Multi-Source Data-Driven Personalized Recommendation and Decision-Making for Automobile Products Based on Basic Uncertain Information Order Weighted Average Operator

The extensive electronic word-of-mouth (eWOM) data generated by consumers encapsulates authentic product experience information. By leveraging advanced data analysis technologies, enterprises can extract sustainable consumer behavior preference knowledge, thereby supporting the optimization of their marketing and management strategies. However, existing data-driven product ranking processes predominantly focus on single-source eWOM data and rarely mine product insights from a multi-source perspective. Moreover, the quality of eWOM data cannot be overlooked. Consequently, this study uses automobile products as a case example and integrates rating eWOM data, complaint eWOM data, and safety test data to construct a multi-source data-driven personalized product ranking recommendation algorithm. Specifically, an evaluation index system is established for each of the three data types. To model information quality, these data are transformed into basic uncertain information (BUI), which incorporates scoring information and credibility metrics. The XLNet model is employed to convert complaint text data into scoring data, and three targeted credibility evaluation models are developed to assess the reliability of the three data types. Subsequently, BUI is aggregated using the BUI ordered weighted average (BUIOWA) aggregation operator. Based on this, a personalized product ranking method aligned with user preferences is proposed, offering consumers recommendation results that match their preferences. Finally, using automobile products as an illustrative example, this study elucidates the multi-source data-driven personalized product recommendation process and provides managerial implications for enterprises.