Y-L Lee, F-H Huang/ Expert Systems with Applications 38 (2011)9696-9703 DF385 Fig 8. The rules of the quasi-conventional wisdom FIS. score of price: DG and DF are derived from multiple attributes. The been bought by other customers in the same group. These queries first rationale of the simplified representation is due to the incon- need only one-time scan of the records of the database, which is clusiveness of researches about green consumer behavior. Strau- efficient on modern database management systems. ghan and Roberts(1999)compared the results of past researches The database of the customers' FIS rules has value for marketing udying the relationship between the characteristics of consumers and product design. Given the advances in science and technolog and ecologically conscious consumer behavior(ECCB). They sur- products could have been designed with outstanding features al veyed research that consider the factors of demographic character- still remain fairly green. However, the price tag would have held istics (such as age, gender, income, and education) and customers back. Consumer electronics companies can gain from psychographic characteristics(such as political orientation, altru- the FIs rules the insight of how much more customers are willing m, perceived consumer effectiveness, and environmental con- to pay for extra features and high greenness. cern), and found that most of the results were inconclusive There is little research of recommender system for green prod- equivocal, or mixed. The second rationale of the simplified repre- ucts. Li ao(2008)proposed a green product recommendation sys sentation is to reduce the amount of user intervention. The only tem in which the product database is queried against user user input the proposed architecture is asking for is product rating. designated weights of green criteria. EPEAT is used to represent Yet, the architecture may even risk losing the customers' patience, the greenness of products as well. Liao's system is effective in re not to mention asking customers to fill out questionnaires to gath- ommending green products: however, users are required to input the demographic or psychographic profiles. In addition, the sys- the weights of certain green indices In our system, on the other m would have to ask the customers to fill out the questionnaires hand, users' criteria of the three decision variables are implicitly again every once in a while to see if their profiles change, which is gathered via users'rating of products For users who do not want impractical. The third rationale is privacy issues. Asking customers the hassle, our system can use the quasi-conventional wisdom to reveal so much information might deter them from shopping at FIs to recommend product. Reducing the level of user intervention is important to the success of a recommender system. The pro- The use of normalization score is justified by the ability of the posed architecture is in line with other commercially available rec- user model to work across product category. Normalized scores al- ommender systems, such as Cinematch of Nextflix or Genius of low comparison among products of different categories Without iTunes, because in these systems, rating data is the minimal re- such measures, the recommender system would have to have a quired inpu user model for each product category To enhance the responsiveness of the recommender system, most calculations and fuzzification can be done off ation and fuzzification of the domain model (i.e. (DP, DF, DG) of products) can be done only when the product catalog is changed In this paper, we proposed using recom also be done offline. The only part that must be done online(or based upon thes g process and to promote green consumerism The training of the ANFIS and the clustering of customers'FIS can the real-time) is the recommendation generation. In the information compliance technique. a proposed recommender system architec filtering recommendation, products of a category pass through a ture in the context of green consumer electronics was then de- customers' FIs to generate recommendation; this process is fast ribed and discussed. Previous research on this type of thanks to the efficiency of fuzzy operations. In the candidate recommender system used weights of certain green indices as xpansion recommendation, products of a category for certain fuzzified words. While in the crowd recom the transaction database is queried for other products emendation. puts The architecture proposed in this paper accepts implicit and explicit criteria by modeling user preference and by considering ad hoc modification. The domain model is defined by the normalizedscore of price; DG and DF are derived from multiple attributes. The first rationale of the simplified representation is due to the incon￾clusiveness of researches about green consumer behavior. Strau￾ghan and Roberts (1999) compared the results of past researches studying the relationship between the characteristics of consumers and ecologically conscious consumer behavior (ECCB). They sur￾veyed research that consider the factors of demographic character￾istics (such as age, gender, income, and education) and psychographic characteristics (such as political orientation, altru￾ism, perceived consumer effectiveness, and environmental con￾cern), and found that most of the results were inconclusive, equivocal, or mixed. The second rationale of the simplified repre￾sentation is to reduce the amount of user intervention. The only user input the proposed architecture is asking for is product rating. Yet, the architecture may even risk losing the customers’ patience, not to mention asking customers to fill out questionnaires to gath￾er the demographic or psychographic profiles. In addition, the sys￾tem would have to ask the customers to fill out the questionnaires again every once in a while to see if their profiles change, which is impractical. The third rationale is privacy issues. Asking customers to reveal so much information might deter them from shopping at a store. The use of normalization score is justified by the ability of the user model to work across product category. Normalized scores al￾low comparison among products of different categories. Without such measures, the recommender system would have to have a user model for each product category. To enhance the responsiveness of the recommender system, most calculations and fuzzifications can be done offline. The gener￾ation and fuzzification of the domain model (i.e. {DP, DF, DG} of products) can be done only when the product catalog is changed. The training of the ANFIS and the clustering of customers’ FIS can also be done offline. The only part that must be done online (or real-time) is the recommendation generation. In the information filtering recommendation, products of a category pass through a customers’ FIS to generate recommendation; this process is fast thanks to the efficiency of fuzzy operations. In the candidate expansion recommendation, products of a category are queried for certain fuzzified words. While in the crowd recommendation, the transaction database is queried for other products that have been bought by other customers in the same group. These queries need only one-time scan of the records of the database, which is efficient on modern database management systems. The database of the customers’ FIS rules has value for marketing and product design. Given the advances in science and technology, products could have been designed with outstanding features and still remain fairly green. However, the price tag would have held customers back. Consumer electronics companies can gain from the FIS rules the insight of how much more customers are willing to pay for extra features and high greenness. There is little research of recommender system for green prod￾ucts. Liao (2008) proposed a green product recommendation sys￾tem in which the product database is queried against user designated weights of green criteria. EPEAT is used to represent the greenness of products as well. Liao’s system is effective in rec￾ommending green products; however, users are required to input the weights of certain green indices. In our system, on the other hand, users’ criteria of the three decision variables are implicitly gathered via users’ rating of products. For users who do not want the hassle, our system can use the quasi-conventional wisdom FIS to recommend product. Reducing the level of user intervention is important to the success of a recommender system. The pro￾posed architecture is in line with other commercially available rec￾ommender systems, such as Cinematch of Nextflix or Genius of iTunes, because in these systems, rating data is the minimal re￾quired input. 4. Conclusions In this paper, we proposed using recommender systems to aid the green shopping process and to promote green consumerism based upon the benefits of recommender systems and the FITD compliance technique. A proposed recommender system architec￾ture in the context of green consumer electronics was then de￾scribed and discussed. Previous research on this type of recommender system used weights of certain green indices as in￾puts. The architecture proposed in this paper accepts implicit and explicit criteria by modeling user preference and by considering ad hoc modification. The domain model is defined by the normalized Fig. 8. The rules of the quasi-conventional wisdom FIS. 9702 Y.-L. Lee, F.-H. Huang / Expert Systems with Applications 38 (2011) 9696–9703