Empirical analysis of decision recommendation models for various processes from a pragmatic perspective

Abstract

Decision recommendation models allow researchers and process designers to identify and implement high-efficiency processes in ambiguous situations. These models perform multipara metric analysis on the given process sets to recommend high-quality decisions that assist in improving process-based efficiency levels. A wide variety of models have been proposed by researchers for the implementation of such recommenders, and each of them varies in terms of their functional nuances, applicative advantages, internal operating characteristics, contextual limitations, and deployment-specific future scopes. Thus, it is difficult for researchers and process designers to identify optimal models for functionality-specific use. Therefore, they tend to validate multiple process models, which increase deployment time, cost and complexity levels. To overcome this ambiguity, a detailed survey of different decision process recommendation models is discussed in this text. Fuzzy logic, analytical hierarchical processing (AHP), the technique for order performance by similarity to ideal solution (TOPSIS), and their variants are highly useful for the recommendation of efficient decisions. Based on this survey, readers will be able to identify recently proposed decision recommendation models and functionality-specific models for their deployments. To further assist in the model selection process, this paper compares the reviewed models in terms of their computational complexity, recommendation efficiency, delay needed for recommendation, scalability and contextual accuracy. Based on this comparison, readers will be able to identify performance-specific models for their deployments. This paper also evaluates a novel decision recommendation rank metric (DRRM), which combines these parameters, to identify models that can optimally perform w.r.t. multiple process metrics. Referring to this parameter comparison, readers will be able to identify optimal recommendation models for enhancing the performance of their decision recommendations under real-time scenarios.