DEIM Forum 2018 C5-2 CGM Youtube pixiv comico CGM Co
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1 九州大学学術情報リポジトリ Kyushu University Institutional Repository 回帰分析による CGM 動画再生回数推定 柴田, 知親九州大学工学部電気情報工学科 伊東, 栄典九州大学情報基盤研究開発センター : 准教授 出版情報 : 電子情報通信学会データ工学研究専門委員会バージョン :published 権利関係 :
2 DEIM Forum 2018 C5-2 CGM Youtube pixiv comico CGM Consumer Generated Media CGM CGM CGM Deep Learning Multi Layer Perceptron Deep Learning CGM Deep Learning MLP 1. Youtube pixiv comico CGM Consumer Generated Media CGM CGM [1] [2] CGM CGM CGM ( ) [3] [4] MLP Multi Layer Perceptron MLP [5]
3 CGM 2. 2 Wikipedia CGM niconico ( ) ( ) ( ) niconico 239,753 [6] , , ,718,230 1,216, ( ) ( ) , Number 2007/3/6 2016/8/31 3,465,469,245 zip 64GB 200GB 14,269,919 video id title description watch num 3 comment num mylist num category tags upload time file type length size high size low zip ID (UNIX ) () 3.6GB 5.8GB (byte) (byte) 2 1 [7] ID out degree in degree in_degree, out_degree 1 in_degree: 3 out_degree: 1
![4 in degree out degree x " comments x # mylists x $ upload date x % video length x & out_degree x ' in_degree 2 w b intercept watch_num y = wx + b 2 [8] R 2 (1) (1) y i f](/docs-images/92/109058976/images/4-0.jpg "i µ R 2 = 1 n i=1 (yi fi)2 n i=1 (yi (1) µ)2 0 1 1 [9] 3. 2 5 VOCALOID 0.6 4. VOCALOID 5 0.601 VOCALOID 0.952 0.930 0.927 0.914 0.860 0.846 0.831 0.821 0.809 0.768 0.764 0.")
![756 0.729 0.708 0.708 0.704 0.696 0.695 0.695 0.682 0.664 0.649 0.644 0.628 0.615 0.605 R-18 0.605 0.570 0.569 0.565 0.484 [10] 3. 2 5 VOCALOID 4.](/docs-images/92/109058976/images/4-1.jpg "1 VOCALOID VOCALOID 3 3 2009 10 2009 10 29 VOCALOID 4 2009 10 29 2009 10 29 category VOCALOID tags tags VOCALOID category VOCALOID 1 tags VOCALOID 2 6 1 1A... 1D 2 2A,.")
4 4 in degree out degree x " comments x # mylists x $ upload date x % video length x & out_degree x ' in_degree 2 w b intercept watch_num y = wx + b 2 [8] R 2 (1) (1) y i f i µ R 2 = 1 n i=1 (yi fi)2 n i=1 (yi (1) µ) [9] VOCALOID VOCALOID VOCALOID R [10] VOCALOID 4. 1 VOCALOID VOCALOID VOCALOID category VOCALOID tags tags VOCALOID category VOCALOID 1 tags VOCALOID A... 1D 2 2A,... 2D 10 3 category VOCALOID
5 4 tags VOCALOID 6 VOCALOID 2007/ /08 () A 2007/ /10 () B 2009/ /04 () C 2012/ /10 () D 2013/ /08 () 4. 2 VOCALOID VOCALOID 8 8 VOCALOID 5 y i f i i ε i (2) (3) i = y i f i (2) ε i = yi fi y i (3) 7 5 1D 2C 1C A B C D A B C D D 2C 1C 2C 6 (1D)
![1 10 MikuMikuDance MMD Word2Vec Word2Vec Python gensim [11] Word2Vec (100 ) [6] Word2Vec 1 9 Niconico dataset (0000.zip 2956.zip) 0000.](/docs-images/92/109058976/images/6-1.jpg "zip Nicopdeia Crawler (Python) Nicopedia http://dic.nicovideo.")
