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WSDM2016読み会 Collaborative Denoising Auto-Encoders for Top-N Recommender Systems

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Collaborative Denoising Auto-Encoders for Top-N Recommender Systems

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WSDM2016読み会 Collaborative Denoising Auto-Encoders for Top-N Recommender Systems

  1. 1. 2016/O3/19 WSDM 20l 6 šiäššê Collaborative Denoising Auto-Encoders for Top-N Recommender Systems YaoWu, Christopher DuBois, Alice X. Zheng, Martin Ester 'J 7)I/ _ l 3 i 127-93 VX" llälälš šälitšlš
  2. 2. TOC ~ Ešiêœlüšlšbwł/ 'ä' 'J yäłiä ~ CDAE(Collaborative Denoising Auto-Encoder)
  3. 3. läšlšl? 4 Jl/ S' 'J `/ '7"0)Fšš$7“ál§U . l movies „rjj I 5|? ? i ; ą|: "‹ i Tuv ` “fluvj I 71m; l ŚZHV” I USBFS l„3„ „ „ „ „Bl 1-5 x ? rfvźbł-Tšllwtility Matrix) - (user, item, rating)0)5”-`-973"-:7i5hš - ilšśšlldxuser, item)l: '_3bTratingE$iFJJ3'š - ratinglä{1,. .,5}0D1›%êt`> {Qnœäšêäläuš gui = f9(u›
  4. 4. läiišwfšäšüüfà lšääšl 7 411/9 'J `/ '7"$>`f ~ Latent Factor Model (LFM) A LFN ~ T : um = fv (u, z) = v5.1 'vz l-*fć747lą7ż7äTfl3q7äiK3xñœćišläŃ7 h)l/ T`§łĘ: L/ %03|7Ś| lšblratinglzüšo
  5. 5. &Yšwfšäšüüf; lššäšlä74lbä 'J V'7`? >`Ż cont'd - Similarity Model (SM) . ziiii= fś`“i'uuz>= Z emsi» jEO'll. {i} 747-Lxi/ 0)ratinglá l-*fuœlüœ747-AĄœääilüšilziät ? šo šäilllat74FLxF䜚lššläłfšœimilarity)älšlämšo
  6. 6. lüišwfšäšüüfà lšäšlšl 7 »r ! L9 'J `/ '7"? >`Ż cont'd ~ Factorized Similarity Model (FSM) ; üm- = FĹŹM(U~'I`) = < Z _ ? luj 'pJ)TqI ETJHj0>74 FLxFäšlššläłfšœššxäüi74 FAäZnlZNLTOmŻYĆišDĹI ? šo % ZT: šlššlüñšüsê 2 90317W] lcášñääšo
  7. 7. Ešišwlüäüqü lšäšlšW 4' Jl/ Ś' 'J V7?? ? Confd ~ LFSM (LFM+FSM) T gui z IŚĹŹSAI : Ć Z yui ' pi + pu) qi jeO„{i} EUŁHUJLFM à: FSMêäääiêbüfcä @Do SVD++7QŻ ‹`: "o
  8. 8. älšlülšäššl - Point-wise objective function Z Źpoint (yuię "l" (u, i)EÓ' O'läêäiĘlJ7`-`-'›"+3r<7i7`- 4 7°user-item/ °7(5E§äi5JJU)5-"-5" láĹv= -l Ł 375) : mmm: '9“/ “"c‹J>%$Ę| JliEż›<`i t2736 - Pair-wise objective function Z fpaźr(yuij, gjuij) -ł- ÄŚÄÓ) yuij : ytm' _ yuja Z Qui _ @uj ('iL, ź,j)E73 'U' y 77L b 7°: 71' i-Aimositive) Łjmegative) l: 'D &Afratingœšäšflüñ
  9. 9. Łääšlšäššl ä EŃB䚌IUJEPTFH Lñpłäšäêäšśü: lilàłlçœlšššbiáö š „ 1 „ ' sqUare 'C333 [SLU/ am = §0/ - W - Log IOSSZ lLL(y, 3)) : log(1+ cxp(y - ~ Hinge IOSSI lHL(y. = max(0,1- y - - Cross entropy loss: l°”(; u.; ü) = -z/ logip) - (1 - ; u)10g(1 -p)- where p = 1/(1 + cxp(-gz])) gœłšäęłššäääiälšębbęäš
