The cognitive comparison enhanced hierarchical clustering

Abstract

The growth of online shopping is rapidly changing the buying behaviour of consumers. Today, there are challenges facing buyers in the selection of a preferred item from the numerous choices available in the market. To improve the consumer online shopping experience, recommender systems have been developed to reduce the information overload. In this paper, a cognitive comparison-enhanced hierarchical clustering (CCEHC) system is proposed to provide personalised product recommendations based on user preferences. A novel rating method, cognitive comparison rating (CCR), is applied to weigh the product attributes and measure the categorical scales of attributes according to expert knowledge and user preferences. Hierarchical clustering is used to cluster the products into different preference categories. The CCEHC model can be used to rank and cluster product data with the input of user preferences and produce reliable customised recommendations for the users. To demonstrate the advantages of the proposed model, the CCR method is compared with the rating approach of the analytic hierarchy process. Two recommendation cases are demonstrated in this paper with two datasets, one collected by this research for laptop recommendation and the other an open dataset for workstation recommendation. The simulation results demonstrate that the proposed system is feasible for providing personalised recommendations. The significance of this research is the provision of a recommendation solution that does not depend on historical purchase records; rather, one wherein the users’ rating preferences and expert knowledge, both of which are measured by CCR, is considered. The proposed CCEHC model could be further applied to other types of similar recommendation cases such as music, books, and movies.