日志挖掘論文網(wǎng)絡(luò)日志挖掘和協(xié)同過濾算法的研究

1、日志挖掘論文：網(wǎng)絡(luò)日志挖掘和協(xié)同過濾算法的研究【中文摘要】隨著互聯(lián)網(wǎng)技術(shù)出現(xiàn)至今不過短短幾十年的時(shí)間,但是正是在這并不算太長的時(shí)間里經(jīng)歷的不斷發(fā)展,使得互聯(lián)網(wǎng)技術(shù)對每個(gè)人的生活都產(chǎn)生了極其深遠(yuǎn)的影響。相對于傳統(tǒng)意義上的網(wǎng)站而言,在二十世紀(jì)末二十一世紀(jì)初所興起的Web 2.0技術(shù)不僅帶來了最新的技術(shù),也致力于將提升用戶使用體驗(yàn)作為目標(biāo)。在這樣的動(dòng)力之下,也帶動(dòng)了各種提升用戶的網(wǎng)站使用體驗(yàn)和優(yōu)化網(wǎng)站結(jié)構(gòu)以及個(gè)性化用戶訪問等等方面的研究。作為實(shí)現(xiàn)對北京語言大學(xué)Web服務(wù)器日志的挖掘來優(yōu)化該網(wǎng)站的設(shè)計(jì),構(gòu)建以用戶為中心的網(wǎng)站,為用戶提供個(gè)性化的信息服務(wù)目標(biāo)的第一步,本文在充分考慮了北京語言大學(xué)Web服

2、務(wù)器日志實(shí)際情況下,對日志挖掘的工具做了相對應(yīng)優(yōu)化和改進(jìn)工作。本文還詳細(xì)介紹了日志挖掘預(yù)處理中的各個(gè)步驟,分析了其中牽涉到的各種預(yù)處理算法,并且實(shí)現(xiàn)了一個(gè)完整的日志挖掘工具。為將來更細(xì)致更詳盡的日志挖掘工作打下了一個(gè)良好的基礎(chǔ)。此外,本文還詳盡分析了兩種傳統(tǒng)的協(xié)同過濾算法,對比了基于用戶相似度的協(xié)同過濾算法和基于項(xiàng)目相似度的協(xié)同過濾算法在不同相似度公式之下的表現(xiàn),并且根據(jù)這兩種算法各自的優(yōu)缺點(diǎn),提出了一種新的基于混合相似度的協(xié)同過濾算法。分析了這種新的算法在理論上對兩種傳統(tǒng)算法的優(yōu)勢所在。本文也實(shí)現(xiàn)了以上各種算法,并使用MovieLens的數(shù)據(jù)集對各種算法進(jìn)行了測試。結(jié)果證明,本文所提出來的新

3、的基于混合相似度的協(xié)同過濾算法不僅吸收了兩種傳統(tǒng)算法的優(yōu)點(diǎn),同時(shí)也在一定程度上對兩種傳統(tǒng)算法的缺點(diǎn)有改進(jìn),在MAE以及召回率等度量指標(biāo)上都一定程度的提升?！居⑽恼縄t was only couple of decades since the emergence of Internet, but as the quickly growing of Internet and developing of techniques, Internet has impacted everyones everyday life enormously. Comparing the traditional w

4、ebsite which only offer the users text and multimedia, Web 2.0 developing during late 20th century and 21st century not only brings new technology but only on the purpose of increasing users experience on the website. Under such an endeavor, researches among increasing users experiences, optimizing

5、the structure of website and personalizing users visiting become more and more important.In order to optimize Beijing Language and Culture Universitys web structure, to construct a website suitable for users and to personalize users information services by mining the logs of web servers, this articl

6、e serves as the first step. After thoroughly considering the current situation of Beijing Language and Culture Universitys web log, this article does corresponding optimization and improvement in doing web mining. Also this article depicts the details of every single step of pre-processing of web lo

7、g mining, analysis different pre-processing algorithm involving it. This article also implements a completed web mining tools, as a solid foundation for future web log mining procedures.Besides that, this article also analyses two traditional collaborative filtering algorithms, which are user-based

8、collaborative filtering algorithm and item-based collaborative filtering algorithm. By comparing the advantages and disadvantages of these two algorithms with different way to calculate similarity, this article introduces a new method called mixed-based collaborative filtering algorithm, and depicts

9、 the advantages of the new method to the traditional two. After implementation and tested with the data of MovieLens, the result proves that this new mixed-based collaborative filtering algorithm not only has the advantages of the traditional two methods but also overtake some disadvantages of them. The results also show that on different measurements like MAE and ratio of recal