Generalized Framework For Mining Web Content Outliers Research Paper

Generalized Framework For Mining Web Content Outliers - Research Paper Example With the advent of social networking web sites, micro blogging as well as an increase in usage of the net over mobile phones added an avalanche of data over the web, which varied in context and attracted all sorts of interest groups. This constant increase in information has created a great need of fast, relevant and mature content search methods that can sift through information, understand and generate search results in the shortest possible time. This requirement resulted in the development of revolutionary search engines like Bing, Google, etc. These search engines not only perform rapid searches over provided query from the user, however, they also maintain huge repositories of data classified into specific categories. There are several challenges that exist within such classification processes, and which can cause imprecise search results if they are not dealt with properly. Several methods have been proposed to match the most relevant web content with the user's query, however , this is a matter of debate that whether search engines should only generate the directly matched results or also provide some relevant results? Both methods have their own significance and a mixed kind of approach is in practice by different search engines. Google's page rank method got great popularity and has been modified by others for different purposes, for example the importance or relevance of a page is not measured for the top query results generation however several other kinds of processes are associated with this page ranking method. For instance, the computational advertisement industry has grown to a $20 billion industry this year and is expected to go further and big search engine groups are leaders in this industry so far. Advertisements are supposed to be placed on relevant pages so that they are viewed by the relevant i.e. targeted customers, which not only increases the revenue for their customers in terms of return of interest, however, moves towards personalize d or behavioral targeting. Out of several