一種網(wǎng)頁推薦系統(tǒng)的設(shè)計與實現(xiàn)
[Abstract]:In the face of a large number of information and the diversity of information, people can obtain more and more information, but it is very difficult to get the information they need quickly, effectively and accurately. The emergence of search engine solves the user's need of retrieval, but it can not provide users with the information they need at the right time. When the user is uncertain about the keyword description of the required information, the search can not be carried out; Or if users do not want to search themselves, just want to obtain some information recommendations, they need a more proactive system than the search engine to meet these needs, intelligent recommendation service system was born. Recommendation technology has increasingly become an important research topic. In this paper, a web recommendation system is designed and implemented according to the information recommendation requirements of users. This system takes the collaborative filtering algorithm based on singular value decomposition as the key technology, uses the feature selection method in text classification to pre-process the web page information, and uses the feature selection method to process the collected web page and the user's potential interest page. At the same time, cluster analysis algorithm is introduced to solve the problem that the server load is heavy and the recommendation result may not be accurate. Finally, the recommended results list is optimized by user feedback and user usage time, so that the accuracy of recommendation results can be improved. So that the recommendation results more in line with the needs of users, more user-friendly. The web recommendation system designed in this paper is a hybrid recommendation system which combines user's interest and adopts collaborative filtering recommendation algorithm based on singular value decomposition (SVD). This recommendation system takes into account the pages that the user is browsing, the user's interest and the user's historical visit record, and combines the time period of the user's use of the recommendation system to provide the web page recommendation service for the users. The final implementation of the recommendation system can discover the interests and interests of users and recommend to the target users information or items in accordance with their interests, reference to user feedback and user use time to carry out intelligent recommendations. Firstly, this paper summarizes the research background, research status and application in practice of web recommendation system. On the basis of analyzing the requirement of web recommendation system, this paper puts forward the overall structure and outline design of web page recommendation system, and designs the module in detail and implements the coding. The key technologies and methods adopted are introduced and explained in detail. At the end of the paper, we test the web recommendation system, and point out the aspects that need to be further improved and perfected.
【學(xué)位授予單位】:北京郵電大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2013
【分類號】:TP391.3
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 朱敏;蘇博;;基于奇異值分解的協(xié)同過濾推薦算法研究[J];計算機(jī)安全;2010年07期
2 方俊;;電子商務(wù)系統(tǒng)商品推薦方法淺析[J];大眾科技;2010年08期
3 王燕紅;;基于聚類分析的新書推薦[J];計算機(jī)光盤軟件與應(yīng)用;2012年03期
4 董祥和;齊莉麗;董榮和;;優(yōu)化的協(xié)作過濾推薦算法[J];計算機(jī)工程與應(yīng)用;2009年08期
5 李燕;馮博琴;魯曉鋒;;Web日志挖掘中的數(shù)據(jù)預(yù)處理技術(shù)[J];計算機(jī)工程;2009年22期
6 吳兵;葉春明;;基于效用的個性化推薦方法[J];計算機(jī)工程;2012年04期
7 曾小波;魏祖寬;金在弘;;協(xié)同過濾系統(tǒng)的矩陣稀疏性問題的研究[J];計算機(jī)應(yīng)用;2010年04期
8 鮑雷;楊天奇;;基于用戶瀏覽行為的個性化網(wǎng)頁推薦[J];微計算機(jī)信息;2010年06期
9 余小高;;基于分布式數(shù)據(jù)挖掘的電子商務(wù)推薦系統(tǒng)[J];計算機(jī)系統(tǒng)應(yīng)用;2009年11期
10 彭飛;鄧浩江;劉磊;;加入用戶評分偏置的推薦系統(tǒng)排名模型[J];西安交通大學(xué)學(xué)報;2012年06期
相關(guān)碩士學(xué)位論文 前3條
1 陳默;基于神經(jīng)網(wǎng)絡(luò)的元搜索引擎[D];浙江大學(xué);2006年
2 程偉想;網(wǎng)格聚類算法的研究[D];華北電力大學(xué)(河北);2008年
3 李柯;基于用戶訪問矩陣的網(wǎng)頁推薦模型研究[D];江蘇大學(xué);2010年
,本文編號:2462414
本文鏈接:http://sikaile.net/kejilunwen/sousuoyinqinglunwen/2462414.html