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/eswa2011.01.164
Recommender 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 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. 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 overload 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 mentioned above. Recommender systems for news, web pages, jokes, academic articles, consumer electronics, restaurants, and a plethora of other subject matters, have been researched and implemented (Adomavicius & Tuzhilin, 2005; Iijima & Ho, 2007). However, to our knowledge, few researches have dealt with recommender 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 design 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, ecolabeling remains one of the fundamental ways to inform the customers how green their products are and in what respect their products are green. Eco-labels, usually issued by third-party organizations, are textual or graphical presentations of the environmental 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 Electrical 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
Y-L Lee, F-H. Huang/ Expert Systems with Applications 38(2011)9696-9703 96 uccessful eco-labeling programs(Malcohn, Paulos, Stoeckle 2 Related work Wang,1994) q Public education campaign of eco-labeling programs can be 2.1. Recommender system le via methods such as media coverage, regulation, promotion, school curriculum, and so on. This research proposes using recom- Recommender systems have a variety of forms with different nender systems, in addition to these methods, as a means to edu- functions( Manouselis Costopoulou, 2007: Wan, Menon, &Rama cate and inform on-line customers. The justification of such prasad, 2007 ). Therefore, it warrants a clear definition of the kind proposal relies on two of the primary functions of a recommender of recommender systems this paper is dealing with Schein, Pope- system: information filtering and candidate expansion. When cus- scul, Ungar, and Pennock(2005)define recommender systems as tomers are confronted with a flood of products or with unfamiliar the following:"Recommender systems suggest items of interest products, they may have difficulty in making a shopping decision. to users based on their explicit and implicit preferences, the pref Based upon what they have purchased before, a recommender sys- erences of other users, and user and item attributes". This defini- tem can help the customers by filtering out items that are unlikely tion points out the fundamental parts and necessary input and to be preferred. For example, Amazon. com,s recommender syster output data of a recommender system. First, a recommender sys generates a personalized list of recommended products each time a tem needs data of preferences from single user or multiple users. customer visits their web site. As to candidate expansion, when a The system can explicitly elicit preferences from users by asking mender system can ensure that other good candidates are included ences from past transactions(Resnick Varian, 1997).Second in the consideration set by finding related products based upon the recommender system requires attributes of users and items. product under consideration. Take Amazon coms recommender Manouselis and Costopoulou(2007)refer to these two sets of attri- system for example again. When a customer is looking at the cat- butes as"user model"and"domain model", respectively. Several alog page of a product, the recommender system recommends representations can be used as user models, such as per user prod items similar to the current item. Tapping into the capabilities of uct ratings, demographic attributes, transaction histories, and so information filtering and candidate expansion, recommender sys- on. On the other hand, domain models can be represented as char tems can be transformed into a green product advocate informing acteristics of products and as derived attributes such as taxono- the customers of available choices that are greener. a re mies, hierarchies, and ontologies. Both models may utilize the mender system of green products can also sieve through a set of acquired user preferences to derive their own data. products to retrieve only the items matching an implicitly or The core of a recommender system is the mechanism of sugges- explicitly degree of greenness designated by a customer. Such sys- tion generation based upon the user model and domain model. The tem can also find other products whose greenness and other as- mechanism can be formulated as follows(Adomavicius Tuzhilin ects are comparable based upon a product under consideration. 2005): Let C be the set of all customers and P be the set of all prod- The reduced effort in the decision making process may enhance ucts that a recommender knows of. In addition, let U(c, p) be the he quality and users'satisfaction of the decision(Haubl Trifts, utility function that associates(c, p) pairs with utility values which 2000), which in turn will make green shopping a more enjoyable can be ratings, profits, or some other measurements. The objective experience of a recommender system is to find a set of items pE P such that The adaptability of a recommender system can also contribute U(c p)is maximized for a customer. The mathematical formulation to the promotion of green consumerism by using a techni is as follows: alled foot-in-the-door (FiTD) technique(Freedman Fraser 1966). FITD is a compliance technique in which a person is more cEC, p=argmaxU(c, p) likely to accept a larger request if this request is preceded by smaller request. The technique is also found to be effective in com- In the formulation, arg max"means"the argument of the puter-mediated communication(CMC) in