日志挖掘论文网络日志挖掘和协同过滤算法的研究

1、日志挖掘论文：网络日志挖掘和协同过滤算法的研究【中文摘要】随着互联网技术出现至今不过短短几十年的时间,但是正是在这并不算太长的时间里经历的不断发展,使得互联网技术对每个人的生活都产生了极其深远的影响。相对于传统意义上的网站而言,在二十世纪末二十一世纪初所兴起的Web 2.0技术不仅带来了最新的技术,也致力于将提升用户使用体验作为目标。在这样的动力之下,也带动了各种提升用户的网站使用体验和优化网站结构以及个性化用户访问等等方面的研究。作为实现对北京语言大学Web服务器日志的挖掘来优化该网站的设计,构建以用户为中心的网站,为用户提供个性化的信息服务目标的第一步,本文在充分考虑了北京语言大学Web服

2、务器日志实际情况下,对日志挖掘的工具做了相对应优化和改进工作。本文还详细介绍了日志挖掘预处理中的各个步骤,分析了其中牵涉到的各种预处理算法,并且实现了一个完整的日志挖掘工具。为将来更细致更详尽的日志挖掘工作打下了一个良好的基础。此外,本文还详尽分析了两种传统的协同过滤算法,对比了基于用户相似度的协同过滤算法和基于项目相似度的协同过滤算法在不同相似度公式之下的表现,并且根据这两种算法各自的优缺点,提出了一种新的基于混合相似度的协同过滤算法。分析了这种新的算法在理论上对两种传统算法的优势所在。本文也实现了以上各种算法,并使用MovieLens的数据集对各种算法进行了测试。结果证明,本文所提出来的新

3、的基于混合相似度的协同过滤算法不仅吸收了两种传统算法的优点,同时也在一定程度上对两种传统算法的缺点有改进,在MAE以及召回率等度量指标上都一定程度的提升。【英文摘要】It 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