Recommender system architecture for adaptive green marketing

Green marketing has become an important method for companies to remain profitable and competitive as the public and governments are more concerned about environmental issues. However, most online shopping environments do not consider product greenness in their recommender systems or other shopping tools. This paper aims to propose the use of recommender systems to aid the green shopping process and to promote green consumerism basing upon the benefits of recommender systems and a compliance technique called foot-in-the-door (FITD). In this study, the architecture of a recommender system for green consumer electronics is proposed. Customers' decision making process is modeled with an adaptive fuzzy inference system in which the input variables are the degrees of price, feature, and greenness and output variables are the estimated rating data. The architecture has three types of recommendation: information filtering, candidate expansion, and crowd recommendation. Ad hoc customization can be applied to tune the recommendation results. The findings are reported in two parts. The first part describes the potentials of using recommender systems in green marketing and the promotion of green consumerism; the second part describes the proposed recommender system architecture using green consumer electronics as the context. Discussion of the proposed architecture and comparison with other systems are also included in this part. The proposed architecture provides a capable platform for personalized green marketing by offering customers shopping advices tailored to their preferences and for the promotion of green consumerism. (C) 2011 Elsevier Ltd. All rights reserved.