addition to face-to-face maximum telephone communications(Gueguen, 2002 ). In a recommender Recommender systems can be generally classified into three system of green products, items with higher degree of greenness categories according to the mechanism of recommendation gener- and with comparable or equal degrees of price and feature can ation(Adomavicius Tuzhilin, 2005; Schein et al, 2005: (1)Con- be first recommended to a user who is reluctant to buy green prod tent-based systems recommend items that are similar to the ones a ucts. Appropriate feedback should be given to the user about the user preferred in the past. (2)Collaborative systems recommend environmental contribution of the purchase one has made The de- items that other like-minded users preferred in the past. (3)Hybrid gree of greenness of the recommended items in the future can be systems recommend items by combining content-based and col- adjusted accordingly if the users'purchasing transactions reflect laborative methods in recommendation generation. vicius and Tuzhilin(2005) out, content-based The goal of this paper is to develop a recommender system and collaborative systems have some challenges to be dealt with architecture for green consumer electronics. Instead of simply add- For content-based systems, the first problem is"limited content ing an additional green attribute to the conventional recommender analysis", in which case the recommendation is limited by the fea- systems, the architecture uses an adaptive behavioral agent to find tures associated with the items. However, some features are hard- the products of a certain degree of greenness according to users' er to extract than others are. For example, extracting features from behaviors. The agent uses an adaptive fuzzy inference system to textual information is easier than from multimedia data. Also, learn users'behavior over time with a basic assumption that a items that are identical in terms of features are indistinguishable ilateral relationship of either symbiosis or antibiosis exists be- The second problem is overspecialization, in which case the sys- tween the pairs of price vs feature price vs greenness, and feature tem can only recommend items that are similar to items a user s greenness. liked in the past. In other words, the lack of diversity may jeopar- The rest of this paper is organized as follows. The nex dize the practicality of a recommender system. The third problem gives brief review of recommender systems and fuzzy is"new user problem", in which case a user is unable to get reli- tems. The proposed architecture is presented and disci able recommendations until a sufficient amount of transactions Section 3. Conclusions and future research directions are presented are present for the recommender system to learn about the users in the final section
successful eco-labeling programs (Malcohn, Paulos, Stoeckle, & Wang, 1994). Public education campaign of eco-labeling programs can be done via methods such as media coverage, regulation, promotion, school curriculum, and so on. This research proposes using recommender systems, in addition to these methods, as a means to educate and inform on-line customers. The justification of such proposal relies on two of the primary functions of a recommender system: information filtering and candidate expansion. When customers are confronted with a flood of products or with unfamiliar products, they may have difficulty in making a shopping decision. Based upon what they have purchased before, a recommender system can help the customers by filtering out items that are unlikely to be preferred. For example, Amazon.com’s recommender system generates a personalized list of recommended products each time a customer visits their web site. As to candidate expansion, when a customer is evaluating the decision to buy a product, a recommender system can ensure that other good candidates are included in the consideration set by finding related products based upon the product under consideration. Take Amazon.com’s recommender system for example again. When a customer is looking at the catalog page of a product, the recommender system recommends items similar to the current item. Tapping into the capabilities of information filtering and candidate expansion, recommender systems can be transformed into a green product advocate informing the customers of available choices that are greener. A recommender system of green products can also sieve through a set of products to retrieve only the items matching an implicitly or explicitly degree of greenness designated by a customer. Such system can also find other products whose greenness and other aspects are comparable based upon a product under consideration. The reduced effort in the decision making process may enhance the quality and users’ satisfaction of the decision (Häubl & Trifts, 2000), which in turn will make green shopping a more enjoyable experience. The adaptability of a recommender system can also contribute to the promotion of green consumerism by using a technique called foot-in-the-door (FITD) technique (Freedman & Fraser, 1966). FITD is a compliance technique in which a person is more likely to accept a larger request if this request is preceded by a smaller request. The technique is also found to be effective in computer-mediated communication (CMC) in addition to face-to-face or telephone communications (Guéguen, 2002). In a recommender system of green products, items with higher degree of greenness and with comparable or equal degrees of price and feature can be first recommended to a user who is reluctant to buy green products. Appropriate feedback should be given to the user about the environmental contribution of the purchase one has