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

Collaborative Topic Modeling for Recommending S...

Sponsored · Your Podcast. Everywhere. Effortlessly. Share. Educate. Inspire. Entertain. You do you. We'll handle the rest. →
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
400

Other Decks in Research

See All in Research
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
160
[BlackHatAsia2026] Hidden Telemetry: Uncovering TraceLogging ETW Providers You're Not Using (Yet)
Avatar for Asuka Nakajima asuna_jp
1
580
(SIGQS17) Frasco-VS：フラグメントに基づく薬剤候補化合物選抜の量子アニーリングによる実現
Avatar for Keisuke Yanagisawa keisukeyanagisawa
PRO
0
160
衛星×エッジAI勉強会 衛星上におけるAI処理制約とそ取組について
Avatar for SatAI.challenge satai
4
600
LINEヤフー データサイエンス Meetup「三井物産コモディティ予測チャレンジ」の舞台裏-AlpacaTechパート
Avatar for tomo gamella
1
610
「AIとWhyを深堀る」をAIと深堀る
Avatar for なつ iflection
0
520
LLM の Attention 機構まとめ — 数式・計算量・メモリ
Avatar for プワー puwaer
8
2.3k
PGDM: Physically Guided Diffusion Model for L Downscaling
Avatar for SatAI.challenge satai
3
350
羽田新ルート運用6年の検証
Avatar for マン点 1manken
0
170
AY 2026 Guide to Academic Writing Using Generative AI - Workshop
Avatar for Kenji Saito ks91
PRO
0
130
NII S. Koyama's Lab Research Overview AY2026
Avatar for NII S. Koyama's Lab skoyamalab
0
410
2026年版中小企業白書・小規模企業白書の概要
Avatar for Takashi Ozeki ozekinote
0
110

Featured

See All Featured
A Modern Web Designer's Workflow
Avatar for Chris Coyier chriscoyier
698
190k
Discover your Explorer Soul
Avatar for Emna emna__ayadi
2
1.2k
How to Ace a Technical Interview
Avatar for Jacob Kaplan-Moss jacobian
281
24k
svc-hook: hooking system calls on ARM64 by binary rewriting
Avatar for Akira Moroo retrage
2
330
Collaborative Software Design: How to facilitate domain modelling decisions
Avatar for Kenny Baas-Schwegler baasie
1
260
Agile Leadership in an Agile Organization
Avatar for Dr. Kim W Petersen kimpetersen
PRO
0
190
Paper Plane (Part 1)
Avatar for Katie Cordina Lindsey katiecoart
PRO
0
9.6k
Producing Creativity
Avatar for Steve Smith orderedlist
PRO
348
40k
Bioeconomy Workshop: Dr. Julius Ecuru, Opportunities for a Bioeconomy in West Africa
Avatar for AKADEMIYA2063 akademiya2063
PRO
1
170
The Hidden Cost of Media on the Web [PixelPalooza 2025]
Avatar for Tammy Everts tammyeverts
2
340
It's Worth the Effort
Avatar for 3n 3n
188
29k
Navigating the Design Leadership Dip - Product Design Week Design Leaders+ Conference 2024
Avatar for Andy Polaine apolaine
1
370

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