Power-average-operator-based hybrid multiattribute online product recommendation model for consumer decision-making
Tipo de publicación: International Journal
Año de publicación: 2021
Tipo: International Journal of Intelligent Systems
Palabras clave: multiattribute decision support, online product recommendation, power average operator, proportional hesitant fuzzy linguistic term set, risk attitude
Resumen: Abstract This study develops a power-average-operator-based hybrid multiattribute online product recommendation model that considers the consumer s risk attitude to rank categoric product options as a complement to existing recommender systems. Online production recommendation plays a key role in the development of e-commerce, and can greatly improve consumers shopping experiences. However, few online shopping sites provide interactive decision aids for consumers such that they can articulate their preferences towards multiple selection attributes with the purpose of mitigating choice difficulty and improving decision quality. Additionally, consumers risk attitudes to online shopping dramatically impact their product choices. In the model proposed in this paper, the risk attitude-based power average (RAPA) operator is used to integrate the risk attitude of the decision-maker into the information fusion process of multiple attribute decision-making. Subsequently, the risk attitude function, with several basic types, is introduced to quantify the risk attitude of the decision-maker for use in the RAPA operator. A proportional hesitant fuzzy 2-tuple linguistic term set (PHF2TLTS) is constructed by incorporating a binary of linguistic information aiming to comprehensively analyze the hybrid product information. With a focus on the information fusion process, the proportional hesitant 2-tuple linguistic RAPA operator and weighted proportional hesitant 2-tuple linguistic RAPA operator are introduced to aggregate a given set of PHF2TLTSs. The validity of the proposed model is demonstrated using an illustrative example, a comparison with existing approaches and detailed explanations of the performance differences.