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1 DEIM Forum 2016 E {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] [10] [5] [6] ( ) [5]

2 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) 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 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)

3 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) 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 = [8] 3. AR SAGH SAGH (1) ( ) (2) ( ) (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

4 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) = a b, c, d, e a = = 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) = h t t score track (h, t) (10) score track (t, h) = a h score t(a, t) (10) 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) = = 40 A A A listcounts(a) max listcounts max listcounts A log ( + 1) listcounts(a) A t score a (11)

5 1 Last.fm Aotm 30music Playlists Users Avg. Playlists/User Tracks Avg. Tracks/Playlist Artists Avg. Artists/Playlist Avg. Artist Usage 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) 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) max score track max score artist 4. (13) 10,50, 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 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) = 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 Last.fm 451 Aotm music 1458 Aotm MRR

6 : (s) Algorithm Last.fm Aotm 30music precision@10(hit rates MRR) PopRank Algorithm Last.fm Aotm 30music SAGH PopRank CAGH SAGH knn CAGH AR(n=2,w=10) knn SP(n=2,w=10) AR AR(n=3,w=100) SP SP(n=3,w=100) Proposed method 0.256* Proposed method score track score artist 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 SAGH CAGH knn AR SP Proposed method 0.339* score track score artist precision@100(hit rates MRR) Algorithm Last.fm Aotm 30music PopRank SAGH CAGH knn AR SP Proposed method 0.365* score track score artist SP Last.fm Hit rates SAGH AR score track AR score track 5 AR Last.fm Aotm Dietmar Jannach Iman Kamehkhosh (B)( :15H02701) (B)( : )

7 [1] Rakesh Agrawal, Ramakrishnan Srikant, et al. Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB, Vol. 1215, pp , [2] Geoffray Bonnin and Dietmar Jannach. A comparison of playlist generation strategies for music recommendation and a new baseline scheme. In Workshops at the Twenty- Seventh AAAI Conference on Artificial Intelligence, [3] Geoffray Bonnin and Dietmar Jannach. Evaluating the quality of playlists based on hand-crafted samples. In 14th International Society for Music Information Retrieval Conference, pp , [4] Geoffray Bonnin and Dietmar Jannach. Automated generation of music playlists: Survey and experiments. ACM Computing Surveys (CSUR), Vol. 47, No. 2, p. 26, [5] Jiajun Bu, Shulong Tan, Chun Chen, Can Wang, Hao Wu, Lijun Zhang, and Xiaofei He. Music recommendation by unified hypergraph: combining social media information and music content. In Proceedings of the international conference on Multimedia, pp ACM, [6] Ziyu Guan, Jiajun Bu, Qiaozhu Mei, Chun Chen, and Can Wang. Personalized tag recommendation using graph-based ranking on multi-type interrelated objects. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, pp ACM, [7] Negar Hariri, Bamshad Mobasher, and Robin Burke. Context-aware music recommendation based on latenttopic sequential patterns. In Proceedings of the sixth ACM conference on Recommender systems, pp ACM, [8] Dietmar Jannach, Lukas Lerche, and Iman Kamehkhosh. Beyond hitting the hits: Generating coherent music playlist continuations with the right tracks. In Proceedings of the 9th ACM Conference on Recommender Systems, pp ACM, [9] Brian McFee, Thierry Bertin-Mahieux, Daniel PW Ellis, and Gert RG Lanckriet. The million song dataset challenge. In Proceedings of the 21st international conference companion on World Wide Web, pp ACM, [10] Brian McFee and Gert RG Lanckriet. Hypergraph models of playlist dialects. In ISMIR, pp Citeseer, [11] Roberto Turrin, Massimo Quadrana, Andrea Condorelli, Roberto Pagano, and Paolo Cremonesi. 30music listening and playlists dataset [12] Ellen M Voorhees, et al. The trec-8 question answering track report. In TREC, Vol. 99, pp , 1999.

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DEIM Forum 2012 D2-1 606 8501 150 0002 2-15-1 28F E-mail: {tsukuda,ohshima,tanaka}@dl.kuis.kyoto-u.ac.jp, {miyamamoto,hiwasaki}@d-itlab.co.jp 1 Wikipedia Wikipedia HITS 1. Web Web Web 1 3 Wikipedia 2 Web