Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Collaborative Topic Modeling for Recommending S...
Search
Shinichi Takayanagi
May 30, 2016
Research
0
1.5k
Collaborative Topic Modeling for Recommending Scientific Articles
論文"Collaborative Topic Modeling for Recommending Scientific Articles"を読んだ際に使用したスライド
Shinichi Takayanagi
May 30, 2016
Tweet
Share
More Decks by Shinichi Takayanagi
See All by Shinichi Takayanagi
[NeurIPS 2023 論文読み会] Wasserstein Quantum Monte Carlo
stakaya
0
520
[KDD2021 論文読み会] ControlBurn: Feature Selection by Sparse Forests
stakaya
2
1.9k
[ICML2021 論文読み会] Mandoline: Model Evaluation under Distribution Shift
stakaya
0
2k
[情報検索/推薦 各社合同 論文読み祭 #1] KDD ‘20 "Embedding-based Retrieval in Facebook Search"
stakaya
2
610
【2020年新人研修資料】ナウでヤングなPython開発入門
stakaya
29
21k
論文読んだ「Simple and Deterministic Matrix Sketching」
stakaya
1
1.1k
Quick Introduction to Approximate Bayesian Computation (ABC) with R"
stakaya
3
340
The Road to Machine Learning Engineer from Data Scientist
stakaya
5
4.3k
論文読んだ「Winner’s Curse: Bias Estimation for Total Effects of Features in Online Controlled Experiments」
stakaya
1
4.7k
Other Decks in Research
See All in Research
EOGS: Gaussian Splatting for Efficient Satellite Image Photogrammetry
satai
4
300
公立高校入試等に対する受入保留アルゴリズム(DA)導入の提言
shunyanoda
0
5.9k
Cross-Media Information Spaces and Architectures
signer
PRO
0
230
ASSADS:ASMR動画に合わせて撫でられる感覚を提示するシステムの開発と評価 / ec75-shimizu
yumulab
1
400
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations
satai
3
220
Adaptive Experimental Design for Efficient Average Treatment Effect Estimation and Treatment Choice
masakat0
0
130
20250624_熊本経済同友会6月例会講演
trafficbrain
1
380
LLM-as-a-Judge: 文章をLLMで評価する@教育機関DXシンポ
k141303
3
830
【緊急警告】日本の未来設計図 ~沈没か、再生か。国民と断行するラストチャンス~
yuutakasan
0
140
最適決定木を用いた処方的価格最適化
mickey_kubo
4
1.7k
MGDSS:慣性式モーションキャプチャを用いたジェスチャによるドローンの操作 / ec75-yamauchi
yumulab
0
250
RHO-1: Not All Tokens Are What You Need
sansan_randd
1
130
Featured
See All Featured
How GitHub (no longer) Works
holman
314
140k
Making the Leap to Tech Lead
cromwellryan
134
9.4k
GraphQLとの向き合い方2022年版
quramy
49
14k
BBQ
matthewcrist
89
9.7k
The Myth of the Modular Monolith - Day 2 Keynote - Rails World 2024
eileencodes
26
2.9k
VelocityConf: Rendering Performance Case Studies
addyosmani
332
24k
Large-scale JavaScript Application Architecture
addyosmani
512
110k
Building a Scalable Design System with Sketch
lauravandoore
462
33k
Become a Pro
speakerdeck
PRO
29
5.4k
Fight the Zombie Pattern Library - RWD Summit 2016
marcelosomers
233
17k
Being A Developer After 40
akosma
90
590k
The Power of CSS Pseudo Elements
geoffreycrofte
77
5.9k
Transcript
RCO論文輪読会(2016/05/27) “Collaborative topic modeling for recommending scientific articles”(KDD2011) Chong Wang,
David M. Blei 高柳慎一
(C)Recruit Communications Co., Ltd. ABSTRACT 1
(C)Recruit Communications Co., Ltd. 1. INTRODUCTION 2
(C)Recruit Communications Co., Ltd. 1. INTRODUCTION 3
(C)Recruit Communications Co., Ltd. 1. INTRODUCTION 4
(C)Recruit Communications Co., Ltd. 2. BACKGROUND & 2.1 Recommendation Tasks
5
(C)Recruit Communications Co., Ltd. 2.1 Recommendation Tasks 6
(C)Recruit Communications Co., Ltd. 2.1 Recommendation Tasks 7
(C)Recruit Communications Co., Ltd. 2.2 Recommendation by Matrix Factorization 8
(C)Recruit Communications Co., Ltd. 2.2 Recommendation by Matrix Factorization 9
(C)Recruit Communications Co., Ltd. 2.2 Recommendation by Matrix Factorization 10
(C)Recruit Communications Co., Ltd. 2.2 Recommendation by Matrix Factorization 11
(C)Recruit Communications Co., Ltd. 2.3 Probabilistic Topic Models 12
(C)Recruit Communications Co., Ltd. LDAの生成過程 13
(C)Recruit Communications Co., Ltd. LDAの特徴 14
(C)Recruit Communications Co., Ltd. 3. COLLABORATIVE TOPIC REGRESSION 15
(C)Recruit Communications Co., Ltd. COLLABORATIVE TOPIC REGRESSION 16
(C)Recruit Communications Co., Ltd. CTRの生成過程 17
(C)Recruit Communications Co., Ltd. 3. COLLABORATIVE TOPIC REGRESSION 18
(C)Recruit Communications Co., Ltd. CTRのモデルのRegressionたる所以 19
(C)Recruit Communications Co., Ltd. 学習のさせ方 20
(C)Recruit Communications Co., Ltd. 学習のさせ方 21
(C)Recruit Communications Co., Ltd. 簡単な証明 by iPad手書き 22
(C)Recruit Communications Co., Ltd. 学習のさせ方 23
(C)Recruit Communications Co., Ltd. 予測 24
(C)Recruit Communications Co., Ltd. 4. EMPIRICAL STUDY 25
(C)Recruit Communications Co., Ltd. データの規模感 26
(C)Recruit Communications Co., Ltd. 評価 27
(C)Recruit Communications Co., Ltd. 結果 28
(C)Recruit Communications Co., Ltd. 結果 (ライブラリ内の論文数(Fig 5)・ある論文をLikeした数(Fig 6) 依存性) 29
数が増えると Recallが下がる (あまり有名な論文じゃ ないのを出すため) 数が増えると Recallが上がる (みんな見てる論文 だとCFがうまく動く)
(C)Recruit Communications Co., Ltd. 結果(ある2ユーザの好んだトピックを抽出) 30 トピックの潜 在ベクトルの 重みをランキ ングして抽出
(C)Recruit Communications Co., Ltd. 結果(オフセットの大きかった論文BEST 10) 31 ※内容よりもCFが効くケースに相当
(C)Recruit Communications Co., Ltd. 結果(EMの論文がベイズ統計勢にもよく参照されている例) 32 ※内容よりもCFが効く ケースに相当
(C)Recruit Communications Co., Ltd. 結果(逆にトピックが広がらない例) 33 ※内容が支配的なケー スに相当
(C)Recruit Communications Co., Ltd. 5. CONCLUSIONS AND FUTURE WORK 34