DEIM Forum 2016 E1-5 152 8550 2 12-1 152 8550 2 12-1 E-mail: {ueda,keyaki}@lsc.cs.titech.ac.jp, miyazaki@cs.titech.ac.jp,, 1. spotify 1 Last.fm ( ) [4] Hit rates [7] Hit rates [2] [8] [7] [3] 1 http://www.spotify.com http://last.fm [10] [5] [6] ( ) [5]
2. Hit rates 2. 1 Hit rates Hit rates [7] Hit rates n n 1 N Hit rates Hit rates Hit rates T rain T est h t r h t r (1) HitRate(T rain, T est) = 1 T est (h,t r ) T est 1 R(h) (t r ) (1) R(h) T est 1 R(h) (t r) t r R(h) 1 0 R(h) 10 50 100 2. 2 2. 2. 1 PopRank [9] Same artists - greatest hits(sagh) h h [9] t (2) score SAGH(t, h) = counts(t) 1 h (a) (2) 1 h (a) a h 1 0 counts(t) t Collocated artists - greatest hits(cagh) SAGH h [2] a b (3) sim a(a, b) = p (1 p(a) 1 p (b)) p 1p(a) p 1p(b) (3) 1 p(a) p a 1 0 t a (4) score CAGH (t, h) = b A h sim a (a, b) counts(t) (4) A h h 2. 2. 2 Association Rules(AR) Association Rule( ) A B A B A B h h h t 1, t 2,...t i (t 1), (t 1, t 2), (t 2), (t 2, t 3),...(t i 1, t i) t t [2] (5) score AR (t, h) = ω Ω confidence(ω t) (5)
Ω h [1] (ω t) n w w h confidence(ω t) ω t Sequential Pattern(SP) Sequential Pattern( ) [2] h t 1, t 2,...t i < t 1 >, < t 1, t 2 >,... < t i 1, t i > t t (6) score SP (t, h) = ω Ω confidence(ω t) (6) 2. 2. 3 knn k [7] h p (7) sim(h p) = h p (7) h p N h t (8) score knn (t, h) = n N h sim p(h, n) 1 n(t) (8) 1 n (t) n t 1 0 [7] knn k k = 10, [8] k = 300. k = 300 2. 3 [8] 3. AR SAGH SAGH (1) ( ) (2) ( ) 3. 1 3. 2 3. 1 (a, b, c), (a, b, c, d), (a, b, d, e) 1 a t cooc(a, t) a counts(a) cooc(a, b) = 3, cooc(a, c) = 2, cooc(a, d) = 2, cooc(a, e) = 1 counts(a) = 3
1 b e 3 1 a 1 c d = {(a,b,c),(a,b,c,d),(a,b,d,e)} counts(a) = 3 cooc(a,b) = 3,cooc(a,c) = 2,cooc(a,d) = 2,cooc(a,e) = 1 total_cooc(a) = 8 1 1 a b, c, d, e a = 3 + 2 + 2 + 1 = 8 a a t (9) score t (a, t) = cooc(a, t) max counts log ( + 1) (9) counts(a) max counts a a (9) cooc(a,t) a = 8 b, c, d, e cooc(a,b) = 3/8, cooc(a,e) 2/8 = 1/4, = 2/8 = 1/4, cooc(a,d) cooc(a,c) = = 1/8 a a max counts max counts log ( + 1) counts(a) TF-IDF TF a t IDF a f 7 b 6 a 5 7 Ar)st A e 5 10 c d listcounts(a) = 10 occur(a,a) = 5,occur(A,b) = 6,occur(A,c) = 10,occur(A,d) = 5, occur(a,e) = 7,occur(A,f) = 7 total_occur(a) = 40 2 - h t t score track (h, t) (10) score track (t, h) = a h score t(a, t) (10) 3. 2 2 A A A occur(a, t) occur(a, a) = 5, occur(a, b) = 6, occur(a, c) = 10, occur(a, d) = 5, occur(a, e) = 7, occur(a, f) = 7 total occur(a) total occur(a) = 5 + 6 + 10 + 5 + 7 + 7 = 40 A A A listcounts(a) max listcounts max listcounts A log ( + 1) listcounts(a) A t score a (11)
