交互式問(wèn)答系統(tǒng)Yahoo! Answers中獲取的數(shù)據(jù)上進(jìn)行的實(shí)驗(yàn),驗(yàn)證了我們所提出的兩種不同問(wèn)題推薦機(jī)制的高效性。進(jìn)一步地,根據(jù)實(shí)驗(yàn)結(jié)果我們對(duì)比了兩種不同問(wèn)題推薦策略的性能。從實(shí)驗(yàn)結(jié)果中,我們可以發(fā)現(xiàn)第一種問(wèn)題推薦策略較優(yōu)于第二種策略。出現(xiàn)該現(xiàn)象的原因可能是,在第一種發(fā)現(xiàn)領(lǐng)域?qū)＜业膯?wèn)題推薦策略中覆蓋了絕大部分的問(wèn)答專家用戶,而第二種發(fā)現(xiàn)特定問(wèn)題問(wèn)答專家方法只能查找出部分問(wèn)答專家,這導(dǎo)致了第二種問(wèn)題推薦策略在性能上略遜于第一種策略。該對(duì)比實(shí)驗(yàn)結(jié)果為用戶交互式問(wèn)答系統(tǒng)中問(wèn)題推薦機(jī)制的策略選擇提供了重要的參考。
[Abstract]:With the rapid development of computer network technology, the Internet application has been popularized rapidly. The user interactive question answering system, as a typical application of the Web2.0 era, has become one of the most popular social network applications. It provides a platform for Internet users to search information and share knowledge. In the way of information, users can get the information quickly and accurately through simple questions and answers in interactive question answering system, instead of searching for information from a large number of relevant documents returned by the search engine. The user interactive question answering system still has a convenient service for people to obtain information. In a variety of questions, the user waiting for a long answer and poor answer is the two most significant problem. In the user interactive question answering system, the questioner sometimes has to wait a few hours or even a few days to wait for the other users to provide answers. In addition, some users have to get the points in the interactive Q & a system. Level, which provides many irrelevant answers or even garbage answers, greatly reduces the efficiency of users getting the information they need.
In order to improve the performance of interuser reciprocal question answering system and solve the problem of zero answer and low quality answer in the system, this paper proposes a mechanism to establish a problem recommendation in the user interactive question answering system. The problem that has not been answered in the system is pushed to the appropriate expert user to answer by the recommendation mechanism. In order to improve the answer efficiency and answer quality, this paper first defines the problem recommendation mechanism in the user interactive question answering system and describes the recommended model in detail. On the basis of this, this paper proposes two different recommendation strategies, which are to recommend the unresolved question to the domain question and answer expert and the question. The question is recommended to a particular question and answer expert to answer.
In the first problem recommendation strategy, the problem under the same category will be recommended to the question and answer expert users in the category domain. The semantic link analysis method and the semantic language model method are proposed to find the domain question and answer expert respectively. In the semantic link analysis, we first based on the user interactive question answering system. In this relationship diagram, each node represents a user, and each connection between nodes represents a question and answer relationship between the users. Secondly, we extract different types of semantic information from the interactive behavior and question and answer content of the user's participation in the question session. And combining these semantic information into the traditional link analysis method, a new method of semantic link analysis is derived. In this new method, the question answer link relationship among users is integrated into the semantic information such as problem difficulty, answer relevance, answer quality, recessive link and so on. We execute a link analysis method named reproduction calculation on the user question answering graph with semantic information to calculate the expert degree value of each user. The top 1% users who obtain higher values will be considered to be more experts. The top 1% users will be considered as domain question and answer experts. In the traditional language model, we incorporate the various semantic information extracted from the traditional language model, and consider it as the weight factor in the traditional language model. By calculating the conditional probability of a question answering expert under a certain problem category, we find out the domain question and answer expert. Through the user interactive question answering system Yahoo! Answers The experiments carried out on the obtained data verify that the semantic link analysis and the semantic language model method proposed by us have improved significantly on the problem of expert discovery in domain problems. In addition, the experiment also validates the effectiveness of the extraction of semantic information for improving the performance of domain expert discovery methods.
Compared to the coarse granularity of the first problem recommendation strategy, the second problem recommendation strategy aims to find a specific expert user who can answer a certain unsolved problem and recommend it to a specific question answer expert. In this recommendation strategy, we first establish a user question and answer file based on the question information returned by the user. On this basis, a user interest model based on the theme is established. In this model, the user question and answer file is considered to be a distribution on different topics. Through the Gibbs sampling method, we can effectively obtain the accurate distribution of the user interest on the subject. Then, according to the subject model of the user interest that has been obtained, I We can effectively calculate the probability of whether a user is a particular question answering expert. The higher the probability of a user will be considered to be more expert in answering the question. The two kinds of experiments we have proposed through the experiment on the data obtained in the user interactive question answering system Yahoo! Answers According to the experimental results, we can find that the first problem recommendation strategy is better than the second strategy. The reason for the emergence of this phenomenon may be the problem recommendation strategy in the first field of discovery experts. It covers most of the question answer expert users, and the second kinds of question answering expert method can only find out some questions and answer experts. This leads to the second problem recommendation strategy less than the first strategy in performance. The comparison experiment results provide the strategy selection of the problem recommendation mechanism in the user interactive question answering system. An important reference.
【學(xué)位授予單位】：中國(guó)科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP391.3

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前2條

1 劉曉鳴;社區(qū)問(wèn)答系統(tǒng)中的專家發(fā)現(xiàn)方法研究[D];大連理工大學(xué);2013年

2 吳瑞紅;互動(dòng)問(wèn)答社區(qū)中回答可信性分析[D];北京信息科技大學(xué);2013年

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本文編號(hào)：1923549