made. The degree of greenness of the recommended items in the future can be adjusted accordingly if the users’ purchasing transactions reflect acceptance or rejection of the items. The goal of this paper is to develop a recommender system architecture for green consumer electronics. Instead of simply adding an additional green attribute to the conventional recommender systems, the architecture uses an adaptive behavioral agent to find the products of a certain degree of greenness according to users’ behaviors. The agent uses an adaptive fuzzy inference system to learn users’ behavior over time with a basic assumption that a bilateral relationship of either symbiosis or antibiosis exists between the pairs of price vs. feature, price vs. greenness, and feature vs. greenness. The rest of this paper is organized as follows. The next section gives brief review of recommender systems and fuzzy inference systems. The proposed architecture is presented and discussed in Section 3. Conclusions and future research directions are presented in the final section. 2. Related work 2.1. Recommender system Recommender systems have a variety of forms with different functions (Manouselis & Costopoulou, 2007; Wan, Menon, & Ramaprasad, 2007). Therefore, it warrants a clear definition of the kind of recommender systems this paper is dealing with. Schein, Popescul, Ungar, and Pennock (2005) define recommender systems as the following: ‘‘Recommender systems suggest items of interest to users based on their explicit and implicit preferences, the preferences of other users, and user and item attributes’’. This definition points out the fundamental parts and necessary input and output data of a recommender system. First, a recommender system needs data of preferences from single user or multiple users. The system can explicitly elicit preferences from users by asking them to rate some items, or implicitly by inferring their preferences from past transactions (Resnick & Varian, 1997). Second, a recommender system requires attributes of users and items. Manouselis and Costopoulou (2007) refer to these two sets of attributes as ‘‘user model’’ and ‘‘domain model’’, respectively. Several representations can be used as user models, such as per user product ratings, demographic attributes, transaction histories, and so on. On the other hand, domain models can be represented as characteristics of products and as derived attributes such as taxonomies, hierarchies, and ontologies. Both models may utilize the acquired user preferences to derive their own data. The core of a recommender system is the mechanism of suggestion generation based upon the user model and domain model. The mechanism can be formulated as follows (Adomavicius & Tuzhilin, 2005): Let C be the set of all customers and P be the set of all products that a recommender knows of. In addition, let U(c, p) be the utility function that associates (c, p) pairs with utility values which can be ratings, profits, or some other measurements. The objective of a recommender system is to find a set of items p0 e P such that U(c, p) is maximized for a customer. The mathematical formulation is as follows: 8c 2 C; p0 ¼ arg max p2P Uðc; pÞ In the formulation, ‘‘arg max’’ means ‘‘the argument of the maximum’’. Recommender systems can be generally classified into three categories according to the mechanism of recommendation generation (Adomavicius & Tuzhilin, 2005; Schein et al., 2005): (1) Content-based systems recommend items that are similar to the ones a user preferred in the past. (2) Collaborative systems recommend items that other like-minded users preferred in the past. (3) Hybrid systems recommend items by combining content-based and collaborative methods in recommendation generation. As Adomavicius and Tuzhilin (2005) point out, content-based and collaborative systems have some challenges to be dealt with. For content-based systems, the first problem is ‘‘limited content analysis’’, in which case the recommendation is limited by the features associated with the items. However, some features are harder to extract than others are. For example, extracting features from textual information is easier than from multimedia data. Also, items that are identical in terms of features are indistinguishable. The second problem is overspecialization, in which case the system can only recommend items that are similar to items a user liked in the past. In other words, the lack of diversity may jeopardize the practicality of a recommender system. The third problem is ‘‘new user problem’’, in which case a user is unable to get reliable recommendations until a sufficient amount of transactions are present for the recommender system to learn about the users’ preferences. Y.-L. Lee, F.-H. Huang / Expert Systems with Applications 38 (2011) 9696–9703 9697
Y-L Lee, F-H Huang/ Expert Systems with Applications 38(2011)9696-9703 Collaborative systems also have the"new user problem"which 3. The architecture is similar to that of content-based systems. Another two problem ollaborative systems are"new item problem"and sparsity. 3. 