Distance-based Similarity Measures of Hypersoft Sets under Uncertain Environment and Application in Customer Support Systems

Abstract

Intuitionistic fuzzy hypersoft sets (IFHSS) represent a novel conceptual framework poised to overcome the limitations associated with intuitionistic fuzzy soft sets (IFSS) concerning the representation of multi-argument domains for parameter approximation. This model offers enhanced flexibility and reliability by facilitating the categorization of parameters into pertinent parametric valued sets. This study investigates the application of the IFHSS theory in enhancing similarity measurement within ChatBot systems. Through experimentation and analysis, the research demonstrates the efficacy of IFHSS-based approaches in handling uncertainties inherent in natural language interactions. We introduce distance measures (DM) along with their corresponding similarity measures (SM). These SMs tailored for IFHSS play a significant role in assessing similarity and facilitating the comparison of various factors. This article aims to develop six SMs based on their DMs and their axiomatic properties, theorems, and illustrative examples. Furthermore, we employ these measures to address real-world problems, particularly in the domain of computer sciences. By leveraging various technical factors, our analysis aids in pinpointing the best ChatBot for the satisfaction of customers. The methodologies proposed in this study hold promise for future case studies involving complex features and multiple decision-makers. Moreover, the suggested approach can be seamlessly integrated with existing structures.