Complex Fuzzy Sets Based Rank Data Processing and Generation

In the rapidly evolving e-commerce industry, displaying an optimal product list (precise and best-matched item set) is one of the most challenging and high-priority tasks. Ranking these item sets faces three main issues: rank reversal phenomena, the satisfiability of the transitivity property, and decomposed/composed rank problems. Additionally, on the client side, online sales are hindered by insufficient understanding of all products and limited knowledge, additionally, lack of evaluation time is also a major concern. To address these challenges, proposed system evaluates product features using a multicriteria group decision-making method based on improved Z-numbers (IVZ). We have extensively performed ranking using uncertain Z-numbers for recommender systems. These rankings are compared with the ranking provided by 84 different participants who kindly participated in the survey. It is noteworthy that the result of the experiments and ranking from the survey are similar. Moreover, we have also proposed multicriteria group decision-making method and exploited entropy, distance measure, and aggregation operator. We argue that the ordered list of products demonstrates robustness and resistance to the previously mentioned ranking issues.