Lock in $30 Savings on PRO—Offer Ends Soon! ⏳

Synapse: 文脈と時間経過に応じて推薦手法の選択を最適化するメタ推薦システム/smash...

monochromegane
September 16, 2021

Synapse: 文脈と時間経過に応じて推薦手法の選択を最適化するメタ推薦システム/smash21-synapse

monochromegane

September 16, 2021
Tweet

More Decks by monochromegane

Other Decks in Research

Transcript

  1. ࡾ୐ ༔հ1,2ɺ็ ߃ݑ3 1. Pepabo R&D Institute, GMO Pepabo, Inc.,

    2. ۝भେֶ େֶӃγεςϜ৘ใՊֶ෎ ৘ใ஌ೳ޻ֶઐ߈ 3. ۝भେֶ େֶӃγεςϜ৘ใՊֶݚڀӃ ৘ใ஌ೳ޻ֶ෦໳ 2021.09.16 SMASH21 Summer Symposium Synapse: จ຺ͱ࣌ؒܦաʹԠͯ͡ ਪનख๏ͷબ୒Λ࠷దԽ͢Δ ϝλਪનγεςϜ
  2. 7 എܠ • ޮՌతͳਪનख๏͸ঢ়گ΍࣌ؒͷܦաʹΑͬͯҟͳΔ • ͔͠͠ͳ͕Βɺ࣮؀ڥͰͷܧଓతͳਪનख๏ͷධՁʹ͸ػձଛࣦ͕൐͏ ӡ༻্ͷ՝୊ • ৘ใγεςϜʹ͓͚Δ৘ใաଟ໰୊Λղܾ͢ΔɺਪનγεςϜͷಋೖ •

    ͳΜΒ͔ͷํࡦʢ=ਪનख๏ʣʹج͖ͮଟ਺ͷબ୒ࢶ͔Βར༻ऀ͕ڵຯΛ࣋ ͭ΋ͷΛఏҊ͢ΔγεςϜ • ਺ଟ͘ͷਪનख๏͕ఏҊ͞Ε͍ͯΔ → ޮՌతͳʮਪનख๏ͷબఆʯ͕ॏཁ
  3. • ਪનख๏ͷ૬ରతͳੑೳͷ༏ྼΛॿ௕͢ΔཁҼͷଘࡏ 14 ਪનख๏ͷબఆʹ͓͚Δจ຺ͷߟྀͷॏཁੑ <>&LTUSBOE .BOE3JFEM +8IFOSFDPNNFOEFSTGBJMQSFEJDUJOHSFDPNNFOEFSGBJMVSFGPSBMHPSJUINTFMFDUJPOBOEDPNCJOBUJPO  1SPDFFEJOHTPGUIFTJYUI"$.DPOGFSFODFPO3FDPNNFOEFSTZTUFNT QQr

      <>#SBVOIPGFS . $PEJOB 7BOE3JDDJ '4XJUDIJOHIZCSJEGPSDPMETUBSUJOHDPOUFYUBXBSFSFDPNNFOEFSTZTUFNT 1SPDFFEJOHTPGUIF UI"$.$POGFSFODFPO3FDPNNFOEFSTZTUFNT QQr   <>"OEFSTPO " .BZTUSF - "OEFSTPO * .FISPUSB 3BOE-BMNBT ."MHPSJUINJDF⒎FDUTPOUIFEJWFSTJUZPGDPOTVNQUJPOPOTQPUJGZ  1SPDFFEJOHTPG5IF8FC$POGFSFODF QQr   ઌߦݚڀ ֓ཁ ਪનख๏ͷબఆʹؔ͢ΔཁҼͷ෼ੳɾར༻ <> ϋΠϒϦουਪનʹ͓͚Δਪનख๏ ͷબఆࣦഊͷݪҼΛ୳ͬͨݚڀ ར༻ऀ͝ͱʹਪનख๏Λ࢖͍෼͚Δ͜ͱͰੑೳ͕վળ͞ΕΔͱใࠂ͠ɼ ͦͷཁҼͷ෼ੳ͕ඞཁ <> ίʔϧυελʔτͷঢ়گʹݶఆͨ͠ ϋΠϒϦουਪનͷݚڀ ਪનख๏ͷબ୒ʹӨڹΛٴ΅͢ཁҼͱͯ͠ɼར༻ऀ΍঎඼ɼίϯςΩε τʹର͢ΔධՁͷ஝ੵ۩߹Λར༻ <> ԻָετϦʔϛϯάαʔϏεʹ͓͚ Δࢹௌ܏޲ͱਪનख๏ͷޮՌͷݚڀ ਪનख๏ͷબ୒ʹӨڹΛٴ΅͢ཁҼͱͯ͠ɼࢹௌ܏޲ͷଟ༷ੑʹண໨Ͱ ͖Δ͜ͱΛࣔͨ͠
  4. • ઌߦݚڀʹ͓͚Δจ຺ͱ࣌ؒͷܦաͷߟྀ 17 ଟ࿹όϯσΟοτ໰୊ͷղ๏Λ༻͍ͨਪનख๏ͷબఆ ઌߦݚڀ จ຺ ࣌ؒͷܦա <> º º

