RETRACTED: Online review–driven Product Recommendation Ranking Considering Interactions Among Features and Consumers’ Psychology

Abstract

In the digital economy, online reviews serve as valuable and real-time feedback for consumers, making them a crucial source of information for product selection. We propose a product recommendation ranking method based on sentiment analysis of online reviews to address information overload and support informed purchase decisions. The proposed model considers the interdependencies among product features and fully simulates the psychological state of consumers before and after purchasing products. First, online reviews of products are mined and preprocessed. Natural language processing techniques are employed to extract product features and perform aspect-based sentiment analysis. The results are transformed into an evaluation matrix of the probabilistic linguistic term sets (PLTSs). Then, we propose a 2-additive fuzzy measures recognition model based on multi-dimensional feature attention information to effectively identify redundant, independent, and complementary interactions among product features. Subsequently, the fuzzy weight and the proposed possibility comprehensive discrepancy measures are applied to the interactive multi-criteria decision-making based on CPT-TODIM (Cumulative Prospect Theory TODIM) method to rank the recommended priorities of alternative products. The feasibility of the proposed method is validated through a case study of smart sweeping robot products from Jingdong Mall, followed by the investigation of the impact of various psychological behaviours that influence purchase decisions on the recommendation ranking based on stochastic multi-criteria acceptability analysis (SMAA). Finally, a comparative analysis is conducted to demonstrate the advantages and effectiveness of the proposed method.