A Hybrid Approach for Recommendation System based on Web Mining

Abstract

The significant issue of many on-line sites is the introduction of numerous decisions to the customer at once; this for the most part brings about strenuous and tedious in finding the correct item or data on the site. In the traditional methodologies, KNN based classification strategies were utilized which depended on suggestion handle. These have some real issue if the information differs. The arrangement approaches that were utilized as a part of customary work are fit just if the data variation is inside the cluster that they have. However, in the event that the data goes out of bound it is hard to perform classification. In this way, there is a need to include a classifier approach that can work in such conditions. For this, a hybrid approach comprising of Multi-Layer ANN and k-NN is proposed in order to take proper choices if there should be an occurrence of data variation. The proposed idea introduces an intelligent approach which captures the clients going out of bound and adds them into the cluster, so that they can be recommended to the user and no client is skipped.