should be taken as the best alternative. Saaty’s AHP, is a well-known decision-making analytical tool used for modeling unstructured problems in various areas, e.g., social, economic, and management sciences (Bard and Sousk, 1990; Triantaphyllou and Mann, 1995; Wabalickis, 1988). After obtaining the weight vector, it is then multiplied with the weight coefficient of the element at a higher level. The procedure is repeated upward for each level, until the top of the hierarchy is reached (Saaty, 1980). Then the overall weight coefficient, with respect to the goal for each decision alternative is obtained. The alternative with the highest weight coefficient value should be taken as the best alternative. II.2 Former Studies Research One common model-based approach is to model customers’ preferences using a vector of weights representing relevancies of product attributes to a given customer. Lee et al. used this approach for recommending DVDs to customers. Their agent based system used genetic algorithms to learn customers’ preferences. Similar approach was used in (Pazzani, Bilssus, 2002) where the system learned user preferences for the source of document recommendation. (Kim et al, 2002) utilized web usage mining for decision tree induction in order to generate personalized recommendations in e-shopping support. One way to elicit customer preferences is through the use of conjoint analysis: a technique long used in marketing (Louviere, 1988). In this approach customer is presented with different products and feature combinations and asked to compare them in terms of desirability. Figure 2: Client-server system In a previous study by Rustam Vahidov and Fei Li in 2005, fuzzy modeling was used to select the optimum notebook according to the given customer preferences. They also used constraints to get the list of feasible candidates as we are going to do. They stated the reason for choosing fuzzy modeling that they were allowing the customers to have some level of softness in expressing their preferences. Their model also reflects the degree of vagueness in customer preferences. Instead, our model chooses the optimum candidate among many, and do not reflects the customer vagueness therefore we don’t need to use fuzzy modeling; instead we decided to model our study using AHP.
should be taken as the best alternative. Saaty’s AHP, is a well-known decision-making analytical tool used for modeling unstructured problems in various areas, e.g., social, economic, and management sciences (Bard and Sousk, 1990; Triantaphyllou and Mann, 1995; Wabalickis, 1988). After obtaining the weight vector, it is then multiplied with the weight coefficient of the element at a higher level. The procedure is repeated upward for each level, until the top of the hierarchy is reached (Saaty, 1980). Then the overall weight coefficient, with respect to the goal for each decision alternative is obtained. The alternative with the highest weight coefficient value should be taken as the best alternative. II.2 Former Studies Research One common model-based approach is to model customers’ preferences using a vector of weights representing relevancies of product attributes to a given customer. Lee et al. used this approach for recommending DVDs to customers. Their agent based system used genetic algorithms to learn customers’ preferences. Similar approach was used in (Pazzani, Bilssus, 2002) where the system learned user preferences for the source of document recommendation. (Kim et al, 2002) utilized web usage mining for decision tree induction in order to generate personalized recommendations in e-shopping support. One way to elicit customer preferences is through the use of conjoint analysis: a technique long used in marketing (Louviere, 1988). In this approach customer is presented with different products and feature combinations and asked to compare them in terms of desirability. Figure 2: Client-server system In a previous study by Rustam Vahidov and Fei Li in 2005, fuzzy modeling was used to select the optimum notebook according to the given customer preferences. They also used constraints to get the list of feasible candidates as we are going to do. They stated the reason for choosing fuzzy modeling that they were allowing the customers to have some level of softness in expressing their preferences. Their model also reflects the degree of vagueness in customer preferences. Instead, our model chooses the optimum candidate among many, and do not reflects the customer vagueness therefore we don’t need to use fuzzy modeling; instead we decided to model our study using AHP.
DSS%20for%20Notebook%20Selection.docx