経営課題にAIを!ビジネスインフォマティクス研究会 (第15回)
JSAI Special Interest Group on Business Informatics (SIG-BI #15)

Graph Attention Networks Λ༻͍ͨ CtoC αʔϏεʹ͓͚ΔϢʔβఆணɾ཭୤༧ଌ
User Settlement/Churn Prediction on a CtoC service޻౻ߤௗւ ෆೋ෉Wataru KudoFujio Toriumi౦‫ژ‬େֶThe University of Tokyo

Recently, the form of transaction between consumers called CtoC (Consumer to Consumer) service has expanded
the market scale and has attracted great interest. In CtoC services, where the decrease in users directly causes
the decline of services, it is necessary to take measures to prevent existing users from leaving. In order to support
such measures, the problem of predicting user departure with high accuracy and interpretability has been actively
studied as ”User Churn Prediction”. The purpose of this research is to construct a high-performing and interpretable
framework to predict user settlement and churn that incorporates not only users’ own characteristics but also theeﬀects of contact between users. We applied Graph Neural Networks(GNNs), which have been attracting attention
in recent years, to the task of predicting user settlement and churn in the CtoC service.

1.

༻͠ɼϕʔεϥΠϯख๏ͱൺֱ࣮‫ݧ‬Λߦ͏ɽ·ͨɼϞσϧͷղऍੑΛ‫͠༻׆‬ɼఆணɾ཭୤Λ༧ଌ͢ΔࡍʹॏཁͰ͋ͬͨଞϢʔβͱͷ઀৮ͷछྨ΍઀৮૬खΛநग़Ͱ͖Δ͜ͱΛࣔ͠ɼϢʔβͷఆணΛଅ͢ࢪࡦ΁ͷ‫༻׆‬Մೳੑʹ͍ͭͯߟ࡯͢Δɽং࿦ۙ೥ͷΠϯλʔωοτͷී‫ʹٴ‬൐͍ɼe ίϚʔε (ిࢠ঎औҾ) ͕੝ΜʹߦΘΕΔΑ͏ʹͳͬͨɽͦΕʹ൐͍ɼফඅऀͷফඅߦಈ΋ଟ༷Խ͍ͯ͠ΔɽಛʹɼεϚʔτϑΥϯ΍ͦΕΛ༻͍ܾͨࡁγεςϜͷී‫ʹٴ‬൐͍ɼCtoC (Consumer to Consumer)ͱ‫ݺ‬͹ΕΔҰൠফඅऀؒͰͷऔҾ‫ܗ‬ଶ͕ࢢ৔‫ن‬໛Λ֦େ͠ɼେ͖ͳؔ৺ΛूΊ͍ͯΔɽ
CtoC ϓϥοτϑΥʔϜΛΛ‫ؚ‬Ή e ίϚʔεϓϥοτϑΥʔϜͰ͸ɼϢʔβ਺Λ૿Ճɾҡ࣋ͤͯ͞རӹΛ࠷େԽ͢ΔͨΊʹɼ཭୤ͦ͠͏ͳϢʔβʹΞϓϩʔν͢Δ͜ͱͰ཭୤Λ๷͙ࢪࡦ͕ੵ‫ۃ‬తʹߦΘΕ͍ͯΔɽֶज़քͰ͸ͦͷࢪࡦΛࢧԉ͢΂͘ɼߴ͍ਫ਼౓ͱղऍੑΛ΋ͬͯϢʔβͷ཭୤Λ༧ଌ͢Δ‫͕ڀݚ‬੝ΜʹߦΘΕ͍ͯΔɽCtoC αʔϏεΛར༻͢ΔϢʔβʹର͢Δఆணɾ཭୤༧ଌΛߟ͑Δͱ͖ɼ༧ଌର৅ͱ͢ΔϢʔβࣗ਎ͷಛ௃͚ͩͰͳ͘ɽϢʔβؒͷ઀৮ͷӨ‫ڹ‬Λৄࡉʹߟྀ͢Δ͜ͱ͸ਫ਼౓ͱղऍੑͷ྆໘ͰඇৗʹॏཁͰ͋Δͱߟ͑ΒΕΔɽಛʹɼ༧ଌର৅ͱͳΔϢʔβͷ 1) ઀৮૬खͷੑ࣭ɼ2) ઀৮ͷํ๏ɼͦͯ͠ 3) Ͳͷ઀৮͕ॏཁͰ͔͋ͬͨΛϞσϦϯά͢Δ͜ͱͰɼߴ͍ਫ਼౓ͱղऍੑΛ‫ظ‬଴Ͱ͖Δɽ͜ͷΑ͏ͳഎ‫͔ܠ‬Βɼຊ‫ڀݚ‬ͷ໨త͸ɼCtoC αʔϏεΛར༻͢ΔϢʔβͷߦಈ܏޲Λ໌Β͔ʹ͢Δͱͱ΋ʹɼϢʔβͷఆணɾ཭୤༧ଌʹ͍ͭͯɼ༧ଌର৅ͷϢʔβͷಛ௃͚ͩͰͳ͘Ϣʔβؒͷ઀৮͕΋ͨΒ͢Ө‫ڹ‬΋૊ΈࠐΜͩఆணɾ཭୤༧ଌϑϨʔϜϫʔΫΛߏங͠ɼߴਫ਼౓ͰղऍՄೳͳ༧ଌΛߦ͏͜ͱͰ͋Δɽ݁ՌͷղऍΛ௨ͯ͠ఆணɾ཭୤ʹؔΘΔϢʔβͷ܏޲Λ໌Β͔ʹ͠ɼϢʔβ਺Λ૿Ճͤ͞ΔͨΊͷࢪࡦ΁ͷ‫༻׆‬Λ໨ࢦ͢ɽຊ‫Ͱڀݚ‬͸ϋϯυϝΠυ࡞඼ΛചങͰ͖Δ CtoC ϓϥοτϑΥʔϜ minne∗1 ͷ࣮σʔλΛର৅ͱ͢ΔɽϢʔβͷಛ௃ͱ઀৮ͷӨ‫ڹ‬Λߟྀͨ͠ߴਫ਼౓ͰղऍՄೳͳఆணɾ཭୤༧ଌΛ࣮‫͢ݱ‬ΔͨΊɼۙ೥஫໨ΛूΊ͍ͯΔ Graph Neural Networks
Λϕʔεͱͨ͠༧ଌϞσϧΛ࣮σʔλ্ͷఆணɾ཭୤༧ଌʹద