    <> <> ˚ º ࿹ͱͳΔਪનख๏͕จ຺Λѻ͏͕ਪનख๏ͷબఆʹ͸จ຺Λߟྀ͠ͳ͍ <> <> ˓ º <>͸ਪન࣌ͷӾཡதͷ঎඼ಛੑΛจ຺ͱͯ͠ར༻ʢ<>͸ఏࣔͳ͠ʣ ˎ͍ͣΕͷख๏΋࣌ؒͷܦաͷߟྀ͕े෼Ͱ͸ͳ͍ <>'FM ’DJP $; 1BJYB _P ,7 #BSDFMPT $"BOE1SFVY 1"NVMUJBSNFECBOEJUNPEFMTFMFDUJPOGPSDPMETUBSUVTFSSFDPNNFOEBUJPO 1SPDFFEJOHTPGUIFUI $POGFSFODFPO6TFS.PEFMJOH "EBQUBUJPOBOE1FSTPOBMJ[BUJPO QQr   <>#SPE FO # )BNNBS . /JMTTPO #+BOE1BSBTDIBLJT %&OTFNCMFSFDPNNFOEBUJPOTWJBUIPNQTPOTBNQMJOHBOFYQFSJNFOUBMTUVEZXJUIJOF DPNNFSDF SEJOUFSOBUJPOBMDPOGFSFODFPOJOUFMMJHFOUVTFSJOUFSGBDFT QQr   <>$BO _BNBSFT 3 3FEPOEP .BOE$BTUFMMT 1.VMUJBSNFESFDPNNFOEFSTZTUFNCBOEJUFOTFNCMFT 1SPDFFEJOHTPGUIFUI"$.$POGFSFODFPO 3FDPNNFOEFS4ZTUFNT QQr   <>4BOUBOB .3 .FMP -$ $BNBSHP ') #SBOE _BP # 4PBSFT " 0MJWFJSB 3.BOE$BFUBOP 4$POUFYUVBM.FUB#BOEJUGPS3FDPNNFOEFS4ZTUFNT 4FMFDUJPO 'PVSUFFOUI"$.$POGFSFODFPO3FDPNNFOEFS4ZTUFNT QQr   <>ࡾ୐༔հɼ็߃ݑ4ZOBQTFจ຺ʹԠͯ͡ܧଓతʹਪનख๏ͷબ୒Λ࠷దԽ͢ΔਪનγεςϜ ిࢠ৘ใ௨৴ֶձ࿦จࢽ% 7PM /P QQr  
  5. 22 Time-varying Thompson Sampling (TVTP) [15] Dynamic Context drift Modeling

    จ຺෇͖ɺ͔ͭɺඇఆৗͳใुͷมԽΛ ѻ͏ͨΊใुͷมಈΛ૊ΈࠐΜͩϞσϧ ͕༻͍ΒΕΔ <>;FOH $ 8BOH 2 .PLIUBSJ 4BOE-J 50OMJOFDPOUFYUBXBSFSFDPNNFOEBUJPOXJUIUJNFWBSZJOHNVMUJBSNFECBOEJU 1SPDFFEJOHTPGUIF OE"$.4*(,%%JOUFSOBUJPOBMDPOGFSFODFPO,OPXMFEHFEJTDPWFSZBOEEBUBNJOJOH QQr   <>'JH(SBQIJDBMNPEFMSFQSFTFOUBUJPOGPSCBOEJUQSPCMFN yk,t ⇠ N(xT t (cwk + ✓k ⌘k,t), 2 k ) <latexit sha1_base64="MOOU/xvogEgNwsUMW1ExG3Gil7E=">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</latexit> ίϯςΩετ ఆৗ߲ ඇఆৗ߲ ʢεέʔϧ߲ɺυϦϑτ߲ʣ ؍ଌޡࠩ
  6. 23 Time-varying Thompson Sampling (TVTP) [15] ঢ়ଶۭؒϞσϧ ใुϞσϧͷύϥϝʔλͷࣄޙ෼෍ͱυ Ϧϑτ߲ͷજࡏঢ়ଶͷஞ࣍ਪఆʹཻࢠ ϑΟϧλͱΧϧϚϯϑΟϧλ͕༻͍ΒΕΔ

