Real Time Personalization Recommendation Based on Classification
Jin Li · Chinese Journal of Computers · 2002
To using user path characteristics to provide the personalization recommendation, this paper presents a new approach of real time personalization recommendation based on the classification approach in web usage mining. The sequence access transaction set is generated by the user access transaction grammar defined by this paper. The grammar is educed from the regular grammar and can get the sequence characteristic from the user access transaction and the result can facilitate the classification. The set can be used to train a classifier that can process the multiple classes. The k -nearest neighbor classification approach is chosen. Authors use recommendation engine to identify the active user, his current access sequence, and his next request. The sequence and the next request are input into the trained classifier to get the new possibly accessed web page. The recommendation web page address is annexed to the requested page and it is returned to the user by the engine. Each user is provided personalization web recommendation. Authors' approach does not require the profile information about the user and the recommendation process will not disturb the user. It can provide the real time personalization recommendation and the experiment manifest the approach is successful in speed and precision.