Web Mining and Social Networking: Techniques and ... - tud.ttu.ee

142 6 Web Usage Mining(2) Degree of User Interests The more times a user clicks one topic, the more interestedthe user is in it. The user’s clicked times can produce a complementary rank list of searchresults.(3) Google List Google applies its patented PageRank technology on the Google Directoryto rank the sites. To keep our rank aggregation from missing the high quality Web pagesin Google, the original rank list of Google Directory Search is considered as well.In [159] authors study the problem of combining sets of rank lists from different attributesof user preferences into a single rank list. Voting provides us with a traditional class of algorithmsto determine the aggregated rank list. The most common voting theory, named after itscreator, is known as Borda’s rule [38] which argues that the majority opinion is the truth, or atleast the closest that we can come to determining it [266]. However, the problem with Borda’srule is that it does not optimize any criterion. Footrule distances [74] is used to weigh edges ina bipartite graph and then find a minimum cost matching. This method was proved in [82] toapproximate the optimal ranking that approximately minimizes the number of disagreementswith the given inputs.In [159] experimental results on a real click-through data set demonstrated the effectivenessof their methods. They argued that some rank-based aggregation methods performedbetter than the Socre-Based method.SummaryIn this chapter, we have reviewed and summarized a number of Web usage mining algorithms.The reported algorithms are mainly related to two kinds of well studied data miningparadigms, namely clustering and latent semantic analysis. In first section, we have describedthe procedures of modeling user interest and capturing user clusters. In the following two sections,we have elaborated the combination of latent semantic analysis into Web usage mining.In section 6.4, we have reported the study of co-clustering of weblogs. Section 6.5 listed acouple of Web usage mining applications in real world. In this chapter, besides the technicalcoverage on algorithmic issues, some interesting and insightful experimental investigationshave also been presented.