本文選題：搜索引擎 + 個(gè)性化搜索�。� 參考：《江南大學(xué)》2012年碩士論文

【摘要】：隨著Web信息的急速增長(zhǎng),搜索引擎已成為用戶信息檢索的主要工具。但通用搜索引擎針對(duì)不同用戶的查詢請(qǐng)求都提供相同的檢索結(jié)果,體現(xiàn)不出用戶的個(gè)性化需求。同時(shí),信息檢索領(lǐng)域存在檢索詞簡(jiǎn)短和歧義的問(wèn)題,這在很大程度上影響了檢索的效率。因此,針對(duì)用戶輸入的查詢請(qǐng)求,如果搜索引擎系統(tǒng)能自動(dòng)提供一個(gè)相關(guān)的查詢列表,將有助于用戶進(jìn)行查詢修正,進(jìn)而檢索到用戶所需要的信息。本文從個(gè)性化搜索引擎的關(guān)鍵技術(shù)出發(fā),詳細(xì)討論了個(gè)性化查詢推薦技術(shù),對(duì)于用戶的查詢請(qǐng)求,提供查詢推薦;同時(shí)通過(guò)獲取不同用戶的查詢偏好,以達(dá)到個(gè)性化查詢推薦的目的。 為此,本文研究和探索了信息檢索和個(gè)性化搜索引擎的相關(guān)技術(shù),并進(jìn)行了仿真實(shí)驗(yàn),主要內(nèi)容包括以下幾個(gè)方面: 首先,提出了一種基于概念抽取的個(gè)性化聚類算法,通過(guò)獲取不同用戶的偏好,以達(dá)到提供個(gè)性化查詢推薦的目的。實(shí)驗(yàn)結(jié)果表明,該方法具有較高的準(zhǔn)確率,為用戶提供潛在感興趣的資源,顯著提高個(gè)性化檢索系統(tǒng)的效率。 其次,通過(guò)對(duì)用戶點(diǎn)擊的URL進(jìn)行分析,發(fā)現(xiàn)URL中出現(xiàn)的許多標(biāo)記都是有意義的,尤其是那些高質(zhì)量的網(wǎng)頁(yè)。因此,這些URL中的標(biāo)記可以作為該網(wǎng)頁(yè)的主要內(nèi)容或者它所表達(dá)主題的扼要說(shuō)明。本文對(duì)URL進(jìn)行分析處理,提出了一種基于TF-IQF模型和圖聚類的個(gè)性化查詢建議方法。 最后,對(duì)用戶查詢?cè)~進(jìn)行分析,用以下三種方法來(lái)表示用戶查詢?cè)~:(1)點(diǎn)擊的文檔;(2)相似查詢?cè)~;(3)反向查詢?cè)~。每種表示方法都對(duì)應(yīng)著一種語(yǔ)義相似度計(jì)算方法,通過(guò)設(shè)定不同方法以不同的權(quán)重,得到新的查詢?cè)~語(yǔ)義相似度計(jì)算方法,然后使用基于用戶偏好的用戶-查詢語(yǔ)義聚類方法對(duì)用戶查詢?cè)~進(jìn)行聚類,從而達(dá)到個(gè)性化查詢推薦的目的。
[Abstract]:With the rapid growth of Web information, search engine has become the main tool of user information retrieval. But the general search engine provides the same retrieval results for different users, which can not reflect the personalized needs of users. At the same time, there are some problems in the field of information retrieval, such as brevity and ambiguity, which greatly affect the efficiency of retrieval. Therefore, if the search engine system can automatically provide a related query list for the query request input by the user, it will be helpful for the user to correct the query and retrieve the information needed by the user. Starting from the key technology of personalized search engine, this paper discusses in detail the personalized query recommendation technology, which provides query recommendation for users' query requests, and obtains different users' query preferences. In order to achieve the purpose of personalized query recommendation, this paper studies and explores the relevant technologies of information retrieval and personalized search engine, and carries out simulation experiments. The main contents include the following aspects: first, This paper presents a personalized clustering algorithm based on concept extraction, which obtains the preferences of different users in order to provide personalized query recommendation. The experimental results show that the method has high accuracy, provides users with potentially interesting resources, and significantly improves the efficiency of the personalized retrieval system. Secondly, the URL clicked by the user is analyzed. Find that many of the tags that appear in URLs are meaningful, especially high-quality web pages. Therefore, tags in these URLs can be used as the main content of the page or as a brief description of the subject it expresses. In this paper, the URL is analyzed and processed, and a personalized query suggestion method based on TF-IQF model and graph clustering is proposed. The following three methods are used to express the user query word: 1) the click of the document / 2) the similar query word / 3) the reverse query word / _ _ _ Each representation method corresponds to a semantic similarity calculation method. By setting different methods with different weights, a new method for calculating semantic similarity of query words is obtained. Then the user-query semantic clustering method based on user preference is used to cluster the user query words to achieve the purpose of personalized query recommendation.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前8條

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2 王繼民,陳，

本文編號(hào)：1987880