DEIM Forum 2009 E

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1 DEIM Forum 2009 E Abstract Study on a Recipe Recommendation Method to Reflect Personal Preference and Nutritional Information Fushi LI, Mayumi UEDA, Yasushi HIRANO, Shoji KAJITA, and Kenji MASE Graduate School of Information Science, Nagoya University Furo-cho 1, Chikusa-ku, Nagoya, Aichi, , Japan Graduate School of Informatics, Kyoto University Yoshida Nihonmatsu-cho, Sakyo-ku, Kyoto, Kyoto, Japan Information Technology Center, Nagoya University Furo-cho 1, Chikusa-ku, Nagoya, Aichi, Japan There are many web sites recommending cooking recipe. They present cooking recipe in the order of entry date, access frequency or user s ratings, which can not reflect personal preferences and health condition. We are engaged on developing recipe recommendation method based on personal use history of foodstuff to reflect personal preferences. However, we consider nutritional information is important for healthy life. In this paper, we propose a recipe recommendation method which considers dietary preference and nutritional information of foodstuff. Our new recommendation method is re-ranking recipe ranking based on nutritional information. Key words Cooking History Recommendation System, Recipe Recommendation, Nutritional Information, Personal Preference, 1. Web NHK [1] 3 [2]

2 Web 46 13,391 1 [3] TF-IDF (Term-Frequency - Inverted Document Frequency) FF-IRF (Foodstuff Frequency - Inverted Recipe Frequency) (FF) (irf ) [3] (1) (2) (3) [4] [5] [4] [6] 6 100g [7] [3] Web 1 2 F k 3 k 4 Score i (1) 2. 2 F k F K TF-IDF (Foodstuff Frequency) (Inverted Recipe Frequency) ( (1))

3 F k = f k irf k (1) k f k k W c = (c 1)/c ( (2)) t W c 1 (k i c), f k = c=1 0 (k i c ). c(1 < = c < = t) i c c W c (1) (2) f (2) 2 f = 7 c=1 c 1 c 1 = 2.36 k irf k ( (3)) irf k = log 10 (M k /M) (3) M M k k M = 465, 325 M = 13, 391 irf = 1.54 F k = f irf k = 3.64 i Score i F k ( (4)) Score i = t c=1 F k irf k irfk (4) 2. 3 FF-IRF [3] 1. 13, ( (5)) = 25 (1) (2) 2 (3) [8] (5)

4 3. FF-IRF [3] [4] (2005 ) [9] [9] (2005 ) a ) (2005 ) I( ) 1,750kcal II( ) 2,050kcal III( ) 2,350kcal I( ) 2,300kcal II( ) 2,650kcal III( ) 3,050kcal (2005 ) b ) (2005 ) (% ) 4 c ) ( ) (2005 ) g/ 50g/ 20 (% ) d ) () ( (% ) 6 17g/ 21g/ e ) 16 ( ) 4 ( ) (%) = (g) 9/ (kcal) (%)= (g) 4/ (kcal) (%)= (g) 4/ (kcal) 100

5 mg( 1.5g ) 8g 700mg 600mg 2300mg f ) 1 A B1 B2 C C 85mg 100mg 4. (2005 ) [9] ( 1 ) ( 2 ) 5 ( 1 ) k 2 k k k j j Score j k k Score j = Score i + Score i (6) Score i Score i k k Score i Score i Score i = Score i = Fk irf k irfk (7) Fk irf k irfk F k irf k (8) 1 ( (5))

6 7 8 DB ( 1) 1 1 DB 2 B Vol.40( ) pp [7] Web.IA Vol.103 No.62 pp [8] [9] (2005 ) /2004/11/h html [1] NHK nhk.or.jp/partner/ryouri/ [2] 3 [3] Letters Vol.6 No.4 pp [4] [5] [6] Web

7 1 (kcal) (g) (g) (g) (mg) (g) (20 ) (kcal) (g) (g) (g) (mg) (g) (20 )

Web [1] [2] [3] [4] [5] SupportVectorMachine SVM [6] [7] Google [11] Web

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