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) decreasingCollaborative 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 un￾able to recommend newly added items because these items have yet to be included in the user preference data. Sparsity problem re￾fers 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 com￾mon senses. By fuzzy set theory, the person, although slightly short￾er 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 val￾ues 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 similari￾ties 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 characteris￾tics of a product. Functional characteristics are directly related to the utilization of a product. Take digital camera for example, func￾tional characteristics may include lens magnifying power, sensor pixel count, battery stamina, and so on. On the other hand, non￾functional 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 aggres￾sively low pricing and still have a rich feature set. Likewise, green￾ness may drive the price higher or lower. For example, green products are usually the result of vigorous research and develop￾ment 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 be￾tween 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 architec￾ture. 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