    <>;FOH $ 8BOH 2 .PLIUBSJ 4BOE-J 50OMJOFDPOUFYUBXBSFSFDPNNFOEBUJPOXJUIUJNFWBSZJOHNVMUJBSNFECBOEJU 1SPDFFEJOHTPGUIF OE"$.4*(,%%JOUFSOBUJPOBMDPOGFSFODFPO,OPXMFEHFEJTDPWFSZBOEEBUBNJOJOH QQr   <>'JH(SBQIJDBMNPEFMSFQSFTFOUBUJPOGPSCBOEJUQSPCMFN yk,t ⇠ N(xT t (cwk + ✓k ⌘k,t), 2 k ) <latexit sha1_base64="MOOU/xvogEgNwsUMW1ExG3Gil7E=">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</latexit> ΧϧϚϯϑΟϧλ ཻࢠϑΟϧλ
  7. 24 Time-varying Thompson Sampling (TVTP) [15] ֬཰Ұக๏ ֤࿹ͰٻΊͨύϥϝʔλͷࣄޙ෼෍ʹै ͍αϯϓϦϯάͨ݁͠ՌΛ࿹ͷબఆʹ༻ ͍Δ͜ͱͰଟ࿹όϯσΟοτղ๏ͱ౷߹

    <>;FOH $ 8BOH 2 .PLIUBSJ 4BOE-J 50OMJOFDPOUFYUBXBSFSFDPNNFOEBUJPOXJUIUJNFWBSZJOHNVMUJBSNFECBOEJU 1SPDFFEJOHTPGUIF OE"$.4*(,%%JOUFSOBUJPOBMDPOGFSFODFPO,OPXMFEHFEJTDPWFSZBOEEBUBNJOJOH QQr   <>'JH(SBQIJDBMNPEFMSFQSFTFOUBUJPOGPSCBOEJUQSPCMFN yk,t ⇠ N(xT t (cwk + ✓k ⌘k,t), 2 k ) <latexit sha1_base64="MOOU/xvogEgNwsUMW1ExG3Gil7E=">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</latexit> a(t) = arg max j=1,K xT t ¯ wk,t 1 ¯ wk,t 1 ⇠ Nm(¯ µwk , ¯ ⌃wk ) <latexit sha1_base64="DJGY+opJHBQ8ZtUO2/R3rcYFv7Q=">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</latexit> ࣄޙ෼෍ ࣄޙ෼෍
  8. 25 TVTPͷ՝୊ a(t) = arg max j=1,K xT t ¯

    wk,t 1 ¯ wk,t 1 ⇠ Nm(¯ µwk , ¯ ⌃wk ) <latexit sha1_base64="DJGY+opJHBQ8ZtUO2/R3rcYFv7Q=">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</latexit> ¯ ⌃wk = 1 p2 p X i=1 2(i) k ⌃(i) wk , where p is number of particles <latexit sha1_base64="xTwhDLbTasGLMML2v9yvADUgJxI=">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</latexit> • ࢼߦճ਺ͷ૿Ճʹ൐͏ਪનख๏ͷબఆͷภΓ • ͋Δ࣌఺ͰධՁͷ௿͍ਪનख๏Λ୳ࡧ͢Δػձ͕ۃ୺ʹ௿Լ • ༗ޮੑ͕ٯస͢ΔΑ͏ͳঢ়گ΁ͷ௥ै͕஗ΕΔ ࢼߦճ਺ͷ૿Ճʹ൐͏ٸܹͳݮগʹΑΓ͋Δ ࣌఺ͷධՁʹج͍ͮͨબ୒ʹݻఆ͞ΕΔ
  9. 26 ఏҊํࣜ: Aggressive Exploration TVTP (AE-TVTP) a(t) = arg max

