LSGDM Two Stage Consensus Reaching Process for Autocratic Decision Making using Group Recommendations
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Abstract
The decision making is a general and significant action in day-to-day life. In some cases, experts cannot express their preferences using precise value due to inherent unreliability. The utilization of linguistic labels creates expert judgement more informative and consistent for decision making. The group recommendation is considered as a significant factors of e-commerce domain due to their direct impact on profit. The personalized experiments improve the engagement and the count of purchases of the customer when the recommended products are matched to the current interest.In this paper, the Large-Scale Group Decision Making (LSGDM) two stage consensus reaching process is proposed by using three various Amazon real world dataset.This proposed method permits an autocratic decision maker to utilize a different group recommendation for a sequence of decisions at highest level of consensus. The performance of the model is estimated by applying parameters like Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Precision and Recall. The obtained result shows that proposed methodology provides better result while comparing various other methods.
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