Improving web search by categorization, clustering, and personalization

This research combines Web snippet1 categorization, clustering and personalization techniques to recommend relevant results to users. RIB - Recommender Intelligent Browser which categorizes Web snippets using socially constructed Web directory such as the Open Directory Project (ODP) is to bedeveloped. By comparing the similarities between the semantics of each ODP category represented by the category-documents and the Web snippets, the Web snippets are organized into a hierarchy. Meanwhile, the Web snippets are clustered to boost the quality of the categorization. Based on an automatically formed user profile which takes into consideration desktop computer informationand concept drift, the proposed search strategy recommends relevant search results to users. This research also intends to verify text categorization, clustering, and feature selection algorithms in the context where only Web snippets are available

IR issues for digital ecosystems users

The purpose of this research is to discuss some challenges of information retrieval, especially Web information retrieval, in digital ecosystems from a user?s perspective. As a dominant search tool, search engines usually return millions of search results in a long flat list in which many or even most of the results can be irrelevant. The long flat list conveys nothing about knowledge structure related to the retrieved results and personal search preferences and interests are not explored.Although some search engines try to cluster the Web results, the automatically formed titles and knowledge hierarchy is prone to mismatching the searcher?s human mental model. In digital ecosystems, while many different search tools are available, they are not integrated. To address these issues, a search framework which combines categorization, clustering, ontology, and personalization is proposed, and thus the quality of search results in digital ecosystems is expected to be boosted

Datamining for Web-Enabled Electronic Business Applications

Web-Enabled Electronic Business is generating massive amount of data on customer purchases, browsing patterns, usage times and preferences at an increasing rate. Data mining techniques can be applied to all the data being collected for obtaining useful information. This chapter attempts to present issues associated with data mining for web-enabled electronic-business

Using thematic ontologies for user- and group- based adaptive personalization in web searching

This paper presents Prospector, an adaptive meta-search layer, which performs personalized re-ordering of search results. Prospector combines elements from two approaches to adaptive search support: (a) collaborative web searching; and, (b) personalized searching using semantic metadata. The paper focuses on the way semantic metadata and the users’ search behavior are utilized for user- and group- modeling, as well as on how these models are used to re-rank results returned for individual queries. The paper also outlines past evaluation activities related to Prospector, and discusses potential applications of the approach for the adaptive retrieval of multimedia documents

Web Page Annotation Using Web Usage Mining and Domain Knowledge Ontology

Today’s world the growth of the WWW has increased tremendously, the user is totally relying on web for information. Search engine provides the result pages to the user but all are not relevant so the challenging task is extracting the pages from web and provide to the user. WUM is an approach to extract knowledge and use it to the different purposes. In this paper new semantic approach is proposed based on WUM and Domain Knowledge Ontology. Ontology database preparation, it is also challenging task in this project

A Survey on Web Usage Mining, Applications and Tools

World Wide Web is a vast collection of unstructured web documents like text, images, audio, video or Multimedia content.  As web is growing rapidly with millions of documents, mining the data from the web is a difficult task. To mine various patterns from the web is known as Web mining. Web mining is further classified as content mining, structure mining and web usage mining. Web usage mining is the data mining technique to mine the knowledge of usage of web data from World Wide Web. Web usage mining extracts useful information from various web logs i.e. users usage history. This is useful for better understanding and serve the people for better web applications. Web usage mining not only useful for the people who access the documents from the World Wide Web, but also it useful for many applications like e-commerce to do personalized marketing, e-services, the government agencies to classify threats and fight against terrorism, fraud detection, to identify criminal activities, the companies can establish better customer relationship and can improve their businesses by analyzing the people buying strategies etc. This paper is going to explain in detail about web usage mining and how it is helpful. Web Usage Mining has seen rapid increase towards research and people communities