1 Last.fm Aotm 30music Playlists 2978 1040 8750 Users 451 142 1141 Avg. Playlists/User 6.60 7.32 7.67 Tracks 18081 11411 71472 Avg. Tracks/Playlist 11.70 16.99 12.52 Artists 3272 2770 17335 Avg. Artists/Playlist 4.55 12.76 8.61 Avg. Artist Usage 10.65 6.38 6.32 score a(a, t) = Artist reuse rate 65.28% 26.63% 40.07% occur(a, t) max listcounts log ( total occur(a) listcounts(a) + 1) (11) h t t score artist(h, t) (12) score artist (t, h) = score a (A, t) 1 h (A) (12) 3. 3 3. 1 3. 2 0.55 0.45 h t score track (h, t) score artist (h, t) t score track (h, t) score artist (h, t) max score track, max score artist (13) score(t, h) = score track(t, h) scoreartist(t, h) 0.55 + 0.45 max score track max score artist 4. (13) 10,50,100 100 PC CPU AMD Phenom ii x6 1090T 8GB 4. 1 [8] Last.fm Art-of-the-Mix [11] Last.fm web api 1 Avg. Artist Usage Artist reuse rate Aotm Last.fm Artist reuse rate 4. 2 Hit rates MRR(mean reciprocal rank) MRR 4. 2. 1 MRR(mean reciprocal rank) MRR [12] h t r h R(h) reciprocal rank (RR) R(h) t r R(h) t r (14) RR(h, t r ) = { 0 (1R(h) (t r ) = 0) 1 rank tr (1 R(h) (t r ) = 1) (14) rank tr R(h) t r reciprocal rank MRR (15) MRR(T rain, T est) = 4. 3 1 T est (h,t r ) T est RR(t r, h) (15) AR SP n w n = 3 w = 100 Proposed method score track score artist Hit rates MRR(mean reciprocal rank) p 0.05 * 10 Hit rates MRR 2 50,100 Hit rates MRR 3 4 100 5 Last.fm 451 Aotm 142 30music 1458 Aotm MRR
2 10 5 : (s) Algorithm Last.fm Aotm 30music precision@10(hit rates MRR) PopRank 0.030 0.010 0.099 Algorithm Last.fm Aotm 30music SAGH 0.034 0.030 0.205 PopRank 0.005 0.001 0.009 0.003 0.004 0.001 CAGH 27.1 6.29 531.5 SAGH 0.208 0.098 0.032 0.013 0.090 0.045 knn 203.9 115.2 830.0 CAGH 0.125 0.044 0.032 0.007 0.072 0.028 AR(n=2,w=10) 2.94 0.627 7.43 knn 0.236 0.143 0.058 0.039 0.078 0.039 SP(n=2,w=10) 2.46 0.380 4.37 AR 0.232 0.154 0.060 0.038 0.073 0.037 AR(n=3,w=100) 13.8 0.746 8.52 SP 0.202 0.135 0.063 0.042 0.058 0.028 SP(n=3,w=100) 14.6 0.506 5.55 Proposed method 0.256* 0.155 0.067 0.041 0.091 0.047 Proposed method 0.482 0.226 1.70 score track 0.234 0.151 0.056 0.037 0.070 0.034 score artist 0.212 0.100 0.037 0.013 0.085 0.041 3 50 precision@50(hit rates MRR) SAGH score artist 30music MRR score artist SAGH Last.fm Aotm score track Algorithm Last.fm Aotm 30music 30music score artist PopRank 0.018 0.001 0.025 0.004 0.009 0.001 SAGH 0.295 0.098 0.062 0.015 0.146 0.048 3 4 CAGH 0.277 0.044 0.083 0.010 0.140 0.032 knn 0.308 0.147 0.083 0.041 0.117 0.041 AR 0.302 0.157 0.086 0.038 0.114 0.039 SP 0.279 0.138 0.076 0.042 0.091 0.030 5. Proposed method 0.339* 0.160 0.095 0.042 0.151 0.050 score track 0.310 0.155 0.083 0.039 0.111 0.036 score artist 0.304 0.105 0.063 0.015 0.148 0.044 4 100 precision@100(hit rates MRR) Algorithm Last.fm Aotm 30music PopRank 0.027 0.002 0.048 0.004 0.016 0.002 SAGH 0.315 0.103 0.074 0.015 0.168 0.048 CAGH 0.329 0.052 0.114 0.010 0.170 0.032 knn 0.328 0.147 0.100 0.041 0.129 0.041 AR 0.323 0.158 0.093 0.040 0.129 0.039 SP 0.303 0.139 0.085 0.042 0.102 0.030 Proposed method 0.365* 0.160 0.120 0.042 0.178 0.050 score track 0.329 0.155 0.095 0.039 0.128 0.037 score artist 0.321 0.105 0.072 0.015 0.170 0.044 SP Last.fm Hit rates SAGH AR score track AR score track 5 AR Last.fm Aotm Dietmar Jannach Iman Kamehkhosh (B)( :15H02701) (B)( :26280115)
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