Intelligent Web Information Retrieval based on User Navigational Patterns

Abstract

The foremost mission of any information retrieval algorithm is the efficient extraction of user interests. The rapid growth of web data, intense competition and user's option to choose from several alternatives increases this issue. In this context Web usage mining can provide valuable contributions in terms of ideas and methods, as it fissures useful knowledge from the pattern of user interactions with the Web. The user interest can be identified by analyzing the access pattern of user browse, the web pages they save, collect, or print. These valuable information's are available in server logs, which can be exploited to satisfy user needs by optimizing the document-retrieval task. This article is a review conducted in the field of web usage mining and its latest works for supporting the research on efficient information retrieval based on user access pattern. This survey analyzes 25 released information retrieval models to find out the major mining techniques applied in them and also to analyze the effect of diverse parameters like feedbacks, time, content, frequency etc in information retrieval. The goal of this survey is to find the best composition of features to be included in an efficient information retrieval model. Using those features a new retrieval model is then proposed. General TermsInformation Retrieval, Web Usage Mining.

Improvements of Data Management Through Fuzzy Logic

“…Their method is specifically used to provide useful information for FAQ queries. Prasanth and Hemalatha [77], they have clustered the users based on their browsing behavior so that users of different groups can get much higher accuracy for prediction to finds the information that he/she really wants while searching on the web. Their model is trained based on generalize clickstreams.…”