グループドデータã®éžè² å€¤è¡Œåˆ—å› å分解 - 廿TTモãƒãƒ™ãƒ¼ã‚·ãƒ§ãƒ³ ãŸã¨ãˆã°ã“ã†ã„ã†è¡¨ãŒã‚る。 gist.github.com 表ã®å·¦ã®ã»ã†ã«ãƒ¦ãƒ¼ã‚¶ãƒ¼å±¤ã®æƒ…å ±ã€å³ã®æ–¹ã«ãƒ¦ãƒ¼ã‚¶ãƒ¼å±¤ã”ã¨ã®ãƒ–ãƒã‚°ã¸ã®ã‚¢ã‚¯ã‚»ã‚¹çµŒè·¯ãŒæ›¸ã‹ã‚Œã¦ã„る。 ã©ã®ãƒ¦ãƒ¼ã‚¶ãƒ¼å±¤ãŒã©ã®çµŒè·¯ã‚’好むã‹çŸ¥ã‚ŠãŸã„ã¨ã™ã‚‹ã€‚ ãã“ã§ãƒˆãƒ”ックモデルã¨ã—ã¦ãƒã‚¢ã‚½ãƒ³åˆ†å¸ƒã‚’使ã£ãŸéžè² å€¤è¡Œåˆ—å› å分解を考ãˆã‚‹ã€‚ （トピックモデルシリーズ 6 GaP （Gamma-Poisson Model） - StatModeling Memorandum ãªã©ã‚’å‚照。） ユーザー層ãŒæ–‡æ›¸ã€ã‚¢ã‚¯ã‚»ã‚¹çµŒè·¯ãŒå˜èªžã«å¯¾å¿œã™ã‚‹ã€‚ ユーザー層ã®æƒ…å ±ã‚’æ¨ã¦ã¦ã€è¡Œåˆ—を分解ã—ã¦ã—ã¾ã†ã®ã¯ãŠã‚‚ã—ã‚ããªã„。 ユーザー層ã®æƒ…å ±ã‚’èª¬æ˜Žå¤‰æ•°ã¨ã—ã¦ã€ãƒ¦ãƒ¼ã‚¶ãƒ¼å±¤ã”ã¨ã«ãƒˆãƒ”ックã®æ§‹æˆãŒå¤‰ã‚るよã†ãªãƒ¢ãƒ‡ãƒ«ã«ã—ãŸã„。 モデル 観測値を行列ã®ç© ã§è¿‘ä¼¼ã™ã‚‹ã“ã¨ã‚’目指ã—ã¾ã™ã€‚ Y: 観測ã•ã‚ŒãŸåˆ†è§£ã—ãŸã„行列(Nè¡ŒK列) X: 観測ã•ã‚ŒãŸèª¬æ˜Žå¤‰æ•°(Nè¡ŒJ列)
LDAã¨ãã‚Œã§ãƒ‹ãƒ¥ãƒ¼ã‚¹è¨˜äº‹ãƒ¬ã‚³ãƒ¡ãƒ³ãƒ‰ã‚’作ã£ãŸã€‚ - tdualã®ãƒ–ãƒã‚°
An Automatic Approach for Document-level Topic Model Evaluation
[PDF]Hinton (2002) Training Products of Experts by Minimizing Contrastive Divergence
xiangze on Twitter: "å½¢ã ã‘ã®é€²æ— https://t.co/LLFwLr6rQr https://t.co/8YXTE44enz"
RCOアドテク部論文輪èªä¼šï¼šã€ŒCollaborative Topic Modeling for Recommending Scientific Articlesã€ã‚’紹介ã—ã¾ã—㟠- RCO アドテクLabブãƒã‚° | RCO アドテクLabブãƒã‚°
Sentence Level Recurrent Topic Model: Letting Topics Speak for Themselves
Topic Coherence To Evaluate Topic Models · Quentin Pleplé
LDA ã®ãƒ‘ープレã‚シティを使ã†ã¨ã - 木曜ä¸è¶³
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[PDF]Lau(2010) Best Topic Word Selection for Topic Labelling
[PDF]O'Callaghan+(2015) An Analysis of the Coherence of Descriptors in Topic Modeling
[PDF]Aletras+(2013) Evaluating Topic Coherence Using Distributional Semantics
[PDF]Lau(2014) Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality
[PDF]Chang(2009) Reading Tea Leaves: How Humans Interpret Topic Models
Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression
[PDF]Mimno(2011) Optimizing Semantic Coherence in Topic Models
[PDF]Lau(2011) Automatic Labelling of Topic Models
[PDF]Newman(2010) Automatic Evaluation of Topic Coherence
Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation — scikit-learn 0.17 documentation
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