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
0
1.6k

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
Tweet

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
1.1k
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
1.5k
[NeurIPS 2023 論文読み会] Wasserstein Quantum Monte Carlo
Avatar for Shinichi Takayanagi stakaya
0
580
[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
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"
Avatar for Shinichi Takayanagi stakaya
2
660
【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
29
21k
論文読んだ「Simple and Deterministic Matrix Sketching」
Avatar for Shinichi Takayanagi stakaya
1
1.2k
Quick Introduction to Approximate Bayesian Computation (ABC) with R"
Avatar for Shinichi Takayanagi stakaya
3
380

Other Decks in Research

See All in Research
Community Driveプロジェクト（CDPJ）の中間報告
Avatar for SMARTふくしラボ smartfukushilab1
0
190
「車1割削減、渋滞半減、公共交通2倍」を 熊本から岡山へ＠RACDA設立30周年記念都市交通フォーラム2026
Avatar for Traffic Brain trafficbrain
1
680
2026年1月の生成AI領域の重要リリース＆トピック解説
Avatar for KAJI | 梶谷健人 kajikent
0
760
ウェブ・ソーシャルメディア論文読み会 第36回: The Stepwise Deception: Simulating the Evolution from True News to Fake News with LLM Agents (EMNLP, 2025)
Avatar for taichi_murayama hkefka385
0
200
FUSE-RSVLM: Feature Fusion Vision-Language Model for Remote Sensing
Avatar for SatAI.challenge satai
3
170
データサイエンティストの業務変化
Avatar for The Japan DataScientist Society datascientistsociety
PRO
0
270
Upgrading Multi-Agent Pathfinding for the Real World
Avatar for Keisuke Okumura | 奥村圭祐 kei18
0
350
空間音響処理における物理法則に基づく機械学習
Avatar for NII S. Koyama's Lab skoyamalab
0
230
ペットのかわいい瞬間を撮影する オートシャッターAIアプリへの スマートラベリングの適用
Avatar for Convergence Lab. mssmkmr
0
350
20年前に50代だった人たちの今
Avatar for marimari hysmrk
0
160
Collective Predictive Coding and World Models in LLMs: A System 0/1/2/3 Perspective on Hierarchical Physical AI (IEEE SII 2026 Plenary Talk)
Avatar for Tadahiro Taniguchi tanichu
1
280
第二言語習得研究における 明示的・暗示的知識の再検討：この分類は何に役に立つか，何に役に立たないか
Avatar for Yu Tamura tam07pb915
0
1.4k

Featured

See All Featured
Measuring Dark Social's Impact On Conversion and Attribution
Avatar for Stephen Akadiri stephenakadiri
1
140
Digital Projects Gone Horribly Wrong (And the UX Pros Who Still Save the Day) - Dean Schuster
Avatar for UX Y'all uxyall
0
590
How to Build an AI Search Optimization Roadmap - Criteria and Steps to Take #SEOIRL
Avatar for Aleyda Solis aleyda
1
1.9k
Improving Core Web Vitals using Speculation Rules API
Avatar for Sergey Chernyshev sergeychernyshev
21
1.4k
BBQ
Avatar for Matthew Crist matthewcrist
89
10k
Principles of Awesome APIs and How to Build Them.
Avatar for Keavy McMinn keavy
128
17k
Redefining SEO in the New Era of Traffic Generation
Avatar for Szymon Słowik szymonslowik
1
230
Why Our Code Smells
Avatar for Brandon Keepers bkeepers
PRO
340
58k
The Organizational Zoo: Understanding Human Behavior Agility Through Metaphoric Constructive Conversations (based on the works of Arthur Shelley, Ph.D)
Avatar for Dr. Kim W Petersen kimpetersen
PRO
0
260
Kristin Tynski - Automating Marketing Tasks With AI
Avatar for Tech SEO Connect techseoconnect
PRO
0
180
The Illustrated Children's Guide to Kubernetes
Avatar for Chris Short chrisshort
51
52k
Scaling GitHub
Avatar for Zach Holman holman
464
140k

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