Abstract The major problem of many on-line web sites is the presentation of many choices to the client at a time; this usually results to strenuous and time consuming task in finding the right product or information on the site. In this work, we present a study of automatic web usage data mining and recommendation system based on current user behavior through his/her click stream data on the newly developed Really Simple Syndication (RSS) reader website, in order to provide relevant information to the individual without explicitly asking for it. The K-Nearest-Neighbor (KNN) classification method has been trained to be used on-line and in Real-Time to identify clients/visitors click stream data, matching it to a particular user group and recommend a tailored browsing option that meet the need of the specific user at a particular time. To achieve this, web users RSS address file was extracted, cleansed, formatted and grouped into meaningful session and data mart was developed. Our result shows that the K-Nearest Neighbor classifier is transparent, consistent, straightforward, simple
D.A. Adeniyi et al. REF in this paper the author had offered a study of automatic web usage data mining and recommendation mechanism on the basis of the present user behavior by his/her click stream data on the fresh generated Really Simple Syndication (RSS) reader website, to offer an appropriate knowledge to the user among explicitly asking no for it.
Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method
Applied Computing and Informatics
Computer Science