【摘要】：傳統(tǒng)搜索引擎能通過關(guān)鍵詞組合方式檢索召回查詢相關(guān)頁面,但還必須經(jīng)過人工方式選擇與查詢主題相關(guān)的信息。專家檢索是當(dāng)前垂直信息檢索研究的熱門領(lǐng)域,是針對(duì)專家特征而開展的更精確的信息檢索方式,其能夠提供多種形式主題相關(guān)查詢,且直接返回與查詢主題最相關(guān)的專家列表或主頁,是當(dāng)前最有效的專家信息獲取手段。專家排序模型是專家搜索的核心,專家排序的效果決定了整個(gè)專家檢索系統(tǒng)的性能。因此,構(gòu)造高效的專家排序模型成為關(guān)鍵。本文對(duì)專家排序方法作了一定的探討,致力于如何融合專家證據(jù)文檔、專家關(guān)系及專家元數(shù)據(jù)等特征信息構(gòu)建基于列表的專家排序模型,進(jìn)而提高專家排序效果。主要在以下幾個(gè)方面展開深入研究,取得了一定的成果： (1)分析了影響專家排序的因素,定義了用于專家排序的三大種類特征。針對(duì)專家排序任務(wù),研究查詢與專家頁面及證據(jù)文檔之間的相關(guān)性,分析專家證據(jù)文檔、專家關(guān)系網(wǎng)、專家元數(shù)據(jù)等因素對(duì)專家檢索排序影響,提取相似度特征、BM25評(píng)分、專家頁面內(nèi)容特征、專家關(guān)聯(lián)關(guān)系特征。后續(xù)的實(shí)驗(yàn)表明,融入上述特征有效地提高了專家排序的效果。 (2)提出了基于ListNet的多特征融合的專家排序方法。該方法首先對(duì)專家的頁面特點(diǎn)進(jìn)行分析,選取查詢和專家候選頁面相關(guān)性特征、專家頁面內(nèi)容及專家頁面間關(guān)聯(lián)關(guān)系特征,然后,將特征融合到ListNet排序模型中,通過梯度下降法學(xué)習(xí)參數(shù),構(gòu)建基于列表的融合多特征的專家排序模型,最后,利用訓(xùn)練好的模型進(jìn)行專家排序?qū)Ρ葘?shí)驗(yàn),實(shí)驗(yàn)結(jié)果表明提出方法有較好的效果,相比基于數(shù)據(jù)對(duì)的專家排序方法NDCG@1值提升14.2%,基于列表的融合多特征方法能夠提高專家排序的效果。 (3)提出了基于關(guān)聯(lián)特征的專家列表學(xué)習(xí)排序方法。該方法首先構(gòu)建基于專家證據(jù)文檔的相關(guān)性模型、構(gòu)建基于專家關(guān)系網(wǎng)的相關(guān)性模型、構(gòu)建基于專家元數(shù)據(jù)的相關(guān)性模型,在獲得以上三個(gè)基于關(guān)聯(lián)特征的相關(guān)性模型基礎(chǔ)上,我們提出Expert-ListNet算法,然后訓(xùn)練得到基于關(guān)聯(lián)特征的專家列表學(xué)習(xí)排序模型。通過實(shí)驗(yàn)證明了提出方法的有效性和優(yōu)越性。 (4)利用上述研究成果,設(shè)計(jì)實(shí)現(xiàn)了融合多特征的專家列表排序?qū)W習(xí)原型系統(tǒng)。
[Abstract]:The traditional search engine can retrieve the relevant pages of recall query by keyword combination, but it must select the information related to the query subject manually. Expert retrieval is a hot field in the research of vertical information retrieval at present. It is a more accurate information retrieval method based on the characteristics of experts, and it can provide various forms of topic related queries. It is the most effective method to obtain expert information by directly returning the list of experts or the home page which is most relevant to the query topic. The expert sorting model is the core of expert search, and the effect of expert sorting determines the performance of the whole expert retrieval system. Therefore, the construction of efficient expert sorting model becomes the key. In this paper, the method of expert sorting is discussed, and how to combine the characteristic information of expert evidence document, expert relation and expert metadata to construct the expert sort model based on list is discussed in order to improve the result of expert sort. The main results are as follows: (1) the factors influencing the expert ranking are analyzed, and the three kinds of characteristics used in the expert ranking are defined. Aiming at the task of expert sorting, this paper studies the correlation between query and expert page and evidence document, analyzes the influence of expert evidence document, expert relation network, expert metadata and other factors on expert retrieval sorting, extracting similarity feature, BM25 score, etc. Expert page content feature, expert association relationship feature. The subsequent experiments show that the integration of the above features can effectively improve the effect of expert ranking. (2) an expert sorting method for multi-feature fusion based on ListNet is proposed. This method firstly analyzes the characteristics of the experts' pages, selects the correlation features of query and expert candidate pages, and then integrates the features into the ListNet sorting model, including the content of the expert pages and the associated features between the expert pages. Through gradient descent method to learn the parameters, the expert ranking model based on list fusion and multiple features is constructed. Finally, the comparison experiment of expert ranking is carried out by using the trained model. The experimental results show that the proposed method has good results. Compared with the expert sorting method based on data pair, the NDCG@1 value is increased by 14.2. the multi-feature method based on list can improve the effect of expert sorting. (3) an expert list learning ranking method based on association feature is proposed. Firstly, this method constructs the correlation model based on expert evidence document, the correlation model based on expert relation network, and the correlation model based on expert metadata. We propose Expert-ListNet algorithm, and then train to get the ranking model of expert list learning based on association feature. The effectiveness and superiority of the proposed method are proved by experiments. (4) based on the above research results, an expert list ranking learning prototype system is designed and implemented.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TP18

【參考文獻(xiàn)】

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本文編號(hào)：2366512