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1 c 1. [1] [2] CD POS 2 2. Tversky [3] Contrast Model [4] [5] Häubl & Trifts [6] Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited.
2 1 SVM Adoavicius and Tuzhilin [9] 2 2 Tapestry [7] GroupLens [8] Adoavicius & Tuzhilin [9] [10, 11] [12] 2 [13, 14, 15] [16] 2 Iacobucci et al. [17] [18] Breese et al. [1] i a j v i,aj ( 0,...,) E(v i,aj )= l=0 Pr(v i,aj = l v i,ak,a k I i) l I i i Breese et al. [1] Pr(v i,aj v i,ak ) [12] precision (recall) Breese et al. [1] Rank Scoring Kubacki [19] 2 CD A&R(Artist & Repertoire) [20] [21] CD ID POS CD 52 (2007) CD Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited
3 (1) 2 (2) (3) 1 (3-A) (3-B) CD I = {1,...,i,...,n} n A = {a 1,...,a j,...,a } U = {u i,j i( I) a j( A) 1 u i,j =1 u i,j =0} i( I) a, b( A) i a P i(b a) a i b P i(a b) i a b P i(a j A) i a j P i(a j A) 2 i a b [16] 2 a 2 a b b P i(a b) P i(b) P i(a b) P i(a b) 1+P i(a b) P i(b) 1 P i(b) 1 P i(b) 1 i 2 a, b P i(a b) P i(b) s i(a, b)= Pi(a)(1 )P i(b)(1 P i(b)) (1) (1) s i(a, b) s i(a, b) i a b P i(b a =1)(1) s i(a, b) Pi(a b) P i(b a =1)= = P i(b) 1 + s i(a, b) P i(b)(1 P i(b)) (2) a b Pi(b) Pi(a b) P i(b a =0)= 1 =P i(b) s i(a, b) Pi(b)(1 Pi(b)) (3) 1 (2) (3) { P i(b a)=p i(b) 1+t i,as i(a, b) ( 1 Pi(a) ) } ti,a 1 P i(b) P i(b) 1 a t i,a = 1 a (4) Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited.
4 (4) P i(b a) P 1 i(b) ( ) ti,a 1 Pi(a) 1 P i(b) =t i,as i(a, b) P i(b) (5) a b (1 Pi(a)/ t i,a a a a Pi(a)/1 2 2 a b s i(a, b) i A i a j b M i,b(a j) b M i,b(a) M i,b(a) M i,b(a j),j =1,..., M i,b(a j) 3-B 3 a, b, c P i(a, b c) =P i(a c)p i(b c). 3-A M i,b(a) A M i,b(a j) 1 (coitte) [22] M i,b(a j) (4) P i(b a j) M i,b(a) P i(b A) 3-B 2 (Naive Bayes classifier) A 2 3-A 2 i s i(a, b) s(a, b) 3-A M i,b(a j) a j A M i,b(a) M i,b(a j) (4) M i,b(a) (6) P i(b A) [ { = 1 P i(b) 1 j=1 ( ) }] ti,aj 1 Pi(a j) 1 Pi(b) +t i,aj s(a j,b) P i(a j) P i(b) Pi(b)(1 P i(b)) =P i(b)+ { ( ) } ti,aj 1 Pi(a j) t i,aj s(a j,b) (6) P i(a j) j=1 (6) s(a, b) s(a, b) 2 2 i a = Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited
5 =c, a A, c (7) (6) P i(b A) { c(1 c) 1 c =c + s i(a, b) c a j A i } c s i(a, b) 1 c a j Ā i A i i A i = {k u i,k =1} Ā i = A A i =c + 1 c s(a j,b) c s(a j,b) a j A i a j Ā i c 1 1 s(a j,b) (8) a j A i (8) s(a j,b) S P = 1 US (9) U S (9) B 2 3-B 3-B P i(a b)= k P i(a k b) (10) P i(b A) P i(b) P i(b =1) 1 P i(b) P i(b =0) P i(b =1 A) Pi(A b =1)Pi(b =1) = P i(a) P i(a b =1)P i(b =1) = P i(a b =0)P i(b =0)+P i(a b =1)P i(b =1) (10) = P i(b =1) k Pi(ak b =1) P i(b =0) Pi(ak b =0)+Pi(b =1) Pi(ak b =1) k k (11) 2 i s i(a, b) s(a, b) (4) a b { P i(a b)= 1+t i,bs(a, b) ( 1 Pi(b) P i(b) ) } ti,b 1 (12) (7) =c (12) P i(a b =1)=c +(1 c)s(a, b) (13) P i(a b =0)=c(1 s(a, b)) (14) (13) (14) (11) P i(b) P i(a k b =1) k = c k A i {c +(1 c)s(a k,b)} k Ā i {(1 c)(1 s(a k,b))} (15) (1 P i(b)) P i(a k b =0) k =(1 c) k A i {c(1 s(a k,b))} k Ā i {1 c(1 s(a k,b)} (16) (15) (16) (11) P i(b =1 A) CD ID POS K(= 3,...,7) Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited.
