Recommendation system with Automated Web Usage data mining using K-Nearest Neighbor(KNN) classification
Main Article Content
Abstract
The major problem of many on-line web sites is the presentation of many choices to the various clients at a time. This usually results into time consumingtask in finding outthe right product or information on the site. The user’s current interest depends upon the navigational behavior which helps the organizations to guideusers in their browsing activities and obtain some relevant information in a short span of time. Since, the resulting patterns which are obtained through data mining techniques did not perform well in the prediction of future browsing patterns because of the low matching rate of resulting rules and of user’s browsing behavior. This paper focuses on the study of the automatic web usage data mining and recommendation system which is based on current user behavior through his/her click stream data. The K-Nearest-Neighbor (KNN) classification method has been trained to be used in real-time and on-line to identify clients and visitors click stream data, matching it to a particular user group and recommends a tailored browsing option that meet the needs of the specific user at particular time.
Downloads
Download data is not yet available.
Article Details
Section
Articles
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.