An online recommendation system based on web usage mining and Semantic Web using LCS Algorithm

Abstract

E commerce has changed the entire look of the world's trading business. Nowadays more and more people are willing to do B2B transactions over the internet. Semantic Web Mining aims at combining the two fast-developing research areas. Web users exhibit a variety of navigational interests through clicking a sequence of web pages. WUM is used for mining the user logs for understanding user interest and generating interesting patterns. Online recommendation and prediction is one of the web usage mining applications. The semantic information of the Web page contents is generally not included in Web. The idea is to improve the results of Recommender system and to overcome the new item problem by exploiting the new semantic structures in the Web. In this paper we present architecture for integrating semantic information about the products with web log data and generate a list of recommended products by using LCS Algorithm. The implementation shows good performance in terms of precision, recall and F1 metrics.