Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Collaborative Topic Modeling for Recommending S...

Avatar for Shinichi Takayanagi Shinichi Takayanagi
May 30, 2016
Research
1.6k
0

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

Avatar for Shinichi Takayanagi

Shinichi Takayanagi

May 30, 2016

More Decks by Shinichi Takayanagi

See All by Shinichi Takayanagi
論文紹介「Evaluation gaps in machine learning practice」と、効果検証入門に関する昔話
Avatar for Shinichi Takayanagi stakaya
0
1.3k
バイブコーディングの正体——AIエージェントはソフトウェア開発を変えるか？
Avatar for Shinichi Takayanagi stakaya
5
1.8k
[NeurIPS 2023 論文読み会] Wasserstein Quantum Monte Carlo
Avatar for Shinichi Takayanagi stakaya
0
610
[KDD2021 論文読み会] ControlBurn: Feature Selection by Sparse Forests
Avatar for Shinichi Takayanagi stakaya
2
2k
[ICML2021 論文読み会] Mandoline: Model Evaluation under Distribution Shift
Avatar for Shinichi Takayanagi stakaya
0
2.1k
[情報検索/推薦 各社合同 論文読み祭 #1] KDD ‘20 "Embedding-based Retrieval in Facebook Search"
Avatar for Shinichi Takayanagi stakaya
2
680
【2020年新人研修資料】ナウでヤングなPython開発入門
Avatar for Shinichi Takayanagi stakaya
28
22k
論文読んだ「Simple and Deterministic Matrix Sketching」
Avatar for Shinichi Takayanagi stakaya
1
1.3k
Quick Introduction to Approximate Bayesian Computation (ABC) with R"
Avatar for Shinichi Takayanagi stakaya
3
390

Other Decks in Research

See All in Research
National high-resolution cropland classification of Japan with agricultural census information and multi-temporal multi-modality datasets
Avatar for SatAI.challenge satai
3
310
Scalable dynamic origin-destination demand estimation enhanced by high-resolution satellite imagery data
Avatar for SatAI.challenge satai
3
300
老舗ものづくり企業でリサーチが変革を起こすまで - 三菱重工DXの実践
Avatar for kazu skydats
0
200
Harness Engineering and Al Agent
Avatar for Kazuki Inamura kzinmr
3
1.7k
AY 2026 Guide to Academic Writing Using Generative AI - Workshop
Avatar for Kenji Saito ks91
PRO
0
120
Research Engineerという仕事 / Research Engineering: Bridging Research and Business
Avatar for chck chck
1
220
進学校の生徒にはア行の苗字が多いのか
Avatar for Takashi Ozeki ozekinote
0
460
長時間動画QAにおけるマルチエージェント推論 ・SVAgent: Storyline-Guided Long Video Understanding via Cross-Modal Multi-Agent Collaboration
Avatar for 村川卓也 murakawatakuya
1
150
研究室単位での自律的 IPv6接続性確立に向けたAS共同運用モデルの提案と実証
Avatar for Hiroki (REO) Kashiwazaki reokashiwa
0
100
通時的な類似度行列に基づく単語の意味変化の分析
Avatar for hajime kiyama rudorudo11
0
320
typst の使い方：言語学を研究する学生のために
Avatar for Tomoya gitomochang
0
470
羽田新ルート運用6年の検証
Avatar for マン点 1manken
0
160

Featured

See All Featured
How To Stay Up To Date on Web Technology
Avatar for Chris Coyier chriscoyier
790
250k
YesSQL, Process and Tooling at Scale
Avatar for Rocio Delgado rocio
174
15k
Conquering PDFs: document understanding beyond plain text
Avatar for Ines Montani inesmontani
PRO
4
2.8k
Scaling GitHub
Avatar for Zach Holman holman
464
140k
Groundhog Day: Seeking Process in Gaming for Health
Avatar for Sebastian Deterding codingconduct
0
220
New Earth Scene 8
Avatar for Sydney Stone popppiees
3
2.4k
Evolving SEO for Evolving Search Engines
Avatar for Ryan Jones ryanjones
0
230
Deep Space Network (abreviated)
Avatar for Tony Rice tonyrice
0
210
Navigating Weather and Climate Data
Avatar for Ryan Abernathey rabernat
0
250
30 Presentation Tips
Avatar for Ian Lurie portentint
PRO
1
330
Fantastic passwords and where to find them - at NoRuKo
Avatar for Phil Nash philnash
52
3.7k
Design and Strategy: How to Deal with People Who Don’t "Get" Design
Avatar for Morgane Peng morganepeng
133
19k

Transcript

  1. 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

  2. （C）Recruit Communications Co., Ltd. ABSTRACT 1

  3. （C）Recruit Communications Co., Ltd. 1. INTRODUCTION 2

  4. （C）Recruit Communications Co., Ltd. 1. INTRODUCTION 3

  5. （C）Recruit Communications Co., Ltd. 1. INTRODUCTION 4

  6. （C）Recruit Communications Co., Ltd. 2. BACKGROUND ＆ 2.1 Recommendation Tasks

    5

  7. （C）Recruit Communications Co., Ltd. 2.1 Recommendation Tasks 6

  8. （C）Recruit Communications Co., Ltd. 2.1 Recommendation Tasks 7

  9. （C）Recruit Communications Co., Ltd. 2.2 Recommendation by Matrix Factorization 8

  10. （C）Recruit Communications Co., Ltd. 2.2 Recommendation by Matrix Factorization 9

  11. （C）Recruit Communications Co., Ltd. 2.2 Recommendation by Matrix Factorization 10

  12. （C）Recruit Communications Co., Ltd. 2.2 Recommendation by Matrix Factorization 11

  13. （C）Recruit Communications Co., Ltd. 2.3 Probabilistic Topic Models 12

  14. （C）Recruit Communications Co., Ltd. LDAの生成過程 13

  15. （C）Recruit Communications Co., Ltd. LDAの特徴 14

  16. （C）Recruit Communications Co., Ltd. 3. COLLABORATIVE TOPIC REGRESSION 15

  17. （C）Recruit Communications Co., Ltd. COLLABORATIVE TOPIC REGRESSION 16

  18. （C）Recruit Communications Co., Ltd. CTRの生成過程 17

  19. （C）Recruit Communications Co., Ltd. 3. COLLABORATIVE TOPIC REGRESSION 18

  20. （C）Recruit Communications Co., Ltd. CTRのモデルのRegressionたる所以 19

  21. （C）Recruit Communications Co., Ltd. 学習のさせ方 20

  22. （C）Recruit Communications Co., Ltd. 学習のさせ方 21

  23. （C）Recruit Communications Co., Ltd. 簡単な証明 by iPad手書き 22

  24. （C）Recruit Communications Co., Ltd. 学習のさせ方 23

  25. （C）Recruit Communications Co., Ltd. 予測 24

  26. （C）Recruit Communications Co., Ltd. 4. EMPIRICAL STUDY 25

  27. （C）Recruit Communications Co., Ltd. データの規模感 26

  28. （C）Recruit Communications Co., Ltd. 評価 27

  29. （C）Recruit Communications Co., Ltd. 結果 28

  30. （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)

  31. （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

  32. （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

  33. （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

  34. （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

  35. （C）Recruit Communications Co., Ltd. 5. CONCLUSIONS AND FUTURE WORK 34