2.

ؔ࿈‫ڀݚ‬2.1Ϣʔβ཭୤༧ଌαʔϏεͷϢʔβ਺Λ૿Ճ͍ͤͯͨ͘͞Ίͷઓུͱͯ͠ɼ৽ͨʹϢʔβΛ֫ಘ͢Δ͜ͱ͸‫ݱ‬ଘ͢ΔϢʔβΛҡ࣋͢Δ͜ͱΑΓ΋ίετ͕ߴ͘ͳΓ΍͍͢͜ͱ͕஌ΒΕ͍ͯΔ [Daly 02,Gillen 05]ɽͦͷͨΊɼ཭୤ͦ͠͏ͳϢʔβʹΞϓϩʔνͯ͠཭୤Λ๷͙ࢪࡦΛߨ͡Δ͜ͱ͸Ϗδωε্ͷ‫͔఺؍‬ΒඇৗʹॏཁͰ͋Δɽֶज़քʹ͓͍ͯ΋ɼ‫ݱ‬ଘϢʔβͷಛ௃͔Βߴਫ਼౓ͰղऍՄೳͳϢʔβ཭୤༧ଌϞσϧΛߏங͢Δ‫͕ڀݚ‬੝ΜʹߦΘΕ͖ͯͨɽຊ‫ؔʹڀݚ‬࿈͢Δྫͱͯ͠ɼϢʔβಉ࢜ͷ઀৮͕͋ΔαʔϏεΛର৅ͱͨ͠Ϣʔβ཭୤༧ଌʹ͍ͭͯ঺հ͢Δɽ
Kwon Β [Kwon 19] ͸ϏδωεαΠτʹର͢ΔϨϏϡʔϓϥοτϑΥʔϜΛར༻͢ΔϢʔβΛର৅ͱͯ͠ɼϢʔβɾ஍ҬͳͲͷಛ௃ྔʹՃ͑ɼϢʔβಉ࢜ͷϑϨϯυωοτϫʔΫʹ͓͚Δ࣍਺΍ۙ๣ͷ཭୤཰΋ಛ௃ͱͯ͠૊ΈࠐΈ཭୤༧ଌΛߦ͏ϞσϧΛఏҊ͠ɼϞσϧͷղऍʹΑΓ཭୤ͱؔ܎͕‫͍ڧ‬ಛ௃ʹ͍ͭͯߟ࡯ͨ͠ɽDrachen Β [Drachen 18] ͸ιʔγϟϧήʔϜͷϢʔβʹର͠ɼϢʔβͷϓϨΠɾߪೖཤྺͱϑϨϯυωοτϫʔΫʹ͓͚Δ࣍਺ɼϖʔδϥϯΫɼ֤छத৺ੑͳͲ͔Β཭୤Λ༧ଌ͢Δख๏ΛఏҊͨ͠ɽYang Β [Yang 18] ͸ιʔγϟϧίϛϡχέʔγϣϯΞϓϦͷ Snapchat Λର৅ʹɼ‫ݸ‬ʑͷϢʔβͷߦಈಛ௃ʹՃ͑ϑϨϯυؔ܎ʹ͓͚ΔΤΰωοτϫʔΫ΋ಛ௃ʹ૊ΈࠐΈɼϢʔβΫϥελϦϯάͱ཭୤༧ଌΛߦ͏ղऍՄೳͳϑϨʔϜϫʔΫΛఏҊͨ͠ɽ͜ΕΒ͸Ϣʔβಉ࢜ͷ઀৮Λߟྀ͍ͯ͠Δ͕ɼ͍ͣΕ΋ಛ௃ྔΤϯδχΞϦϯάʹΑΓωοτϫʔΫ্ͷϊʔυ͕࣋ͭτϙϩδΧϧͳੑ࣭ (࣍਺ɼϖʔδϥϯΫͳͲ) Λநग़ͨ͠Γɼۙ๣ͷ࣋ͭಛ௃Λ୯७ฏ‫ͨ͠ۉ‬Γ͢ΔΞϓϩʔνͰ͋ΔɽͦͷͨΊɼϢʔβ͕઀৮ͨ͠‫ݸ‬ʑͷ઀৮૬खͷੑ࣭΍઀৮ํ๏ʹ͍ͭͯ‫ݸ‬ผʹ૊ΈࠐΉ͜ͱɼ͓Αͼղऍ͢Δ͜ͱ͸Ͱ͖ͳ͍ɽ͕ͨͬͯ͠ɼຊ‫Ͱڀݚ‬ѻ͏Α͏ͳଟछଟ༷ͳϢʔβ͕ෳࡶͳ઀৮Λ࿈བྷઌ: ޻౻ߤɼ౦‫ژ‬େֶେֶӃγεςϜ૑੒ֶઐ߈ ௗւ ‫ ࣨ ڀ ݚ‬ɼ౦ ‫ ౎ ژ‬จ ‫ ࠜ ۠ ژ‬௡ 1-24-5ɼ09030938349ɼ
kudo@torilab.net∗1 https://minne.com

