本文選題：查詢擴(kuò)展 + 眼動(dòng)(Eye　； 參考：《天津大學(xué)》2016年碩士論文

【摘要】：對(duì)于大部分用戶甚至是有經(jīng)驗(yàn)的用戶來(lái)說(shuō)如何形成一個(gè)較好的查詢能夠獲得更好的搜索結(jié)果仍然被認(rèn)為是信息檢索(Information Retrieval)的一大難題。查詢擴(kuò)展往往是提高檢索性能的有效方法。通過(guò)找出語(yǔ)義上與原始查詢比較相關(guān)的詞語(yǔ)、概念等,再結(jié)合用戶的原始查詢,使得擴(kuò)展之后的查詢能夠提供更多的積極信息來(lái)從海量信息中找出與用戶查詢相關(guān)的文檔,改善用戶搜索體驗(yàn)。傳統(tǒng)的查詢擴(kuò)展技術(shù)已經(jīng)在很大程度上解決了查全率(Recall)低下的問(wèn)題,但是對(duì)于查準(zhǔn)率(Precision)上卻很難去的較令人滿意的結(jié)果。個(gè)性化的查詢擴(kuò)展部分解決了查準(zhǔn)率較低的問(wèn)題。但是傳統(tǒng)的個(gè)性化的查詢擴(kuò)展往往利用用戶過(guò)去的搜索數(shù)據(jù)而且很難捕捉用戶在本次查詢中的需求動(dòng)態(tài)變化,很難實(shí)時(shí)地根據(jù)用戶與搜索引擎的交互來(lái)滿足用戶的查詢需求。眼動(dòng)(Eye Movements)能夠在不引起用戶注意的情況下實(shí)時(shí)地捕捉到用戶的注視信息,進(jìn)而提供用戶的實(shí)時(shí)搜索行為數(shù)據(jù),被視為用戶研究和個(gè)性化的搜索的一個(gè)全新的方向。因此,若能將眼動(dòng)技術(shù)應(yīng)用在當(dāng)前亟待解決的個(gè)性化的查詢擴(kuò)展上來(lái),將是一個(gè)全新的啟發(fā)式的研究方向,具有重大意義。論文的主要研究工作分為以下幾個(gè)方面:第一,對(duì)眼動(dòng)(Eye Movements)在IR上的主要應(yīng)用進(jìn)行了概述。除了介紹眼動(dòng)在IR上的應(yīng)用之外,著重介紹了如何利用眼動(dòng)(Eye Movements)實(shí)時(shí)捕捉用戶的動(dòng)態(tài)搜索數(shù)據(jù)以及如何利用捕捉之后的眼動(dòng)數(shù)據(jù)來(lái)進(jìn)行個(gè)性化的查詢擴(kuò)展。第二,介紹了主題模型與眼動(dòng)(Eye Movements)的結(jié)合方法。僅僅利用捕捉到的用戶的眼動(dòng)(Eye Movements)數(shù)據(jù)進(jìn)行個(gè)性化查詢擴(kuò)展詞的計(jì)算,還不能夠充分挖掘用戶的潛在搜索意圖,為此利用主題模型Latent Dirichlet Allocation(LDA)來(lái)發(fā)掘和用戶查詢潛在相關(guān)的查詢?cè)~,提高檢索成績(jī)。第三,建立實(shí)時(shí)查詢擴(kuò)展模型(Real-Time Query Expansion,RTQE)。通過(guò)創(chuàng)新性地結(jié)合眼動(dòng)和LDA,該模型能夠在用戶點(diǎn)擊若干篇文檔之后,記錄用戶的注視數(shù)據(jù),在用戶刷新當(dāng)前搜索結(jié)果界面或者點(diǎn)擊下一頁(yè)的同時(shí)根據(jù)用戶若干分鐘前的注視數(shù)據(jù)通過(guò)建立的RTQE模型重新對(duì)已有的搜索結(jié)果進(jìn)行排序和優(yōu)化,提升用戶體驗(yàn)。
[Abstract]:For most users and even experienced users, how to form a better query to obtain better search results is still considered a big problem in Information Retrieval. Query expansion is often an effective way to improve retrieval performance. By finding out the words, concepts and so on, which are related to the original query semantically, and combining with the original query of the user, the extended query can provide more positive information to find the documents related to the user query from the massive information. Improve the user search experience. The traditional query expansion technique has solved the problem of low recall to a great extent, but it is difficult to get satisfactory results for precision. The personalized query extension solves the problem of low precision. But the traditional personalized query expansion often makes use of the user's past search data and it is difficult to capture the dynamic changes of the user's demand in this query. It is difficult to satisfy the user's query demand according to the interaction between the user and the search engine in real time. Eye movements (Eye-Movements), which can capture the user's gaze information in real time without attracting the user's attention, and then provide the real-time search behavior data of the user, are regarded as a new direction of user research and personalized search. Therefore, if the eye movement technology can be extended to the current personalized query, it will be a new heuristic research direction and has great significance. The main research work is as follows: first, the main applications of Eye movements in IR are summarized. In addition to introducing the application of eye movement in IR, this paper mainly introduces how to use eye movements to capture the user's dynamic search data in real time and how to use the captured eye movement data to expand the query. Secondly, the method of combining theme model with Eye movements is introduced. Only using the captured user's eye movements to compute the extended words of personalized query can not fully excavate the potential search intention of the user. Therefore, the topic model named Latent Dirichlet Allocation (LDA) is used to discover the query terms related to the user's query potential. Improve retrieval results. Thirdly, real-time query expansion model (RTQE) is established. With an innovative combination of eye movements and LDAs, the model can record the user's gaze data after a user clicks several documents. At the same time the user refreshes the current search results interface or clicks the next page and reorders and optimizes the existing search results according to the user's gaze data several minutes ago through the RTQE model to improve the user experience.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.3

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本文編號(hào)：2102652