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Personalized next-song recommendation in online karaokes(Recsys 2013)
 

Personalized next-song recommendation in online karaokes(Recsys 2013)

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Personalized next-song recommendation in online karaokes

Personalized next-song recommendation in online karaokes

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    Personalized next-song recommendation in online karaokes(Recsys 2013) Personalized next-song recommendation in online karaokes(Recsys 2013) Presentation Transcript

    • Personalized next-song recommendation in online karaokes Xiang Wu, et al. Recsys2013 読み会 @y_benjo
    • 流れ • 問題設定 • 手法 • 評価 • 感想
    • 問題設定 ? • カラオケでユーザが次に何を歌うかを当てたい • 既存研究 • • music recommendationはユーザのratingを当てる 「次に何を歌うか?」とは問題設定がそもそも異なる
    • hort-term and long-term preferences. Stra 手法 described as: Pr ( sb |sa , u) ∝ − x( sa )−x( sb ) e 次に選ぶ曲 曲同士 2 − 2 y(u)−x( sb ) 2 2 曲とユーザ 2 − x( sa )−x( sb ) 2 Pr ( sb |sa ) is proportional to e • Personalized Markov Embedding(PME) b er information is ignored by LME, while • 曲同士の近さ(short term) PME. 曲とユーザの近さ(long term) • • の2つを学習する • 学習にはMetric Embeddingを使う
    • Metric Embeddingとは? • 大雑把に言うと, • 近いものは近く • 遠いものは遠く • に配置されるような空間(に写像する関数)を学習する • ※metric learningは距離関数を学習するので少し違う
    • hort-term and long-term preferences. Stra それを踏まえて式を見ていく described as: Pr ( sb |sa , u) ∝ 次に選ぶ曲 − x( sa )−x( sb ) e 2 − 2 曲同士 y(u)−x( sb ) 2 2 曲とユーザ 2 − x( sa )−x( sb ) 2 Pr ( sb |sa ) is proportional to e er information is ignored by LME, while b • 学習するのはxとy PME. R^d次元の曲/曲空間と曲/ユーザ空間を考えたい • • • x: 曲をR^d次元の空間に写像する関数 y: 曲をR^d次元の空間に写像する関数
    • 超大雑把な例え • 曲 - 曲空間(R^2) アニソン メタル ボカロ ロック アイドル 演歌 サザン 懐メロ
    • 1 2 |U| Then, we could transform Equation (3) into its equivalent form 学習: 最尤推定 by applying the ln function: (X, Y) = arg max X,Y | pu | | pu, j | (u,pu )∈D j=1 k=2 = arg max X,Y u∈U sa ∈S sb ∈S = arg max X,Y u∈U sa ∈S sb ∈S − y (u) − x (sb ) def 2 2 (k−1) pu, j , u cu,sa ,sb ln Pr (sb |sa , u )      cu,sa ,sb − x (sa ) − x (sb )   − ln = arg max L1 (D |X, Y ) X,Y ln Pr (k) pu, j s∈S 2 2 (4)   2 − y(u)−x(s) 2   − x(sa )−x(s) 2 2  e   where cu,sa ,sb is the number of occurrence of song sb after song sa by
    • 学習: 最尤推定 (X, Y) = arg max X,Y | pu | | pu, j | (u,pu )∈D j=1 k=2 = arg max X,Y ln Pr u∈U sa ∈S sb ∈S (k) pu, j (k−1) pu, j , u cu,sa ,sb ln Pr (sb |sa , u )      cu,sa ,sb − x (sa ) − x (sb )   !!!max O(¦U¦ * ¦S¦^2) !!! = arg X,Y u∈U sa ∈S sb ∈S − y (u) − x (sb ) def 2 2 − ln = arg max L1 (D |X, Y ) X,Y s∈S 2 2   2 − y(u)−x(s) 2   − x(sa )−x(s) 2 2  e  
    • n, to overcome the time-consuming problem, we propo 学習 n (5) to simulate Equation (2). In this way, the two ty ean distances can be decoupled: Pr (sb |sa ) Pr (sb |u) = − x( sa )−x( sb ) e s∈S 2 2 − x(sa )−x(s) 2 2 e − y(u)−x( sb ) e 2 2 − y(u)−x(s) 2 2 s∈S e Pr ( sb |sa ) is the transition probability from song sa to so ( sb |u) is the probability of user u singing song sb . Not on (5) is not simply an assembled model, since all param • 重すぎるので,曲/曲と曲/ユーザの項を分解 e trained simultaneously. これでO(¦U¦ * ¦S¦) • a similar process of Equation (3) and Equation (4 owing あとは正則化して勾配法で最急降下法で学習 • get: (X, Y) = arg max cu,sa ,sb ln Pr (sb |sa ) Pr (sb |u)
    • 結果 • 評価指標 • Prec@K, Recall@K, F-1@K, MAP@K • bigram, LME, LME + UE • 比較手法 • LMEは曲のみを metric embedding する手法 • 注目する部分 • 学習データに登場する回数が少ない曲ほど高精度に当て られている
    • transitions have comparably low occurrence rate, PME could outperforms LME and Bigram in real applications, i.e., PME is better at predicting the unseen and sparse data. Embedding例 5 Songs Songs of User 1 Songs of User 2 Songs of User 3 4 [4] [5] [6] 3 [7] 2 User 2 1 User 1 -4 -3 -2 [8] 0 -1 0 1 2 3 4 5 -1 User 3 [9] -2 -3 [10] -4 Figure 4: Visualization of PME in R2 . Case Study. Figure 4 is a visualization of the trained PME model in R2 where all songs are represented by blue dots and 3 randomly picked users are represented by circles with different colors. We [11] [12] dat H.on vol S. C pre 714 S. F tec 13( J. L Eva 22( K. pro fee N. rec and 201 Q. col ran K. rec Pro E. app
    • 感想 • Metric Embedding面白そう • R^20とかR^50に飛ばすのは次元の呪いとか問題無い? • 13000ユーザに対してアイテムが943 • データが少し密? • 勾配の計算だるそう