6 8 1.33% VOCALOID 2.35% 2.18% 1.94% 1.91% 1.83% 1.65% 1.64% 1.47% 1.41% 1.29% 1.23% 1.20% 1.17% 1.11% 1.10% 1.10% 1.09% 1.06% 1.03% 0.99% 0.97% 0.94% 0.93% 0.87% 0.85% 0.82% 0.81% 0.74% 0.72% R % 0.67% 8 (1C) MikuMikuDance MMD Word2Vec Word2Vec Python gensim [11] Word2Vec (100 ) [6] Word2Vec 1 9 Niconico dataset (0000.zip 2956.zip) 0000.zip Nicopdeia Crawler (Python) Nicopedia Word2Vec (Python) Tags extractor (Python) HTML Scraper (Python) Word vectors Tags d 1 d 2 d n Nicopedia Documents 7 (2C) vector(v) = 1 vector(t) (4) m t v
7 4 t v vector(t) Word2Vec t m v 5. 2 Ward A... 1D 2A... 2D Ward [12] Ward [13] 2 15% A... 1D 2A... 2D 9 1A 56 2A 67 1B 43 2B 51 1C 38 2C 47 1D 44 2D 45 c j t i c j t i n i,j C {c : c t i} t i (5) (7) tfidf i,j tfidf i,j 5 tf i,j = ni,j k n k,j (5) C idf i = log {c : c t i} (6) tfidf i,j = tf i,j idf i (7) 2C VOCALOID MLP 6. 1 MLP (2C) tfidf 5 0 DB, DB, ,, KTKT 1 DB,, DB, , 2 DB,, DB, , UTAU 3 MikuMikuDance, MMD, MMD, Tda Append , 4 MikuMikuDance, MMD, Tda Append, , MMD 5 MMD, MikuMikuDance,, Lat, Tda Append 6 VOCALOID UTAU, UTAU , UTAU, UTAU, UTAU 7 VOCALOID UTAU, UTAU, UTAU ,, 8 UTAU,, 8P, MEIKO, HANASU, UTAU,,, UTAU,, UTAU, UTAU ROCK, 11,,, VO CAROCK, VOCALOID 12 VOCAROCK,,, VOCALOID, ,,, VOCALOID, VOCALOID 14,,, , Append 15, 8P, MEIKO,, ,,, , 17 VOCALOID, T.M.Revolution, KAITO, , VOCALOID VOCALOID 18 VOCALOID, VOCALOID, YAMAHA, UTAU, , KAITO, VOCALOID VOCALOID,, 20,,,, GUMI,, V3GUMI, GUMI, VOCAROCK 22 GUMI, V3GUMI, VOCALOID3, , GUMI 23 GUMI, V3GUMI,, , GUMI 24,,, , 25, inst,,, VOCALOID VOCALOID,, , KAITO, Append 27, VOCAROCK,, , 28,,, VOCALOID VOCALOID, 29, VOCALOID, ,, 30,, , VOCAMASH, 31 BGM,,, , 32,, BGM,, VOCALOID ProjectDIVAArcade, -ProjectDIVA-, DIVA, PS3, DIVA 34 VOCALOID,,, , VOCALOID 35,,,, , VocaNicoNight,, , 37,, VOCAMASH,, P 38,, ProjectDIVAArcade,, HD, PDA,, HD, PS ,,,, VOCALOID, P,, , 42 CeVIO,,, CeVIO , CeVIO 43,, VOICEROID,, exvoice 44 IA, IA,, VOCALOID VOCALOID, GUMI 45 VOCALOID,, MIRIAM, , Oliver 46 VOCALOID3, VOCALOID VOCALOID,,, (8) L MLP [14] L = 1 n n {(log(y i + 1) log(f i + 1)} 2 (8) i=1 2 1,2,...,7 4,8,12,24,
