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
1.2k
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
1.7k
[NeurIPS 2023 論文読み会] Wasserstein Quantum Monte Carlo
Avatar for Shinichi Takayanagi stakaya
0
600
[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
670
【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
Model Discovery and Graph Simulation: A Lightweight Gateway to Chaos Engineering
Avatar for Anatoly A. Krasnovsky anatolykr
0
190
定数整数除算・剰余算最適化再考
Avatar for herumi herumi
1
120
業界横断 副業コンプライアンス調査 三者（副業者・本業先・発注者）におけるトラブル認知ギャップの構造分析
Avatar for fkske fkske
0
1.3k
適応的スパムフィルタのための軽量な類似メッセージカウンタ / jsai2026-adaptive-spam-filter
Avatar for monochromegane monochromegane
0
260
National high-resolution cropland classification of Japan with agricultural census information and multi-temporal multi-modality datasets
Avatar for SatAI.challenge satai
3
260
2026.01ウェビナー資料
Avatar for Elith elith
0
390
2026 東京科学大 情報通信系 研究室紹介 (大岡山)
Avatar for Tokyo Tech ICT Dept. icttitech
0
3.7k
機械学習で作った ポケモン対戦bot で 遊ぼう！
Avatar for Yusuke Fukasawa fufufukakaka
0
250
The mathematics of transformers
Avatar for Gabriel Peyré gpeyre
0
300
オーストリア流 都市の公共交通サービス水準評価@公共交通オープンデータ最前線2026
Avatar for Traffic Brain trafficbrain
0
180
Unified Audio Source Separation (Defense Slides)
Avatar for Kohei Saijo kohei_1979
1
610
「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
1.1k

Featured

See All Featured
Unlocking the hidden potential of vector embeddings in international SEO
Avatar for Frank van Dijk frankvandijk
0
830
Optimizing for Happiness
Avatar for Tom Preston-Werner mojombo
378
71k
Everyday Curiosity
Avatar for Cassini Nazir cassininazir
0
220
Jamie Indigo - Trashchat’s Guide to Black Boxes: Technical SEO Tactics for LLMs
Avatar for Tech SEO Connect techseoconnect
PRO
0
160
"I'm Feeling Lucky" - Building Great Search Experiences for Today's Users (#IAC19)
Avatar for Dan Newman danielanewman
231
23k
We Analyzed 250 Million AI Search Results: Here's What I Found
Avatar for Josh Blyskal joshbly
1
1.3k
Have SEOs Ruined the Internet? - User Awareness of SEO in 2025
Avatar for Akash Hashmi akashhashmi
0
360
The Invisible Side of Design
Avatar for Vitaly Friedman smashingmag
302
52k
Writing Fast Ruby
Avatar for Erik Berlin sferik
630
63k
Beyond borders and beyond the search box: How to win the global "messy middle" with AI-driven SEO
Avatar for David Carrasco davidcarrasco
3
150
How to audit for AI Accessibility on your Front & Back End
Avatar for davetheseo davetheseo
0
400
The Impact of AI in SEO - AI Overviews June 2024 Edition
Avatar for Aleyda Solis aleyda
5
1.1k

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