6 2K K 2 a, b s(a, b) =c c c = ui,j/n i,j / (9) U S = {s(a, b)} P i(b A) c s(a, b) (15) (16) (11) P i(b A) 2 SVM SVM S i w i S + b i e (17) w i b i e 1 i w i w i i a j u i,j/ (17) (9) P =1/ US i (9) S SVM R kernlab [23] Chang & Lin [24] Jaccard [15] Breese et al. [1] Rank Scoring 2 Rank Scoring i R i R i = {3, 5,...} R i i v i R ax i = {1, 2,...,v i} (18) i Rank(R i)= k R i 1 2 k 1 T 1 (18) T Rank Scoring (19) Rank(R i) 100 Rank Scoring = 100 i I i I Rank(Ri) Rank(Rax i ) (19) Breese et al. [1] T T 2 2 T =10Rank Scoring [12] Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited
7 2 K =3 8 PAM (Partitioning Around Medoids) 2 PAM Kaufan & Rousseeuw [25] K = K =3 K / / 1 3 8, , , , , , , , , , Gr. 1/Gr. 2 / / 1 5,256/3, ,216/3, ,714/2, ,954/2, ,724/2, ,156/4, ,723/1, ,373/ CD CD 417,380 ID POS CD 82.5 [26] K =3 3 (= K) 6 (= 2K) 12 K K 2 K (= 33 12) K = 3 Rank Scoring 2 SVM K = K K Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited.
8 5 K =3 Rank Scoring / 7 Rank Scoring / K SVM / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /n.a. 2.92/ /n.a. 2.73/ / / / / / / / / / /0.01 n.a. p Rank Scoring / K SVM / / / / / / / / / / / / /n.a 6.61/n.a /0.24 n.a. p Rank Scoring K =7 SVM 2 3 SVM K = CD (book series) [20] [27] CD Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited
9 ! ! CD A B δ A,B δ A,B B = A (20) A 10 B B z CHEMISTRY No. 10 [] [] 1 25 [UFW a ] 79 [UFW a ] 2 2 [avex] 6 BoA [avex] KinKi Kids [ ] [avex] [UFW a ] CHEMISTRY [SME(J) b ] J-FRIENDS [( )] Every Little Thing [avex] [UFW a ] [SME(J) b ] 7 25 [UFW a ] 74 [UFW a ] 8 2 [avex] 3 B z [ ] [UFW a ] [UFW a ] 147 [UFW a ] 79 [UFW a ] UFW a SME(J) b Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited.
10 9 No avex 2 6 BoA avex 3 16 TLC Sony Music Entertainent 4 13 Every Little Thing avex 5 1 CHEMISTRY SME(J) a SME(J) a R EMI Sony Music Entertainent EMI SME(J) a SVM CD Kubacki [19] Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited
11 [1] J. S. Breese, D. Heckeran and C. Kadie, Epirical analysis of predictive algoriths for collaborative filtering, in Proceedings of the fourteenth Annual Conference on Uncertainty in Artificial Intelligence, 43 52, [2] E. Brynjolfsson and A. McAfee, Big data: The anageent revolution, Harvard Business Review, 60, 60 68, [3] A. Tversky, Features of siilarity, Psychological Review, 84, , [4] [5] [6] G. Häubl and V. Trifts, Consuer decision aking in online shopping environents: The effects of interactive decision aids, Marketing Science, 19, 4 21, [7] D. Goldberg, D. Nichols, B. M. Oki and D. Terry, Using collaborative filtering to weave an inforation tapestry, Counications of the ACM, 35, 61 70, [8] P. Resnick, N. Iacovou, M. Suchak, P. Bergstro and J. Riedl, GroupLens: An open architecture for collaborative filtering of netnews, in Proceedings of the 1994 International ACM Conference on Coputer Supported Collaborative Work Conference, , [9] G. Adoavicius and A. Tuzhilin, Toward the next generation of recoender systes: A survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge and Data Engineering, 17, , [10] (1) 22, , [11] (2)(3) 23, , , [12] D. Jannach and M. Zanker, A. Felfernig and G. Friedrich, Recoender systes: An introduction, Cabridge University Press, [13] M. Wedel and K. A. Wagner, Market Segentation: Conceptual and Methodological Foundations 2nd ed., Kluwer Acadeic Publisher, [14] A. N. Albatineh, M. Niewiadoska-Bugaj and D. Mihalko, On siilarity indices and correction for chance agreeent, Journal of Classification, 23, , [15] J. C. Gower and P. Legendre, Metric and Euclidean properties of dissiilarity coefficients, Journal of Classification, 3, 5 48, [16] 2 38, 65 81, [17] D. Iacobucci, P. Arabie and A. Bodapati, Recoendation agents on the internet, Journal of Interactive Marketing, 14, 2 11, [18] 57, , [19] K. Kubacki and R. Croft, Mass arketing, usic, and orality, Journal of Marketing Manageent, 20, , [20] H. L. Vogel, Entertainent Industry Econoics: A Guide for Financial Analysis, 8th ed., Cabridge University Press, [21] CD CRM 16, 25 47, [22] C. M. Bishop, Pattern Recognition and Machine Learning, Springer, , [23] R Core Tea, R: A language and environent for statistical coputing, R Foundation for Statistical Coputing, Vienna, Austria, org/ (Accessed 12 June 2013). [24] C.-C. Chang and C.-J. Lin, LIBSVM: A library for support vector achines, tw/ cjlin/libsv [25] L. Kaufan and P. J. Rousseeuw, Finding Groups in Data, John Wiley & Sons, Inc., [26] ediauser/pdf/softuser2009.pdf [27] D. A. Aaker, Brand Portfolio Strategy, Free Press, Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited.
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c 816 Web 1. 30 [1] [2] [3, 4] [5] 10 [6] 185 8540 2 8 38 [5] [5, 7] [5] 3 (1) 608 18 Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited. (2) (3) 2. 2.1 Web 2014 1 2013 12 2.2
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