Expert Systems with Applications 38(2011)9696-9703 Contents lists available at Science Direct Expert Systems with Applications ELSEVIER journalhomepagewww.elsevier.com/locate/eswa Recommender system architecture for adaptive green marketing Ying-Lien Lee.*, Fei-Hui Huang b Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung County 413, Taiwan Department of marketing and Distribution Management, Oriental Institute of Technology, Taipei County 220, Taiwan ARTICLE INFO A BSTRACT Green marketing has become an important method for companies to remain profitable and competitive Its are more concerned about environmental issues. however most online shopping environments do not consider product greenness in their recommender systems or other shop- Fuzzy inference system ping tools. This paper aims to propose the use of recommender systems to aid the green shopping proce 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 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 lectronics as the context. Discussion of the proposed architecture and comparison with other re also included in this part. The proposed architecture provides a capable platform for person- keting by offering customers shopping advices tailored to their preferences and for the erism e 2011 Elsevier Ltd. All rights reserved. 1 Introduction recommends blogs a rater might be interested in. The domain of recommender systems is not limited to the famous instances men- Recommender systems have become an important technology tioned above Recommender systems for news, web pages, jokes for electronic commerce on many fronts( Bose, 2009: Kauffman& academic articles, consumer electronics, restaurants, and a pleth Walden, 2001). It can filter for online shoppers the vast amount ora of other subject matters, have been researched and imple of information, saving the customers from the information over- mented(Adomavicius Tuzhilin, 2005: Iijima Ho, 2007) ad problem(Chen, Shang, Kao, 2009). It can be a decision aid However, to our knowledge few researches have dealt with rec for customers who are challenged when they are in the market ommender system of green product. for unfamiliar products. It can be a strategic marketing platform Green product is increasingly important in our global village n which online venders can personalize promotions and sales the general public is becoming more concerned of our i for each customer(Chen, 2008: Shih, Chiu, Hsu, lin, 2002). the planet. Driven by this trend, companies have been trying to de- ems have been vigorously researched and sign and manufacture greener products, and have been trying to developed in the fields of academia and business. Some notable promote their products and brand images by communicating their examples include apple Inc's Genius of iTunes that make music greenness to the customers via a variety of channels. Yet, eco- recommendations, University of Minnesotas MovieLens and labeling remains one of the fundamental ways to inform the cus- Netflix's Cinematch recommend movie titles, Amazon. coms tomers how green their products are and in what respect their ecommender system that generates recommendations of an products are green. Eco-labels, usually issued by third-party orga assortment of products, and Outbrain coms blog rating widget that nizations, are textual or graphical presentations of the environ- mental characteristics of a product, which can be found on the product itself, on the packaging, or in the manual. Examples of eco-labels include Green Seal, Energy Star, and WEEE (Waste Elec A*Corresponding author. Address: No. 168. Jifong E Rd, Wufong Township, trical and Electronic Equipment Directive). Studies have shown chung County 413, Taiwan. Tel: +886 4 23323000: fax: +886 4 2374232 E-mailaddressyinglienlee@gmail.com(y.-lLee). that public education campaign is one of the key determinants of 0957-4174 front matter o 2011 Elsevier Ltd. All rights reserved o:10.1016/eswa2011.01.164Recommender system architecture for adaptive green marketing Ying-Lien Lee a,⇑ , Fei-Hui Huang b aDepartment of Industrial Engineering and Management, Chaoyang University of Technology, Taichung County 413, Taiwan bDepartment of Marketing and Distribution Management, Oriental Institute of Technology, Taipei County 220, Taiwan article info Keywords: Green marketing Green consumerism Recommender system Fuzzy inference system abstract 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 shop￾ping 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 person￾alized green marketing by offering customers shopping advices tailored to their preferences and for the promotion of green consumerism. 2011 Elsevier Ltd. All rights reserved. 1. Introduction Recommender systems have become an important technology for electronic commerce on many fronts (Bose, 2009; Kauffman & Walden, 2001). It can filter for online shoppers the vast amount of information, saving the customers from the information over￾load problem (Chen, Shang, & Kao, 2009). It can be a decision aid for customers who are challenged when they are in the market for unfamiliar products. It can be a strategic marketing platform on which online venders can personalize promotions and sales for each customer (Chen, 2008; Shih, Chiu, Hsu, & Lin, 2002). Recommender systems have been vigorously researched and developed in the fields of academia and business. Some notable examples include Apple Inc.’s Genius of iTunes that make music recommendations, University of Minnesota’s MovieLens and Netflix’s Cinematch that recommend movie titles, Amazon.com’s recommender system that generates recommendations of an assortment of products, and Outbrain.com’s blog rating widget that recommends blogs a rater might be interested in. The domain of recommender systems is not limited to the famous instances men￾tioned above. Recommender systems for news, web pages, jokes, academic articles, consumer electronics, restaurants, and a pleth￾ora of other subject matters, have been researched and imple￾mented (Adomavicius & Tuzhilin, 2005; Iijima & Ho, 2007). However, to our knowledge, few researches have dealt with rec￾ommender system of green product. Green product is increasingly important in our global village as the general public is becoming more concerned of our impact on the planet. Driven by this trend, companies have been trying to de￾sign and manufacture greener products, and have been trying to promote their products and brand images by communicating their greenness to the customers via a variety of channels. Yet, eco￾labeling remains one of the fundamental ways to inform the cus￾tomers how green their products are and in what respect their products are green. Eco-labels, usually issued by third-party orga￾nizations, are textual or graphical presentations of the environ￾mental characteristics of a product, which can be found on the product itself, on the packaging, or in the manual. Examples of eco-labels include Green Seal, Energy Star, and WEEE (Waste Elec￾trical and Electronic Equipment Directive). Studies have shown that public education campaign is one of the key determinants of 0957-4174/$ - see front matter 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2011.01.164 ⇑ Corresponding author. Address: No. 168, Jifong E. Rd., Wufong Township, Taichung County 413, Taiwan. Tel.: +886 4 23323000; fax: +886 4 23742327. E-mail address: yinglienlee@gmail.com (Y.-L. Lee). Expert Systems with Applications 38 (2011) 9696–9703 Contents lists available at ScienceDirect Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa