K.j. Kim, H. Ahn Expert Systems with Applications 34(2008)1200-1209 There are two prevalent approaches for recommenda Nonetheless, it also has many critical limitations. Usu- ion-collaborative filtering(CF)and content-based( CB) ally, it cannot recommend newly added items, and requires methods. Both approaches have weaknesses as well as high computational complexity as the user base increases. strengths. However, CF has been used more frequently in Moreover, it mainly relies on annotations by users and pro- real-world applications vides meaningless results in heterogeneous domains such as Cf works by collecting user feedback in the form of rat- food. However, the most critical problem is that it cannot ings for items in a given domain and exploiting similarities make any recommendations when customers have not and differences among profiles of several users in determin- bought items repeatedly, which is generally called thespar ing how to recommend items. In general, CF can make sity problem'(Cho Kim, 2004; Cho, Kim, Kim, 2002; accurate recommendations in homogeneous domains, Good et al., 1999; Herlocker, Konstan, riedl, 2000: and it requires lower computational complexity when the Kim, Cho, Kim, Kim, Suh, 2002; Resnick, lacovou amount of the collected data set is small. Moreover, it Suchak, Bergstrom, 1994) can recommend items in novel territories because it deter These limitations are very critical in particular for Inter mines items to be recommended by considering only net shopping malls with specialized items. In the case of similarity between users, not characteristics of products our target shopping mall which is specialized for dieters, (Konstan et al., 1997, Pazzani, 1999 80.44% of total buyers had purchased just one time at Table 1 Features and their descriptions Code Description ADDO Residences are located in Seoul (the capital of South Korea 0: False/1: T Al Residences are located in big cities 0: False/1: T ADD2 Residences are located in the places other than Seoul and big cities 0: False/1: True CUO stomers work for companies CCU Customers are housewives 0: False/1:True oCCUr 0: False/1:True CCU Customers run their own businesses 0: False/1:True CCU EX Gender 0: Male/1: Female MARRIED 0: False/1: True LOSSI Customers'need to lose weight around their bellies 0: Not exist/1: Exist Customers need to lose weight around their hip 0: Not exist/1: Exist Customers' need to lose weight around their waists 0: Not exist/1: Exist Customers' need to lose weight around their faces 0: Not exist/1: Exist Customers' need to lose weight around their backs 0: Not exist/1: Exist Customers need to lose weight around their legs and thigh 0: Not exist/1: Exist LOSS7 Customers' need to lose weight around their arms 0: Not exist/1: Exist LOSS& Customers' need to lose weight around their chests 0: Not exist/1: Exist Customers' need to lose weight around their shoulders 0: Not exist/1: Exist 0: False/1: True Customers diet for urgent purpose such as employment, marriage ustomers diet for the reasons other than PURo. Purl WAIST Continuous (inch) Customers target weight to reduce tinuou BMI Body mass index(BMi) is the measure of body fat based on height and weight that applies Continuous(kg/m to both adult men and women. It is calculated as follows: BMI(kg/m2)=weight False/1: Tr D ustomers experienced constipation 0: Not exist/1: Exis 3 ustomers experienced hypertension or high blood fat 0: Not exist/1: Exis Customers experienced"anemia' or'maInutrition 0: Not exist/1: Exis Prior experience with functional diet food 0: Not exist/1: Exis E02 Prior experience with 'diet drugs 0: Not exist/1: Exis Prior experience with 'diuretics or the drugs generating 'diarrhea 0: Not exist/1: Exis Prior experience with hunger 0: Not exist/1: Exis Prior experience with ' folk remedies' such as 'one food diet and " Denmark die Prior experience with fasting treatment 0: Not exist/1: Exist E07 Prior experience with 'diet clinic 0: Not exist/1: Exist Prior experience with 'oriental diet treatment 0: Not exist/1: Exist Prior experience with " toning sy 0: Not exist/1: Exist Prior experience with other diet methods other than EO1-E09 0: Not exist/1: ExistThere are two prevalent approaches for recommenda￾tion – collaborative filtering (CF) and content-based (CB) methods. Both approaches have weaknesses as well as strengths. However, CF has been used more frequently in real-world applications. CF works by collecting user feedback in the form of rat￾ings for items in a given domain and exploiting similarities and differences among profiles of several users in determin￾ing how to recommend items. In general, CF can make accurate recommendations in homogeneous domains, and it requires lower computational complexity when the amount of the collected data set is small. Moreover, it can recommend items in novel territories because it deter￾mines items to be recommended by considering only similarity between users, not characteristics of products (Konstan et al., 1997; Pazzani, 1999). Nonetheless, it also has many critical limitations. Usu￾ally, it cannot recommend newly added items, and requires high computational complexity as the user base increases. Moreover, it mainly relies on annotations by users and pro￾vides meaningless results in heterogeneous domains such as food. However, the most critical problem is that it cannot make any recommendations when customers have not bought items repeatedly, which is generally called the ‘spar￾sity problem’ (Cho & Kim, 2004; Cho, Kim, & Kim, 2002; Good et al., 1999; Herlocker, Konstan, & Riedl, 2000; Kim, Cho, Kim, Kim, & Suh, 2002; Resnick, Iacovou, Suchak, & Bergstrom, 1994). These limitations are very critical in particular for Inter￾net shopping malls with specialized items. In the case of our target shopping mall which is specialized for dieters, 80.44% of total buyers had purchased just one time at Table 1 Features and their descriptions Code Description Range AGE Age Continuous (yrs) ADD0 Residences are located in Seoul (the capital of South Korea) 0: False/1: True ADD1 Residences are located in big cities 0: False/1: True ADD2 Residences are located in the places other than Seoul and big cities 0: False/1: True OCCU0 Customers work for companies 0: False/1: True OCCU1 Customers are housewives 0: False/1: True OCCU2 Customers are students 0: False/1: True OCCU3 Customers run their own businesses 0: False/1: True OCCU4 Customers are jobless 0: False/1: True SEX Gender 0: Male/1: Female MARRIED Customers got married 0: False/1: True LOSS1 Customers’ need to lose weight around their bellies 0: Not exist/1: Exist LOSS2 Customers’ need to lose weight around their hips 0: Not exist/1: Exist LOSS3 Customers’ need to lose weight around their waists 0: Not exist/1: Exist LOSS4 Customers’ need to lose weight around their faces 0: Not exist/1: Exist LOSS5 Customers’ need to lose weight around their backs 0: Not exist/1: Exist LOSS6 Customers’ need to lose weight around their legs and thighs 0: Not exist/1: Exist LOSS7 Customers’ need to lose weight around their arms 0: Not exist/1: Exist LOSS8 Customers’ need to lose weight around their chests 0: Not exist/1: Exist LOSS9 Customers’ need to lose weight around their shoulders 0: Not exist/1: Exist PUR0 Customers diet for beauty 0: False/1: True PUR1 Customers diet for urgent purpose such as employment, marriage 0: False/1: True PUR2 Customers diet for the reasons other than PUR0, PUR1 0: False/1: True HEIGHT Height Continuous (m) WEIGHT Weight Continuous (kg) WAIST Waist Continuous (inch) OBJ Customers’ target weight to reduce Continuous (kg) BMI Body mass index (BMI) is the measure of body fat based on height and weight that applies to both adult men and women. It is calculated as follows: BMI ðkg=m2Þ ¼ weight ðkgÞ ðheight ðmÞÞ2 Continuous (kg/m2 ) D1 Customers experienced ‘edema’ 0: False/1: True D2 Customers experienced ‘constipation’ 0: Not exist/1: Exist D3 Customers experienced ‘hypertension’ or ‘high blood fat’ 0: Not exist/1: Exist D4 Customers experienced ‘anemia’ or ‘malnutrition’ 0: Not exist/1: Exist E01 Prior experience with ‘functional diet food’ 0: Not exist/1: Exist E02 Prior experience with ‘diet drugs’ 0: Not exist/1: Exist E03 Prior experience with ‘diuretics’ or the drugs generating ‘diarrhea’ 0: Not exist/1: Exist E04 Prior experience with ‘hunger’ 0: Not exist/1: Exist E05 Prior experience with ‘folk remedies’ such as ‘one food diet’ and ‘Denmark diet’ 0: Not exist/1: Exist E06 Prior experience with ‘fasting treatment’ 0: Not exist/1: Exist E07 Prior experience with ‘diet clinic’ 0: Not exist/1: Exist E08 Prior experience with ‘oriental diet treatment’ 0: Not exist/1: Exist E09 Prior experience with ‘toning system’ 0: Not exist/1: Exist E10 Prior experience with other diet methods other than E01-E09 0: Not exist/1: Exist K.-j. Kim, H. Ahn / Expert Systems with Applications 34 (2008) 1200–1209 1205