8 を示す 表 12 各クラスタの平均誤差率 (2C) クラスタ データ数 平均誤差率 クラスタ データ数 平均誤差率 抑えた Dropout は ノードをランダムに消去しながら学習す る手法である Dropout を用いることで 学習用データと評価 用データの精度の隔たりが小さくなる [16] 図 10 のモデルを 5. 節で定義した各クラスタに適用した そ の際 各クラスタ内の動画を学習用と評価用のデータにランダ ムで 9 対 1 に分割した パラメータの更新回数を 2000 回とし て学習し 評価用データに対して誤差関数 L が最小となる時 点でのパラメータを保存した 学習済みモデルに対し学習用と 評価用のデータを入力し その推定結果を用いて各クラスタの 図 10 MLP また 各 Affine 層に対し Dropout [15] を実装し 過学習を 6. 2 手 法 平均誤差率を算出した この操作を 10 回繰り返し クラスタ 毎に平均誤差率の平均値を算出した また 部分集合 1A... 1D 2A... 2D それぞれにおいて 各クラスタの平均誤差率 から 各部分集合の平均誤差率を算出した 6. 3 結 果 表 11 に 部分集合 1A,...,1D,2A,...,2D の平均誤差率を示す クラスタリングによる推定精度の差を見るため クラスタリン グを適用せずに上記手法で MLP を適用した際の結果も示す また 線形回帰分析で平均誤差率が最低であった部分集合 2C における クラスタ毎の平均誤差率を表 12 に 実測値と推定 値の関係を図 11 に示す 表 11 を見ると 1A 以外の部分集合ではクラスタリングによ り推定精度が向上している また 表 12 と表 10 を比較すると 各クラスタの平均誤差率は大幅に減少している さらに 図 11 を見ても 線形回帰分析における低再生回数部分の広がりが抑 えられていることが確認できる 図 11 MLP による実測値と推定値の関係 (2C) 表 11 各部分集合における平均誤差率 クラスタリングなし クラスタリングあり 平均誤差率 平均誤差率 1A B データ数との相関性は低い 今回 動画集合の多様性を低くす 部分集合 6. 4 考 察 表 12 を見ると 各クラスタで平均誤差率に差がある また 1C るため タグを用いた階層的クラスタリングを適用した しか 1D し 平均誤差率の高いクラスタにおいては 依然として動画の 2A B C D 多様性が高いと考えられる 本研究では 閾値をデンドログラムの下位 15%に設定した この閾値を下げることで クラスタ数を増やし より多様性の 低い動画集合を定義できる可能性がある また デンドログラ ムの枝分かれの回数でクラスタを定義したり 非階層的手法を 用いることで 改善を図れる可能性もある 学習データの数が 少ないと Neural Network の性能が下がるという指摘もあるた
9 [16] 7. CGM VOCALOID Word2Vec Ward MLP MLP CGM SNS SNS JSPS 15K00451 [11] gensim topic modeling for humans, gensim/. [12] E. Jones, T. Oliphant, P. Peterson et al., SciPy: Open source scientific tools for Python, [13],., [14] M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, TensorFlow: Large-scale machine learning on heterogeneous systems, [15] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, Journal of Machine Learning Research, vol. 15, pp , [16], Deep Learning Python., [1] N. Murakami and E. Ito, Emotional video ranking based on user comments, in Proceedings of iiwas2011. ACM, December 2011, pp [2] K. Kamihata and E. Ito, A quantitative contents diversity analysis on a consumer generated media site, in In Proceedings of AROB 21st 2016 (The Twenty-First International Symposium on Artificial Life and Robotics 2016), 2016, pp [3],, view count prediction/index.html, [4],, 29, vol. 70, pp , [5], , [6], CGM, in 29, 2017, pp [7], MCMC., [8] S. Seabold and J. Perktold, Statsmodels: Econometric and statistical modeling with python, Proceedings of the 9th Python in Science Conference, pp , [9],., [10],., 2014.
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