1. Definitions and assumptions New item problem refers to situations in which the system is un ble to recommend newly added items because these items have The core of the proposed recommender system architecture for fers to situations in which there are few like-minded users, or in learns users'preferences in terms of price, feature, and greenness. which there are items that are preferred by few people Feature is defined as the functional and non-functional characteris tics of a product. Functional characteristics are directly related to 2.2. Fuzzy set theory and fuzzy inference system the utilization of a product. Take digital camera for example, func tional characteristics may include lens magnifying power, sensor A fuzzy set a in x is a set of ordered pairs(Zimmermann, 2001 ): pixel count, battery stamina, and so on. On the other hand,non- functional characteristics are not directly related to the use of a A={(x,(x)kx∈x} oroduct, such as brand, industrial design, emotion invoked by the use of the product, and so on. Greenness is the degree of how green X is a collection of objects. F is called the membership function a product is, and price is the degree of the retail monetary value of a product. The representations of the degrees will be described later. that associates X with the membership space M A fuzzy set whose The adaptive behavioral agent has two assumptions. First, there membership space contains only 0 and 1 is identical to a crisp set In is a bilateral relationship between the pairs of price vs. feature, other words, a fuzzy set is a set without crisp and dichotomous boundary. Consider a set of"tall people". By classical set theory, a price vs greenness, and feature vs. greenness. The relationships berson would have to be taller than a clearly defined height to be are either symbiosis or antibiosis. For example, a product whose feature set is rich and innovative may have a relatively high price ncluded in the tall people set, which is incongruent with our com- However, a product in a competitive market may have an aggres- mon senses. By fuzzy set theory, the person, although slightly short- sively low pricing and still have a rich feature set. Likewise, green- er than a predefined height of tall people, is still considered tall only to a lesser degree. Such reasoning is more compatible with human ess may drive the price higher or lower. For example, green hinking and decision making(Zadeh, 1965 ). roducts are usua result of vigorous research and develop A Fuzzy Inference System(FIS)is a type of Expert System in ment or the use of new material or manufacturing process, which which fuzzy sets and fuzzy rules are used to represent human mean extra costs. Yet, cases, the new design, material, enowledge that is imprecise and uncertain(zimmermann, 2001). tween the three pairs are mutually enhancing or decreasing. So To illustrate the process of an FIS and the concept of fuzzy rules, feature can drive greenness higher or lower, and vice versa. Fig. 1 consider an example from Zimmermann(2001)which is about the setting of a special throttle of a technical process. To achieve illustrates the idea thecorrect"setting of the throttle, the following rules must b The second assumption is that customers make their shopping followed decisions according to the criteria of price, feature and greenness. The inclusion of greenness makes this assumption an unusual one. 1. If Temperature is Low and Pressure is Low, Then set Throttle to However, as the green consumerism is becoming a trend, such Medium assumption is rational. The exclusion of other criteria is a practical 2. If Temperature is Low and Pressure is High, Then set Throttle to simplification of the architecture. Lo 3. 2. Domain model Throttle)are described linguistically such as High or Low instead of The domain model defines the representation of products and as precious values. A user of such FIS would input the current val- the generation of the representations in our recommender architec- the degree of highness and lowness. The FiS will then aggregate stand for the degree of price, feature, and greenness, respectiv p es of Temperature and pressures which are then converted into ture. Each product is represented by three cardinal numbers that the degrees of input variables to infer the appropriate settings of the throttle (i.e. the output). 23. Fuzzy set theory and recommender systems Fuzzy set theory and its extensions have been used used to model users'knowledge of a subject matter to appropriate navigation technique(Kavcic, 2004). Fuzzy se can be used to bridge the gap between linguistic user inputs and precise attributes of products. In a consumer electronics shopping d system(Cao Li, 2007), customers'needs are elicited by answering some questions about their needs and concerns. Based on these answers and some predefined fuzzy sets, the system then computes the weight of each specification to get the suitability Functional scores of products. Fuzzy sets has been applied to model similari- ties between item vs item user vs item and user vs user in movie emendation( Perny Zucker, 2001). Fuzzy sets also has been Fig. 1. The bilateral relationship between the pairs of price vs feature, price vs. used to represent the characteristics of items and users(Wang, greenness, and feature vs greenness. Plus sign means enhancing and minus sign 2004) decreasing
Collaborative systems also have the ‘‘new user problem’’ which is similar to that of content-based systems. Another two problems of collaborative systems are ‘‘new item problem’’ and sparsity. New item problem refers to situations in which the system is unable to recommend newly added items because these items have yet to be included in the user preference data. Sparsity problem refers to situations in which there are few like-minded users, or in which there are items that are preferred by few people. 