    j=1,K xT t ¯ wk,t 1 ¯ wk,t 1 ⇠ Nm(¯ µwk , ¯ ⌃wk ) <latexit sha1_base64="DJGY+opJHBQ8ZtUO2/R3rcYFv7Q=">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</latexit> ¯ ⌃wk = 1 p2 p X i=1 2(i) k ⌃(i) wk , where p is number of particles <latexit sha1_base64="xTwhDLbTasGLMML2v9yvADUgJxI=">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</latexit> • ࢼߦճ਺ͷ૿Ճʹ൐͏ਪનख๏ͷબఆͷภΓΛղফ • ͋Δ࣌఺ͰධՁͷ௿͍ਪનख๏Λੵۃతʹ୳ࡧ͢ΔػձΛઃ͚Δ • ࿹ͷ༗༻ੑ͕ٯస͢Δ؀ڥʹ͓͍ͯɺ௥ैੑͷ޲্ΛਤΔ ཻࢠͷฏۉ ཻ֤ࢠͰͷ৐ࢉͷΈ
  10. • จ຺ͱ࣌ؒͷܦաͷߟྀͷͦΕͧΕͷد༩౓Λ໌Β͔ʹ͢Δ4άϧʔϓͷγ ϛϡϨʔγϣϯΛ࣮ࢪ 30 ධՁํ๏(2/2) ࣌ؒͷܦա º ˓ จ຺ º

    "ىटͷ࠷ળͳਪનख๏ΛશظؒҰ؏ ͯ͠༻͍Δ $࣌఺͝ͱʹධՁͷߴ͍ਪનख๏Λόϯ σΟοτΛ༻͍ͯબఆ ˓ #จ຺͝ͱʹ࠷ળͳਪનख๏Λશظؒ Ұ؏ͯ͠༻͍Δ %จ຺͝ͱ࣌఺͝ͱʹධՁͷߴ͍ਪનख ๏ΛόϯσΟοτΛ༻͍ͯબఆ • จ຺ʹ͸ɺਪન࣌ʹӾཡதͷ঎඼ΧςΰϦΛ༻͍Δ • ଟ࿹όϯσΟοτղ๏͸ɺLTS(จ຺) ɺTVTP(จ຺/࣌ؒͷܦա) ɺAE-TVTP(จ຺/࣌ؒͷܦա)
  11. • Bάϧʔϓʢจ຺ʣ͸ظट࣌఺ʹ͓ ͍ͯAάϧʔϓͱจ຺ʹΑΔࠩҟ͕ ΄΅ͳ͍ͨΊ݁Ռ΋ࠩҟͳ͠ • Cάϧʔϓʢ࣌ؒͷܦաʣ͸ਪનख ๏ͷ༗ޮੑͷมԽʹ௥ैͨ͜͠ͱ Ͱվળ͕ݟΒΕΔ • Dάϧʔϓʢจ຺ͱ࣌ؒͷܦաʣ͸

    ঎඼ΧςΰϦ͝ͱͷมԽʹ௥ै͠ ͨ͜ͱͰߋͳΔվળ͕ݟΒΕΔ 31 ධՁ݁Ռ: AάϧʔϓΛج४ͱͨ͠ྦྷੵใुͷࠩͷൺֱ จ຺ͱ࣌ؒͷܦաͷߟྀͳΒͼʹɺٯస؀ڥͷ௥ ैੑΛߴΊͨఏҊํࣜʹΑͬͯ໿૿Ճ จ຺ͷΈ ࣌ؒͷܦաͷΈ
  12. 33 ߟ࡯ • ਪનख๏ͷ༗ޮੑ͕ٯస͢Δࠨྻ ʹ͓͍ͯ͸ఏҊख๏͕༗ޮ • ӈྻʹ͓͍ͯ͸ɺఏҊख๏ͷੵۃ తͳ୳ࡧʹىҼͯ͠ɺظؒதܧଓత ʹྦྷੵϦάϨοτ͕૿Ճ͢Δɻ3ׂ ఔ౓Ͱಉ༷ͷࣄ৅Λ֬ೝɻ

    ਪનख๏ͷ༗ޮੑʹٯస͕͋Δ঎඼ΧςΰϦ ࠨ ͱɺ ͳ͍঎඼ΧςΰϦ ӈ ʹ͓͚ΔྦྷੵϦάϨοτͷਪҠ ˎྦྷੵϦάϨοτ͸ਪનख๏ͷ͏ͪ࠷େͷظ଴஋ͱબ୒ͨ͠ਪનख ๏ͷظ଴஋ͷࠩΛظؒ·Ͱʹ߹ܭͨ͠΋ͷ มԽͷͳ͍ظؒʹ͓͍ͯ΋ػձଛࣦΛ ௿ݮ͢ΔదԠతͳ୳ࡧख๏ͷݚڀ΁