An effective recommendation based on user behaviour: a hybrid of sequential pattern of user and attributes of product

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.