A Dynamic Intelligent Recommendation Method Based on the Analytical ER Rule for Evaluating Product Ideas in Large-Scale Group Decision-Making

Abstract

In large-scale group decision-making, participants with large differences in knowledge structures and educational backgrounds are unlikely to give an accurate evaluation of each criterion of product ideas. To solve this problem and to effectively extract and combine uncertainty in the evaluation information to ultimately obtain a ranking of product ideas, we propose a dynamic intelligent integration recommendation method for product ideas. First, we construct a new evaluation criteria system for product ideas that includes input criteria and output criteria. Second, we describe steps for static information extraction and information combination. We use the basic probability assignment function as an information extraction method to effectively capture and accurately reflect the authenticity of experts’ evaluation. For information combination, we employ the analytical evidence reasoning rule for both individual and group combination of evaluation information. On this basis, we can achieve real-time updating of ideas, the screening of effective ideas, and a dynamic intelligence recommendation method. We apply our method to an illustrative example to demonstrate our method’s practical use.