本文選題：數(shù)據(jù)挖掘 + 個性化推薦　； 參考：《電子科技大學》2014年碩士論文

【摘要】：隨著Internet的快速發(fā)展,網(wǎng)絡已經(jīng)成為人類生活的一部分,也成為人們獲取信息的一個重要途徑。由于網(wǎng)絡信息量的不斷增加,人們不得不花時間從海量的信息中搜尋去自身所需的相關信息。僅僅依靠人們的搜索時很難在短時間內(nèi)找到自己的目標的。雖然搜索引擎可以幫助人們解決這個矛盾,但是由于搜索引擎缺乏智能以及個性化的推薦,因此不能從根本上解決這個難題。解決這一難題的思路就是本文提出的基于Web Agent的用戶訪問行為的個性化系統(tǒng)。本文重點以用戶瀏覽行為的角度分析了用戶對網(wǎng)頁的興趣度,對于當前被廣泛使用的用戶個性化模型只依賴頁面內(nèi)容而建立的方式,具有一定的啟發(fā)作用。本文在總結(jié)了其他學者們的研究成果基礎上,對Web Agent和Web數(shù)據(jù)挖掘進行了研究,并根據(jù)用戶行為的特性,提出了一種基于關聯(lián)規(guī)則的協(xié)同過濾算法,并對本基于Web Agent的個性化推薦系統(tǒng)進行了設計和原型實現(xiàn)。本文所做的工作如下:1.提出了獲取客戶端用戶行為數(shù)據(jù)的方法并對行為數(shù)據(jù)進行數(shù)據(jù)挖掘,將用戶的訪問行為和其有興趣的頁面結(jié)合起來構(gòu)建了一個基于Web Agent的用戶訪問行為的個性化推薦原型系統(tǒng)。2.使用Web數(shù)據(jù)挖掘?qū)τ脩舻男袨閿?shù)據(jù)進行了分析,由于服務器記錄的用戶數(shù)據(jù)具有冗余性,而且服務器和客戶端的日志文件也存在著差異,本文就此使用Web數(shù)據(jù)挖掘,就用戶行為數(shù)據(jù)進行了篩選和排除。本文設計的基于Web Agent的個性化推薦原型的數(shù)據(jù)處理包括三個流程:在線監(jiān)聽、離線學習和在線推薦。在線監(jiān)聽的作用是將用戶的各種行為數(shù)據(jù)、注冊信息等數(shù)據(jù)結(jié)合起來成為有價值的數(shù)據(jù)挖掘的數(shù)據(jù)來源,通過對這些數(shù)據(jù)進行挖掘和分析,就能發(fā)現(xiàn)其中的關聯(lián)規(guī)則。離線學習包括為內(nèi)容數(shù)據(jù)的預處理、結(jié)構(gòu)數(shù)據(jù)的預處理和使用數(shù)據(jù)的預處理。當預處理結(jié)束之后,再選用合適的工具對這些模式和數(shù)據(jù)進行分析,從海選的數(shù)據(jù)中選拔出有用的規(guī)則。在線推薦模塊式根據(jù)使用者的喜好向用戶其推薦他們可能會感興趣的產(chǎn)品,然后在根據(jù)用戶的反應,系統(tǒng)給予統(tǒng)一的評價,通過挖掘規(guī)則提取出的模式與當前用戶會話比較,生成用戶需要的個性化頁面。
[Abstract]:With the rapid development of Internet, the network has become a part of human life, and it has also become an important way for people to obtain information. Because of the increasing amount of information in the network, people have to spend time searching for the necessary information from a large amount of information. It is difficult to find a short time only by relying on people's search. Although the search engine can help people to solve this problem, the search engine can't solve this problem fundamentally because of lack of intelligence and personalized recommendation. The idea to solve this problem is the personalized system of user access behavior based on Web Agent. This paper focuses on the use of this paper. In the perspective of user browsing behavior, the user's interest in web pages is analyzed. It has a certain inspiring effect on the way that the widely used user personalization model relies on the content of the page only. This paper has studied the Web Agent and Web data mining on the basis of the research results of other scholars, and based on the users. A collaborative filtering algorithm based on association rules is proposed, and the personalized recommendation system based on Web Agent is designed and implemented. The work done in this paper is as follows: 1. the method of obtaining the client user behavior data and data mining for the behavior data are proposed, and the user's access behavior and the user's access behavior are presented. Interested pages are combined to build a personalized recommendation prototype system based on Web Agent user access behavior..2. uses Web data mining to analyze user behavior data. Because the server records are redundant, and there are differences between the server and the client's log files. This uses Web data mining to screen and exclude user behavior data. The data processing of personalized recommendation prototype based on Web Agent includes three processes: online monitoring, offline learning and online recommendation. The role of online monitoring is to combine the user's various behavioral data, registration information, and so on. Data mining is a source of data mining. By mining and analyzing these data, the association rules can be found. Off-line learning includes preprocessing for content data, preprocessing of structural data and preprocessing of data. Select the useful rules from the data selected by the sea. The online recommendation module recommends the products that they may be interested in according to the user's preference, and then gives a unified evaluation based on the response of the user, and compares the patterns extracted by the mining rules to the current user conversations to generate the personalized pages needed by the user. Noodles.

【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP391.3

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本文編號：1865493