2.2. Fuzzy set theory and fuzzy inference system A fuzzy set e A in X is a set of ordered pairs (Zimmermann, 2001): e A ¼ fðx;le A ðxÞÞjx 2 Xg X is a collection of objects. le AðxÞ is called the membership function that associates X with the membership space M. A fuzzy set whose membership space contains only 0 and 1 is identical to a crisp set. In other words, a fuzzy set is a set without crisp and dichotomous boundary. Consider a set of ‘‘tall people’’. By classical set theory, a person would have to be taller than a clearly defined height to be included in the tall people set, which is incongruent with our common senses. By fuzzy set theory, the person, although slightly shorter than a predefined height of tall people, is still considered tall only to a lesser degree. Such reasoning is more compatible with human thinking and decision making (Zadeh, 1965). A Fuzzy Inference System (FIS) is a type of Expert System in which fuzzy sets and fuzzy rules are used to represent human knowledge that is imprecise and uncertain (Zimmermann, 2001). To illustrate the process of an FIS and the concept of fuzzy rules, consider an example from Zimmermann (2001) which is about the setting of a special throttle of a technical process. To achieve the ‘‘correct’’ setting of the throttle, the following rules must be followed: 1. If Temperature is Low and Pressure is Low, Then set Throttle to Medium. 2. If Temperature is Low and Pressure is High, Then set Throttle to Low. The input and output variables (i.e. Temperature, Pressure, and Throttle) are described linguistically such as High or Low instead of as precious values. A user of such FIS would input the current values of Temperature and Pressures which are then converted into the degree of highness and lowness. The FIS will then aggregate the degrees of input variables to infer the appropriate settings of the throttle (i.e. the output). 2.3. Fuzzy set theory and recommender systems Fuzzy set theory and its extensions have been used to model various aspects of recommender systems. For example, it has been used to model users’ knowledge of a subject matter to select an appropriate navigation technique (Kavcic, 2004). Fuzzy set theory can be used to bridge the gap between linguistic user inputs and precise attributes of products. In a consumer electronics shopping aid system (Cao & Li, 2007), customers’ needs are elicited by answering some questions about their needs and concerns. Based on these answers and some predefined fuzzy sets, the system then computes the weight of each specification to get the suitability scores of products. Fuzzy sets has been applied to model similarities between item vs. item, user vs. item, and user vs. user in movie recommendation (Perny & Zucker, 2001). Fuzzy sets also has been used to represent the characteristics of items and users (Wang, 2004). 3. The architecture 3.1. Definitions and assumptions The core of the proposed recommender system architecture for green consumer electronics is an adaptive behavioral agent that learns users’ preferences in terms of price, feature, and greenness. Feature is defined as the functional and non-functional characteristics of a product. Functional characteristics are directly related to the utilization of a product. Take digital camera for example, functional characteristics may include lens magnifying power, sensor pixel count, battery stamina, and so on. On the other hand, nonfunctional characteristics are not directly related to the use of a product, such as brand, industrial design, emotion invoked by the use of the product, and so on. Greenness is the degree of how green a product is, and price is the degree of the retail monetary value of a product. The representations of the degrees will be described later. The adaptive behavioral agent has two assumptions. First, there is a bilateral relationship between the pairs of price vs. feature, price vs. greenness, and feature vs. greenness. The relationships are either symbiosis or antibiosis. For example, a product whose feature set is rich and innovative may have a relatively high price. However, a product in a competitive market may have an aggressively low pricing and still have a rich feature set. Likewise, greenness may drive the price higher or lower. For example, green products are usually the result of vigorous research and development or the use of new material or manufacturing process, which mean extra costs. Yet, in some cases, the new design, material, or manufacturing process actually saves money. Relationships between the three pairs are mutually enhancing or decreasing. So, feature can drive greenness higher or lower, and vice versa. Fig. 1 illustrates the idea. The second assumption is that customers make their shopping decisions according to the criteria of price, feature, and greenness. The inclusion of greenness makes this assumption an unusual one. However, as the green consumerism is becoming a trend, such assumption is rational. The exclusion of other criteria is a practical simplification of the architecture. 3.2. Domain model The domain model defines the representation of products and the generation of the representations in our recommender architecture. Each product is represented by three cardinal numbers that stand for the degree of price, feature, and greenness, respectively. Functional Non-functional Fig. 1. The bilateral relationship between the pairs of price vs. feature, price vs. greenness, and feature vs. greenness. Plus sign means enhancing and minus sign decreasing. 9698 Y.-L. Lee, F.-H. Huang / Expert Systems with Applications 38 (2011) 9696–9703
