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

Collaborative Topic Modeling for Recommending Scientific Articles

8a7e83d2e447783ab6d824f553429a09?s=47 Shinichi Takayanagi
May 30, 2016
Research
0
1.1k

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

8a7e83d2e447783ab6d824f553429a09?s=128

Shinichi Takayanagi

May 30, 2016
Tweet

More Decks by Shinichi Takayanagi

See All by Shinichi Takayanagi
8a7e83d2e447783ab6d824f553429a09?s=48 stakaya
0
1.2k
8a7e83d2e447783ab6d824f553429a09?s=48 stakaya
2
270
8a7e83d2e447783ab6d824f553429a09?s=48 stakaya
26
14k
8a7e83d2e447783ab6d824f553429a09?s=48 stakaya
1
500
8a7e83d2e447783ab6d824f553429a09?s=48 stakaya
3
200
8a7e83d2e447783ab6d824f553429a09?s=48 stakaya
4
3k
8a7e83d2e447783ab6d824f553429a09?s=48 stakaya
1
3.3k
8a7e83d2e447783ab6d824f553429a09?s=48 stakaya
4
2.1k
8a7e83d2e447783ab6d824f553429a09?s=48 stakaya
2
1.4k

Other Decks in Research

See All in Research
8ba32afb4b7d6967cb38b2dadf1ddd80?s=48 daigo0927
1
400
290a66d9796825297c2cdb438a372f6a?s=48 hashi0203
0
150
C612ab39597a17ba5948cae54d13f99f?s=48 mkimura
3
3.3k
E8dd0ab5a1ff487f6e6f93cfa0d4a348?s=48 udonda
1
440
0f2a473c07602f3dd53c5ed0de0c56b5?s=48 danielkatz
PRO
0
4.3k
C612ab39597a17ba5948cae54d13f99f?s=48 mkimura
2
660
F8b607a95506da4517e1dedd78023d04?s=48 jumon
0
520
31bf3df4bac9b584c89bc11b0c84e132?s=48 resnant
2
2.1k
067c2e9dfad1914df731f6f0d65d9890?s=48 kyoun
1
180
608638a6cfefbf0c2294a30e55634f6d?s=48 mihozono
2
1.6k
6027044e52fbcbc07f22d0235fc9da37?s=48 secondapunta
0
180
753eb5a167cf43a033413f08e63f3632?s=48 osx
0
400

Featured

See All Featured
0bce06bd06ee7a2c4f5500f25dcf7f18?s=48 paulrobertlloyd
74
1.6k
0fe2973fa8d27d96547e43322341183a?s=48 swwweet
209
7.1k
F57e2136eb9895eb95ff5dc8a1c02677?s=48 zakiwarfel
91
3.6k
E837d2996f52008ace055a08fbfff992?s=48 frogandcode
129
20k
8ec1383b240b5ba15ffb9743fceb3c0e?s=48 philnash
20
1k
E76911cbe088e5b850d966de3fc7435b?s=48 robhawkes
55
3k
8081b26e05bb4354f7d65ffc34cbbd67?s=48 chriscoyier
498
130k
06f8b41980eb4c577fa40c41d5030c19?s=48 keathley
25
910
C51b39fa3c79ccb99dcb8a076bc98287?s=48 brianwarren
87
5.3k
027d1ebf66cc039a0bd3b55eeadbe75d?s=48 sachag
448
36k
A16eb159db895e3b01d3dc95767ad595?s=48 jonyablonski
37
1.7k
812e1705ff0f8bc1bcf18587bde687d5?s=48 62gerente
593
210k

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