Document Type : Research Paper

Authors

• Hadis Shafaei ¹
• Rouzbeh Razavi ²

¹ Department of Computer Engineering, Tehran Science and Research Branch, Islamic Azad University, Iran

² Department of Management and Information Systems, Kent State University, New York, United States

Abstract

Web recommender systems provide the most appropriate recommendations by analyzing user’s navigation behavior. This recommender system can be considered in different cases, such as e-commerce, search engines, etc. The aim of the proposed approach in this research is to create users’ profiles and find their common navigation patterns implicitly. The web log file is utilized to analyze browsing history and discover users' navigation models. This analysis is called web usage mining. This research focused on the K-means algorithm as a cluster, and the neural network as a classification algorithm, along with the recommended Markov model. The innovation of this research is to consider a threshold for the proposed Markov model. The main goal of this research is to create a recommender system based on the Markov model and neural network that provides an acceptable suggestion with high accuracy and precision.

Keywords

• Web usage mining
• k-means algorithm
• neural network
• Markov model
• Recommender systems