Y-L Lee, F-H Huang/ Expert Systems with Applications 38 (2011)9696-9703 96 699 The degree of price is a representation of how expensive a pro ducers of consumer electronics reveal the greenness of their prod- uct is relative to other products under the same category and the ucts via eco-labels. However, most eco-labels are concerned with Z-score normalization of the products' prices is used to obtain this only some aspects of the overall greenness. For example, Energy degree. Let Pi be the set of all products under category i For a prod- Star reveals the information of energy consumption, and roHS uct pE P the degree of price DP of p is defined as the following hort for Restriction of Hazardous Substances Directive) the infor mation of raw material selection. The proposed architecture uses Analytic Hierarchy Process, or AHP(Saaty, 1987), to elicit experts knowledge of the eco-labels' weights relative to the overall green- Me and Sp, are the means and the standard deviations of the prices ness of a products, as Fig. 2 illustrates. After the process of AHP of products under category i, and Xp is the price of a particular prod calculation, the weights of eco-labels are obtained. Let E=e, ct p. Products with higher DP values are more expensive than ez,.. en be the set of all eco-labels the proposed architecture con those with lower Dp values siders, the vector W is the weights of (e1 e2, .. en). For a product In the same way the degree of feature represents the powerful- PEP, the Eco-label-based degree of greenness DGEco-label of p is ness of a product relatively to other products under the same cat- defined as the following egory. The average of the z-scores of the quantifiable specification of a product category is used to represent this degree. Let S be theDGEco-label set of all quantifiable specifications under category i. For a product PEP, the degree of feature DF of p is defined as the average of the z- scores of s'∈S,i,e, MECO-label and SEco-label are the means and the standard deviations of the eco-label-based of greenness of products under category DF=avg i. XEco-label is the eco ased degree of greenness of a particular product p, which as the following: Myp and Ssp are the means and the standard deviations of the spec- ification s'of products under category i, and X is the specifications XEco-labtlLxw of a particular product p Products witn nigner Dr values are more L is a vector whose n elements are either ero indicatin ifications are more favorable when their numbers are smaller: such whether product p has or does not have ular eco-label specifications need to be converted first. Therefore products whose DGEco-label are also Eco-label Xs=MAXs-Xs where MAXs is the maximal value of specification s 33. User model The degree of greenness is a representation of how product is in relation to other products under the same While DP and dF are readily derivable from the product ion, the degree of greenness is not. Most, if not all, produc t informa- While the domain model described above is a set of attributes the user model is represented by an FIS-based adaptive behavioral agent whose input variables are the attributes of the domain model logs do not have integrated ratings of the greenness of products. and the output variable is the estimated rating of a product. As de- In addition, the assessment of the greenness of a product, scribed in Section 2, an FIS contains fuzzy if-then rules that are de- usually involves a thorough Life Cycle Assessment(LCA), is consuming( Cooper& Fava, 2006). Therefore, the proposed determine the membership functions of an FIS is by observing the tecture uses two approximation methods to represent the degree input and output variables and choosing the membership func- enne tions that fit the variables. The resultant system is a static FIs The first representation is based upon the criteria of Electronic whose membership functions and parameters of the functions Products Environmental Assessment Tool(EPEAT) which is a rating are predetermined. Another method of constructing the fuzzy if- system that gauges the environmental performance of a product then rules is through a soft computing method called Adaptive according to the fifty-one criteria defined in IEEE 1680 standard. Neuro-Fuzzy Inference Systems(ANFIS), in which the rules are ob- EPEAT classifies products satisfying all the twenty-three required tained by training the system with the input and output variables criteria as Bronze Medal. all the twenty-three required criteria (ang. 1993). and 50% of the twenty-eight optional criteria as Silver Medal, and The proposed architecture uses ANFIS and the rating data of all the twenty-three required criteria and 75% of the twenty-eight products to obtain a Fis that represents a users'preference in terms optional criteria as Gold Medal. The proposed architecture uses the of price, feature, and greenness. The architecture trains the anFIs number of satisfied criteria as one of the representations of the de- by feeding it with training data set (p, DP, DF, DG, r IpE P), where gree of greenness. For a product pE P. the EPEAT-based degree of p is a product and r is the rating of a product supplied by the user greenness DGEPEAT of p is defined as the following: The resultant FIS of Pi for a user takes the input of DP, DF, DG) and MPEAT and sEPaT are the means and the standard deviations of the of the gre number of satisfied criteria of products under category i, and Xp is the number of satisfied criteria of a particular product p. The z-score DGEPEAT of a product uses only the data from other prod ucts whose dgEpEar are also EPEAt-based Although EPEAT is an increasingly popular overall greenness Eco-label 2 Eco-label n rating system, its database currently contains only 1175 products. To compensate this limitation, the proposed architecture uses a Fig. 2. The AHP used in the architecture to elicit experts knowledge of the weights complementary degree of greenness based on eco-labels. Most pro- of the eco-labels
