The cognitive comparison enhanced hierarchical clustering

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