Abstract

The recommender systems are new type of software tools designed to help users find their way through today's online shops. Due to the increasing number of e-commerce websites, it is necessary to render effective recommendation to the users. Here we present an overview of current recommendation systems and then our proposed system that employs WordNet dictionary in clustering and content based filtration to provide tailored and friendly suggestions to the user. The proposed system is a user-centric approach that analyses the navigation path of the user and clusters the keyword extracted from WordNet to recommend user articles.

References

• Christos Bouras, Vassilis Tsogkas. Clustering user preferences based on W-kmeans, 2011, Seventh International Conference IEEE.
• Zakaria Elberrichi, Abdelattif Rahmoun, and Mohamed Amine Bentaalah. Using Wordnet for Text Categorization, the International Arab Journal of Information Technology, Vol. 5, January 2008.
• Feng-Hsu Wang, Hsiu-Mei Shao. Effective Personalization Recommendation Based On Time Framed Navigation Clustering and Association Mining, Effective Systems with Application 2004, (365-377)
• Yanjun Li, Soon M. Chung. Parallel Bisecting K-means, Wright State University USA, 2007.
• Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom and John Riedl. GroupLens: An Open Architecture for Collaborative Filtering of Netnews, 1994.
• Epimenidis Voutsakis, Giannis Varelas and Paraskevi Raftopoulou. Semantic Similarity Methods in WordNet and their Application to Information Retrieval on the Web, November 5, 2005, Bremen, Germany.
• Robert Cooley, Bamshad Mobasher and Jaideep Srivastava. Data preparation for mining World Wide Web, Department of Computer Science and Engineering, University of Minnesota, USA, 2000.
• Christos Bouras, Vassilis Poulopoulos and Vassilis Tsogkas. PeRSSonal's core functionality evaluation: Enhancing text labelling through personalized summaries, Data Knowledge and Engineering 64 (2008) 330-345.
• Y. Fu, K. Sandhu and M. Shih. Clustering of web users based on access patterns, Computer Science Department, University of Missouri-Rolla, 2001.
• Michael J. Pazzani and Daniel Billsus "Content based Recommendation Systems" Palo Alto Laboratory CA 94304.
• K A Abdul Naseer, S D Madhu Kumar and M P Sebastian "Enhancing the k-means algorithm by using a O(n logn) heuristic method for finding better initial centroids" 2011 Second International Conference on Emerging Applications of Information Technology, Calicut India.
• Lior R. , Oded M. "Clustering Methods" Data Mining and Knowledge Discovery Handbook, Tel Aviv University.
• Mahlon Lovett, "http://wordnet. princeton. edu/" Office of Communications, Princeton University, August 2014.