  10. 10. Collaborative Denoising Auto-Encoder DAE(Denoising Auto-Encoders) ? EFH LV( rating? ? ? šäll Input Hiddt-n Output ]. H_'('l` LH_'('I' I. n_'‹~r ! Lii ! Iul z-mnrąr; 74 ? ma W łœrating. negative item ' ' ' ' "F Ńu: -'/ ~= DAEœüm m: lŻ-l . łnlXÄlälOo . Im . I/. .n 7'( ? Aœratingœ-“Iššñü . ü.. i . I/. .i ! ius V ! Ius CDAElêtl-*ft 74 71x03 , _ _ ñäääêäšbruš 1-*Tu@une-hot-verrui* Bias Ż‹›‹l‹- U501* Nudi' decoder _tym : f (w, 'iz„ + b; ) h(. r) = l/ (l +‹'xp(-. r)) encoder zu : h (WIŚU +`lu
  11. 11. CDAEUD? ? LXTUDIŚĘšŚZêSGD, AdaGradTš/ Mlsüš U W 3313i„ Ś ZEMüiiIyi. ) lê(üuiüu)l+R (WW, V, b,b') ' ` " “:1 mamma à Rt): 2 (IIWIIŚ + | |W'| IŚ + IIVIIŚ + Ilbllš + Ill/ IiŚ)
  12. 12. Generalization of other models f(X) Ć NX) ; älääälšąliäš Ł CDAEĹŻŚŹE? ? 717l/ U3_i¶§'§'-łc`: äš Z ŁDVĆšš guz' Z f (Wi/ TZU + = WZTh (WTgi. + Vi. . + b) + bi š Wii/ T ( Z Śuiwj + Vu) . jEOii 11 T ; tjm = .FŚ_ŹSAI(”II. 'Ź) = ( Z yuj 'Pj +p„) qi je@ {}
  13. 13. 53%@ äšälclá 3 'DUbT-Śt "J I~ êlšñä Table 3: Dataset Statistics #users #items #dyads density(%) ML 69K 8.8K 5M 0.82 Netñix 37K l 1K 4.8M l. l8 Yelp 9.6K 7K 243K 0.36 'lêäššflüülc lit Averaged Precisionäštš N . . Z Z "C" AP@N z P_ g Inln{N, )cadoptcd| } 100151011 . . _ TN l . . , r _ | (-". '.1'‹-‹* nCzuluptvilk RIMĄHUN _ T. (v «uilupli-il
  14. 14. tt$5”ê%7'-")lz äišäcläüłTœ5üœłäi`iš57lbj` 'J XL Łttä - POPI šslzlüñbftl-"jxwäiüęśštń74 ? ĹAHETEŚĘ - ITEMCF: 74' ? AN-Xœłäššiühccardñêää) - IVIFI 7ŚE? :E5"`IL - BPR: llišäźšœfeedbackźFlätAfcšifœ-“J[l] - FISMI FSMUDŻEŻĘR] [l] Steffen Rendle, et al. 2009. [2] S. Kabbur. X. Ning. and G. Karypis. 2013.
  15. 15. tää 390371- 006 MAP@1 004 MAP@5 (m4 MAP@10 ':1 °-°5 0.03 0.03 I 0m o o2 o o2 i 0.03 ` ` 3 „m, 0.01 0.01 = 0.01 0.00 0.00 Figure 6: MAP scores with different N on the Yelp data set. M MAP@1 „g25 MAP@5 025 MAP@10 : l Z M 0.20 0.20 1 0.15 0,15 1 0.2 1 0.10 0.10 Ź 0.1 0.05 0.05 ätvhtämfüœšiábämüä POP ITEMCF MF 8PR PISM CDAE POP ITEMCF MF 8PR FISM CDAE Figure 7: MAP scores with different N on the MovieLens data set. 03 MAP@1 „o MAP@10 . 015 0.10 0.10 . 0.05 0.05 0.0 - 0.00 0.00 MAre-s 0.15 IIIIID Figure 8: MAP scores with different N on the Netfiix data set. POP ITEMCF MF 8PR FISM CDAE B-“xbfc
  16. 16. - cDAElácl-*ft 74' ? Aœáiššzäłäê Lr( mä - EJETaZOJšEaLFSM) iáCDAEœtäBIJo/ r-X Ł ana -ä? -?tvhtämfüœšäàbämüüäñbt

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