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WDSM
WU&ESTER 2015
1
Thursday, May 14, 15
WHO
• バクフー株式会社 柏野 雄太
Thursday, May 14, 15
読んだ論文
• FLAME: Probabilistic Model Combining Aspect
Based Opinion Mining and Collaborative
Filtering
• Yao Wu / Matin Este...
動機
Thursday, May 14, 15
動機
• webの世界にはレビューが れているが,沢山ありす
ぎて全部読めない
• 同じ対象でも意見は各人多様のプリファレンスを持つ
ので容易に扱えない
• それでもレビューは意思決定に役に立つはずだ
Thursday, May 14, 15
先行研究
• Collaborative Filtering + LDA (science articles)
• Wang & Blei 2011
↵ ✓ z w
rv
u u
N K
J
I
v
rij ⇠ N(uT
i vj, cij1)...
先行研究
• Aspect-based Opinion Mining (hotel review)
• Wang et al. 2010
⌃
µ
↵
2
s wr
D
K
r : overall rating
s : aspect rating...
ASPECT?
• location, sleep quality, room, service, value,
cleanliness
Thursday, May 14, 15
提案モデル
Thursday, May 14, 15
提案モデル
i,a u
'd,a
✓d
rd
⌘0 ⌘u ⌘i
st
at
wn
a
a,r
W
T
D
A
A
IU
UI
R
A
p(wn|at, st, ↵) ⇠ Multi(↵at,st
)
↵a,s[j] =
exp( a[j] + ...
提案モデル
i,a u
'd,a
✓d
rd
⌘0 ⌘u ⌘i
st
at
wn
a
a,r
W
T
D
A
A
IU
UI
R
A
p(wn|at, st, ↵) ⇠ Multi(↵at,st
)
↵a,s[j] =
exp( a[j] + ...
生成プロセス
Thursday, May 14, 15
LIKELIFOOD
• MAP
• 変分ベイズ
{{⌘}, { }, , }
{↵, s}
Thursday, May 14, 15
実験と結果 データ
• TripAdvisor / Yelp
Thursday, May 14, 15
実験と結果 PERPLEXITY
• FLAMEがアウトパフォーム
TripAdvisor Yelp
LDA-A
LDA-AR
D-LDA
FLAME
1012.80 767.24
918.07 728.00
771.05 621.24
733...
実験と結果 PREDICTION
• TripAdvisorアスペクト評価予測
PMF LRR+PMF FLAME
RMSE 0.970 1.000 0.980
N/A 0.110 0.195
0.304 0.177 0.333
0.210 0...
実験と結果 質的評価
a a,r
Thursday, May 14, 15
将来の応用 アスペクト分布
• ユーザごとのレビュー推薦
• 推薦の理由付け
Thursday, May 14, 15
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FLAME: Probabilistic Model Combining Aspect Based Opinion Mining and Collaborative Filtering

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FLAME: Probabilistic Model Combining Aspect Based Opinion Mining and Collaborative Filtering

Yao Wu / Matin Ester

Published in: Software
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FLAME: Probabilistic Model Combining Aspect Based Opinion Mining and Collaborative Filtering

  1. 1. WDSM WU&ESTER 2015 1 Thursday, May 14, 15
  2. 2. WHO • バクフー株式会社 柏野 雄太 Thursday, May 14, 15
  3. 3. 読んだ論文 • FLAME: Probabilistic Model Combining Aspect Based Opinion Mining and Collaborative Filtering • Yao Wu / Matin Ester • collaborative filtering -> opinion mining • large geo DB -> spatial data mining • Review Mining using LDA like method Thursday, May 14, 15
  4. 4. 動機 Thursday, May 14, 15
  5. 5. 動機 • webの世界にはレビューが れているが,沢山ありす ぎて全部読めない • 同じ対象でも意見は各人多様のプリファレンスを持つ ので容易に扱えない • それでもレビューは意思決定に役に立つはずだ Thursday, May 14, 15
  6. 6. 先行研究 • Collaborative Filtering + LDA (science articles) • Wang & Blei 2011 ↵ ✓ z w rv u u N K J I v rij ⇠ N(uT i vj, cij1) ✓j ⇠ Dirichlet(↵) wjn ⇠ Mult( zjn ) rij 2 {0, 1} r : overall rating v : latent item distribution u : latent user preference Thursday, May 14, 15
  7. 7. 先行研究 • Aspect-based Opinion Mining (hotel review) • Wang et al. 2010 ⌃ µ ↵ 2 s wr D K r : overall rating s : aspect rating Thursday, May 14, 15
  8. 8. ASPECT? • location, sleep quality, room, service, value, cleanliness Thursday, May 14, 15
  9. 9. 提案モデル Thursday, May 14, 15
  10. 10. 提案モデル i,a u 'd,a ✓d rd ⌘0 ⌘u ⌘i st at wn a a,r W T D A A IU UI R A p(wn|at, st, ↵) ⇠ Multi(↵at,st ) ↵a,s[j] = exp( a[j] + a,s[j]) PV l=1 exp( a[l] + a,s[l]) rd ⇠ N( X a ✓d[a]E[rd, a], 2 r ) E[rd, a] = T u i,a Thursday, May 14, 15
  11. 11. 提案モデル i,a u 'd,a ✓d rd ⌘0 ⌘u ⌘i st at wn a a,r W T D A A IU UI R A p(wn|at, st, ↵) ⇠ Multi(↵at,st ) ↵a,s[j] = exp( a[j] + a,s[j]) PV l=1 exp( a[l] + a,s[l]) rd ⇠ N( X a ✓d[a]E[rd, a], 2 r ) E[rd, a] = T u i,a 潜在ユーザ選好 語-アスペクトの相関 語,アスペクト,評価の相関 文ごとのアスペクト 文ごとの評価 アスペクトごとの評価分布 評価分布 評価の出やすさの潜在変数 アスペクト分布 Thursday, May 14, 15
  12. 12. 生成プロセス Thursday, May 14, 15
  13. 13. LIKELIFOOD • MAP • 変分ベイズ {{⌘}, { }, , } {↵, s} Thursday, May 14, 15
  14. 14. 実験と結果 データ • TripAdvisor / Yelp Thursday, May 14, 15
  15. 15. 実験と結果 PERPLEXITY • FLAMEがアウトパフォーム TripAdvisor Yelp LDA-A LDA-AR D-LDA FLAME 1012.80 767.24 918.07 728.00 771.05 621.24 733.12 590.46 Thursday, May 14, 15
  16. 16. 実験と結果 PREDICTION • TripAdvisorアスペクト評価予測 PMF LRR+PMF FLAME RMSE 0.970 1.000 0.980 N/A 0.110 0.195 0.304 0.177 0.333 0.210 0.238 0.196 ⇢A ⇢I L0/1 Pearson correlation inside reviews ⇢A = 1 D AX d=1 ⇢(sd, s⇤ d) Pearson correlation pers.ed ranking ⇢I = 1 UA UX u=1 AX d=1 ⇢(sIu,a , s⇤ Iu,a ) Thursday, May 14, 15
  17. 17. 実験と結果 質的評価 a a,r Thursday, May 14, 15
  18. 18. 将来の応用 アスペクト分布 • ユーザごとのレビュー推薦 • 推薦の理由付け Thursday, May 14, 15

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