New energy vehicle (NEV), an Eco-friendly innovation to alleviate the problems of energy scarcity and environmental pollution, is increasingly popular in many countries. Various new energy vehicles are provided with quantity of basic information (e.g., performance, quality, and price), which hinders potential users from effectively finding the most desired or interested new energy vehicles to satisfy their personalized requirements. This paper proposes a three-stage recommendation method for facilitating users to find the proper NEV considering users' preferences and social trust relationship. In the first stage, the users' preferences on evaluation criteria are determined by best-worst method (BWM) through hesitant fuzzy preference comparison vectors. In the second stage, the users' demographic similarity is obtained considering different formats of information, and then users' trust degrees are generated from the entire propagation paths using n dimensional path-ordering-induced order-weighted averaging (NP-IOWA) operator, thereby obtaining the trust-based similarity. In the third stage, the comprehensive user-rating matrix is constructed with the obtained weights, and then, it is combined with the trust-based similarity to recommend NEV based on collaborative filtering technique. A case study is given to illustrate the feasibility of the proposed method and the comparative analysis is conducted to show the advantages of the proposed method.
Keywords: Best-worst method; Hesitant fuzzy set; New energy vehicle (NEV); Purchase recommendation; Trust propagation; Users’ preferences.