The degree of price is a representation of how expensive a product is relative to other products under the same category, and the z-score normalization of the products’ prices is used to obtain this degree. Let Pi be the set of all products under category i. For a product p e Pi, the degree of price DP of p is defined as the following: DP ¼ Xp MPi SPi MPi and SPi are the means and the standard deviations of the prices of products under category i, and Xp is the price of a particular product p. Products with higher DP values are more expensive than those with lower DP values. In the same way, the degree of feature represents the powerfulness of a product relatively to other products under the same category. The average of the z-scores of the quantifiable specifications of a product category is used to represent this degree. Let S be the set of all quantifiable specifications under category i. For a product p e Pi, the degree of feature DF of p is defined as the average of the z-scores of s0 e S, i.e., DF ¼ avg s02S Xs0 Ms0Pi Ss0Pi Ms0Pi and Ss0Pi are the means and the standard deviations of the specification s0 of products under category i, and Xs0 is the specification s0 of a particular product p. Products with higher DF values are more powerful than those with lower DF values. Some quantifiable specifications are more favorable when their numbers are smaller; such specifications need to be converted first. Therefore, Xs0 ¼ MAXs0 Xs0 where MAXs0 is the maximal value of specification s0 . The degree of greenness is a representation of how green a product is in relation to other products under the same category. While DP and DF are readily derivable from the product information, the degree of greenness is not. Most, if not all, product catalogs do not have integrated ratings of the greenness of products. In addition, the assessment of the greenness of a product, which usually involves a thorough Life Cycle Assessment (LCA), is timeconsuming (Cooper & Fava, 2006). Therefore, the proposed architecture uses two approximation methods to represent the degree of greenness. The first representation is based upon the criteria of Electronic Products Environmental Assessment Tool (EPEAT) which is a rating system that gauges the environmental performance of a product according to the fifty-one criteria defined in IEEE 1680 standard. EPEAT classifies products satisfying all the twenty-three required criteria as Bronze Medal, all the twenty-three required criteria and 50% of the twenty-eight optional criteria as Silver Medal, and all the twenty-three required criteria and 75% of the twenty-eight optional criteria as Gold Medal. The proposed architecture uses the number of satisfied criteria as one of the representations of the degree of greenness. For a product p e Pi, the EPEAT-based degree of greenness DGEPEAT of p is defined as the following: DGEPEAT ¼ XEPEAT p MEPEAT Pi SEPEAT Pi MEPEAT Pi and SEPEAT Pi are the means and the standard deviations of the number of satisfied criteria of products under category i, and XEPEAT P is the number of satisfied criteria of a particular product p. The z-score DGEPEAT of a product uses only the data from other products whose DGEPEAT are also EPEAT-based. Although EPEAT is an increasingly popular overall greenness rating system, its database currently contains only 1175 products. To compensate this limitation, the proposed architecture uses a complementary degree of greenness based on eco-labels. Most producers of consumer electronics reveal the greenness of their products via eco-labels. However, most eco-labels are concerned with only some aspects of the overall greenness. For example, Energy Star reveals the information of energy consumption, and RoHS (short for Restriction of Hazardous Substances Directive) the information of raw material selection. The proposed architecture uses Analytic Hierarchy Process, or AHP (Saaty, 1987), to elicit experts’ knowledge of the eco-labels’ weights relative to the overall greenness of a products, as Fig. 2 illustrates. After the process of AHP calculation, the weights of eco-labels are obtained. Let E = {e1, e2, ..., en} be the set of all eco-labels the proposed architecture considers, the vector W is the weights of {e1, e2, ..., en}. For a product p e Pi, the Eco-label-based degree of greenness DGEco-label of p is defined as the following: DGEcolabel ¼ XEcolabel p MEcolabel Pi SEcolabel Pi MEcolabel Pi and S Ecolabel Pi are the means and the standard deviations of the Eco-label-based degree of greenness of products under category i. XEcolabel P is the Eco-label-based degree of greenness of a particular product p, which is defined as the following: XEcolabel p ¼ Lp W Lp is a vector whose n elements are either one or zero indicating whether product p has or does not have a particular eco-label. The z-score DGEco-label of a product uses only the data from other products whose DGEco-label are also Eco-label-based. 3.3. User model While the domain model described above is a set of attributes, the user model is represented by an FIS-based adaptive behavioral agent whose input variables are the attributes of the domain model and the output variable is the estimated rating of a product. As described in Section 2, an FIS contains fuzzy if-then rules that are derived from human experts’ knowledge of a system. One method to determine the membership functions of an FIS is by observing the input and output variables and choosing the membership functions that fit the variables. The resultant system is a static FIS whose membership functions and parameters of the functions are predetermined. Another method of constructing the fuzzy ifthen rules is through a soft computing method called Adaptive Neuro-Fuzzy Inference Systems (ANFIS), in which the rules are obtained by training the system with the input and output variables (Jang, 1993). The proposed architecture uses ANFIS and the rating data of products to obtain a FIS that represents a users’ preference in terms of price, feature, and greenness. The architecture trains the ANFIS by feeding it with training data set {p, DP, DF, DG, r |p e Pi}, where p is a product and r is the rating of a product supplied by the user. The resultant FIS of Pi for a user takes the input of {DP, DF, DG} and Which eco-label is the most determinant factor of the greenness of a product? Eco-label 1 Eco-label 2 …... Eco-label n Fig. 2. The AHP used in the architecture to elicit experts’ knowledge of the weights of the eco-labels. Y.-L. Lee, F.-H. Huang / Expert Systems with Applications 38 (2011) 9696–9703 9699
