480 Int. J. Business Information Systems, Vol. 14, No. 4, 2013 Copyright © 2013 Inderscience Enterprises Ltd. An effective recommendation based on user behaviour: a hybrid of sequential pattern of user and attributes of product Mojtaba Salehi Industrial Engineering Department, K.N. Toosi University of Technology, 1999143344, Tehran, Iran Fax: +982188674858 E-mail: m_salehi61@yahoo.com E-mail: m.salehi@kntu.ac.ir Abstract: Recommender system is a promising technology for companies to present personalised offers to their customers. But this technology suffers from sparsity problem. In addition, most researches are based on explicit rating. But most users do not spend time for rating of products. Therefore, this research proposes an effective recommendation based on user behaviour. Since users express their opinions implicitly based on some specific attributes of products, we introduce a preference matrix that can collect user preferences based on attributes of products. In addition, since there are some sequential patterns in purchasing of products, we use weighted association rules to discover these patterns to improve the quality of recommendation. The method outperforms current algorithms and alleviates sparsity problem. Main contribution is implementation of a user behaviour-based recommendation method that discovers interest of users based on implicit rating of product attributes. In addition, this approach uses sequential pattern of purchasing to improve the quality of recommendation. Keywords: personalisation; content-based filtering; CBF; personalised recommender; sparsity; product attributes; attribute-based; sequential-based; association rules; information overload; collaborative filtering. Reference to this paper should be made as follows: Salehi, M. (2013) ‘An effective recommendation based on user behaviour: a hybrid of sequential pattern of user and attributes of product’, Int. J. Business Information Systems, Vol. 14, No. 4, pp.480–496. Biographical notes: Mojtaba Salehi received his BSc degree from Shahid Bahonar University in 2004 and MSc degree from Tehran University in 2006 and his PhD degree from Tarbiat Modares University in 2013. During 2012, he was a Researcher in TU/e, Mathematics and Computer Science Department. He is currently working as an Assistant Professor of K.N. Toosi University of Technology (KNUT). His research areas of interest include soft computing, data mining, combinatorial optimisation, recommender systems and applied multivariate analysis.