本文選題：元搜索引擎 + 用戶興趣群集模型�。� 參考：《安徽工業(yè)大學(xué)》2012年碩士論文

【摘要】：隨著web技術(shù)的發(fā)展，網(wǎng)絡(luò)資源呈現(xiàn)爆炸式的增長(zhǎng)，搜索引擎已成為人們?nèi)粘Ｉ钪胁檎屹Y源的必備工具。而現(xiàn)有的搜索引擎存在無(wú)法返回用戶所需的全部信息以及返回的信息大部分與用戶的興趣無(wú)關(guān)等主要問(wèn)題。元搜索引擎的出現(xiàn)解決了獨(dú)立搜索引擎覆蓋率不足和查準(zhǔn)率不高的問(wèn)題。然而現(xiàn)有元搜索引擎建模技術(shù)的研究，往往以單個(gè)用戶為基點(diǎn)建立用戶興趣模型，而忽略了用戶共同的興趣；注重用戶查詢興趣模型，而忽略了用戶對(duì)成員搜索引擎的偏好、用戶收藏和用戶友好等與社會(huì)化搜索相關(guān)的用戶興趣信息。本文從用戶共同興趣出發(fā)，建立了用戶興趣群集模型，設(shè)計(jì)和實(shí)現(xiàn)了基于用戶興趣群集模型的元搜索引擎GMS（Group Meta Search）。 本文的主要研究工作如下： 1、分析了用戶搜索的行為方式和特點(diǎn)，提出了基于用戶搜索行為的興趣度計(jì)算方法。 2、提取多個(gè)用戶興趣的主要特征，分析出多個(gè)用戶搜索的興趣類；采用Beeferman聚類算法形成不同的用戶群，，并在本體模型的基礎(chǔ)上建立用戶興趣群集模型。 3、提出基于用戶興趣群集模型的相關(guān)推薦算法。通過(guò)用戶對(duì)成員搜索引擎的偏好調(diào)整各搜索引擎在元搜索引擎中的權(quán)重；結(jié)合了成員搜索引擎的推薦結(jié)果和用戶興趣群集模型推薦結(jié)果兩方面改進(jìn)了元搜索引擎的排序算法。 4、設(shè)計(jì)和實(shí)現(xiàn)了基于用戶興趣群集模型的元搜索引擎GMS（Group Meta Search），并用于推薦算法的仿真。 最后，論文分別采用不同的搜索主題，對(duì)GMS元搜索引擎進(jìn)行了性能測(cè)試。實(shí)驗(yàn)結(jié)果表明，基于用戶興趣群集模型的元搜索引擎GMS能更好地反映用戶共同的興趣特征，在個(gè)性化推薦服務(wù)方面也有更好的表現(xiàn)。結(jié)合用戶對(duì)搜索引擎的需求趨勢(shì)，還指明了下一步研究的方向。
[Abstract]:With the development of web technology and the explosive growth of network resources, search engine has become a necessary tool to find resources in people's daily life. However, the existing search engines have the main problems such as not returning all the information needed by the user, and most of the information returned is irrelevant to the interest of the user, and so on. The emergence of meta-search engine solves the problem of insufficient coverage and low precision of independent search engine. However, the existing research of meta-search engine modeling technology often builds user interest model based on a single user, but neglects the common interest of users, pays attention to user query interest model, and neglects user preference to member search engine. User collection and user-friendly and other social search related to user interest information. Based on the common interests of users, this paper establishes a cluster model of user interest, and designs and implements a meta search engine GMS (Group Meta search) based on user interest cluster model. The main research work of this paper is as follows: 1. The behavior and characteristics of user search are analyzed, and the method of calculating interest degree based on user search behavior is proposed. 2. The interest classes of multiple users are analyzed, and different user groups are formed by Beeferman clustering algorithm, and the user interest cluster model is established on the basis of ontology model. 3. A related recommendation algorithm based on user interest cluster model is proposed. Adjust the weight of each search engine in the meta search engine through the user's preference to the member search engine; Combining the recommended results of member search engines and the recommended results of user interest cluster model, the sorting algorithm of meta search engine is improved. 4. A meta-search index based on user interest cluster model is designed and implemented. GMS (Group Meta search), and used in the simulation of recommendation algorithm. Finally, the performance of GMS meta-search engine is tested with different search topics. The experimental results show that the meta-search engine GMS based on user interest cluster model can better reflect the common interest characteristics of users and has better performance in personalized recommendation service. Combined with the trend of users' demand for search engine, it also points out the direction of further research.
【學(xué)位授予單位】：安徽工業(yè)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

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本文編號(hào)：2052007