Y-L Lee, F-H Huang/ Expert Systems with Applications 38(2011)9696-9703 outputs r, the estimation of r, of product p. The process is illus-. 4. Recommendation generation trated in Fig 3. The use of the obtained Fis will be described later. To minimize the amount of user interaction the architecture Recommendation generation is the process that takes account uses Asynchronous JavaScript and XML (AJAX) to elicit the rating of the input variables(i.e. DP, DF, and dG)and produces an estima data via one click from the user. In addition, the architecture uses tion of the output variables (i.e. r). The proposed architecture ha the ANFis module of the Fuzzy Logic Toolbox of MATLAB as the three types of recommendation: information filtering, candidate core of the adaptive behavioral agent representing users' prefer- expansion, and crowd recommendation. ence. The ANFIS used in the proposed architecture is a five-layer The aim of information filtering is to prevent a customer from network with three input variables (i.e. DP, DF, and DG)and one being overwhelmed by the amount of information, such as unfa output variable(estimated r, or r). Fig 4 illustrates the resultant miliar products or a long list of products to choose from. In the pro- FIS of a sample pro-environment and price-sensitive customer. posed architecture, information filtering is very straightforward in Fig 4a is the rating matrix in which rows are (DP, DF, DG, r. Given that the customers' FIs acts like a filter. When a customer c is the characteristics of this customer, the products with low price browsing products in P. the DP, DF, and dg of each product p in and high degree of greenness are given the highest rating(5 Pi is passed to the Fis of the customer FISc and the one with max stars), while the others are given the lowest rating(1 star). The imal r is recommended to the customer, i.e. surface plot of the estimated output and the inputs is shown in VcEC, P=arg max FIS(p, DPp, DFp, DGp) The recommendation can generate one item(maximal r)or a num- ber of items(e.g. top five rs). The process of recommendation gen- eration is illustrated in Fig. 5 Rating data Product database Ad hoc customization Rule p, DP DF DG rI P∈P catena conversion ANFIS FIS of Best or Top-N database/DP, DF, DG) L customer c User model Fig 3. The generation of user model in the pro Fig. 5. The process of the first type of recommendation: information filtering 113311 131313 33 (b) Fig 4.(a) Sample rating data of a pro-environmental and price-sensitive customer; (b)The surface plot of the estimated output and the inputs
outputs r0 , the estimation of r, of product p. The process is illustrated in Fig. 3. The use of the obtained FIS will be described later. To minimize the amount of user interaction, the architecture uses Asynchronous JavaScript and XML (AJAX) to elicit the rating data via one click from the user. In addition, the architecture uses the ANFIS module of the Fuzzy Logic Toolbox of MATLAB as the core of the adaptive behavioral agent representing users’ preference. The ANFIS used in the proposed architecture is a five-layer network with three input variables (i.e. DP, DF, and DG) and one output variable (estimated r, or r0 ). Fig. 4 illustrates the resultant FIS of a sample pro-environment and price-sensitive customer. Fig. 4a is the rating matrix in which rows are {DP, DF, DG, r}. Given the characteristics of this customer, the products with low price and high degree of greenness are given the highest rating (5 stars), while the others are given the lowest rating (1 star). The surface plot of the estimated output and the inputs is shown in Fig. 4b. 3.4. Recommendation generation Recommendation generation is the process that takes account of the input variables (i.e. DP, DF, and DG) and produces an estimation of the output variables (i.e. r0 ). The proposed architecture has three types of recommendation: information filtering, candidate expansion, and crowd recommendation. The aim of information filtering is to prevent a customer from being overwhelmed by the amount of information, such as unfamiliar products or a long list of products to choose from. In the proposed architecture, information filtering is very straightforward in that the customers’ FIS acts like a filter. When a customer c is browsing products in Pi, the DP, DF, and DG of each product p in Pi is passed to the FIS of the customer FISc, and the one with maximal r0 is recommended to the customer, i.e. 8c 2 C; p0 ¼ arg max p2Pi FIScðp;DPp;DFp;DGpÞ The recommendation can generate one item (maximal r0 ) or a number of items (e.g. top five r0 s). The process of recommendation generation is illustrated in Fig. 5. {p, DP, DF, DG, r | p ∈ Pi } Fig. 3. The generation of user model in the proposed architecture. Fig. 4. (a) Sample rating data of a pro-environmental and price-sensitive customer; (b) The surface plot of the estimated output and the inputs. FIS of customer c User Model Product database Pi Best or Top-N recommendation {DP, DF, DG} ' r Rule adjustment Criteria designation Criteria conversion Ad hoc customization Non-model criteria Fig. 5. The process of the first type of recommendation: information filtering. 9700 Y.-L. Lee, F.-H. Huang / Expert Systems with Applications 38 (2011) 9696–9703