基于在线评分的新能源汽车推荐排序方法

Online Rating-Based Recommendation and Ranking Method for New Energy Vehicles

摘要: 针对当前新能源汽车推荐方法未充分考虑消费者购物心理、排序结果公平性不足的问题，本文融合电商平台的评分数据，引入前景理论计算消费者的心理损益值，代入博弈交叉效率排序模型，为平台提出一套新能源汽车推荐排序方法。选取汽车之家14款新能源汽车作为研究样本验证模型有效性，结果表明：本文方法相较于传统TOPSIS和均分排序结果，能够同时兼顾客观产品性能与消费者主观心理损益偏好，可为新能源汽车消费决策、平台高效推荐提供量化参考。

Abstract: To address the limitations of existing new energy vehicle (NEV) recommendation methods, including insufficient consideration of consumers’ purchasing psychology and inadequate fairness of ranking results, this paper integrates rating data from e-commerce platforms, adopts Prospect Theory to calculate consumers’ psychological gain and loss values, and incorporates the calculation results into the Game Cross Efficiency Ranking Model, to propose a systematic NEV recommendation and ranking method for e-commerce platforms. 14 NEV models from Autohome are selected as research samples to verify the effectiveness of the proposed model. The results show that compared with the traditional TOPSIS method and equal-weighted average ranking method, the method proposed in this paper can simultaneously take both objective product performance and consumers’ subjective psychological gain and loss preferences into account, which can provide quantitative references for NEV consumption decision-making and efficient recommendation operation of e-commerce platforms.