1

経営課題にAIを!ビジネスインフォマティクス研究会 (第15回)
JSAI Special Interest Group on Business Informatics (SIG-BI #15)

ߦ͍ͬͯΔσʔλʹద༻͢Δ৔߹ɼϞσϧͷද‫ྗݱ‬ɾղऍੑͷ૒ํʹվળͷ༨஍͕͋Δͱߟ͑ΒΕΔɽ

2.2·ͨຊ‫Ͱڀݚ‬͸ɼαʔϏεͷར༻ํ๏ (ߪೖ, ൢച) ʹैͬͯϢʔβΛ࡞Ոͱߪೖऀʹ෼ྨ͢Δɽͦ͜Ͱ·ͣɼର৅‫ؒظ‬தͷऔҾཤྺ͔Β࡞඼ͷൢചΛߦͬͨϢʔβ਺ (=࡞Ո਺)ɼ͓Αͼ࡞඼ͷߪೖΛߦͬͨϢʔβ਺ (=ߪೖऀ਺) Λࢉग़ͨ͠ͱ͜Ζɼ࡞Ո਺͕ 141,906 ਓɼߪೖऀ਺͕ 2,438,592 ਓͰ͋ͬͨɽ͜ͷ͜ͱ͔Βɼߪೖऀ͸࡞Ոͷ໿ 17.2 ഒଘࡏ͠ɼগ਺ͷ࡞Ոͱଟ਺ͷߪೖऀؒͰ࡞඼ͷऔҾ͕ߦΘΕ͍ͯΔ͜ͱ͕Θ͔ͬͨɽ·ͨɼ‫ؒظ‬தͷ݄͝ͱͷऔҾ਺ͷฏ‫ۉ‬͸໿ 410618 ճɼ݄͝ͱͷϨϏϡʔ਺ͷฏ‫ۉ‬͸໿ 155630 ճɼϦϓϥΠ਺ͷฏ‫ۉ‬͸໿ 81617 ճɼ݄͝ͱͷϑΥϩʔ૿Ճ਺ͷฏ‫ۉ‬͸໿ 550053 ճͰ͋ͬͨɽ͜ΕΒ͔Βɼ࡞ՈɾߪೖऀؒͰ࡞඼ͷऔҾɼϨϏϡʔɾϦϓϥΠɼϑΥϩʔͱ͍ͬͨ઀৮͕੝ΜʹߦΘΕ͍ͯΔ͜ͱ͕Θ͔ΔɽෳࡶωοτϫʔΫʹؔ͢Δख๏ຊ‫Ͱڀݚ‬ѻ͏σʔλ͸঎඼ͷఏ‫ߪͱऀڙ‬ೖऀͱ͍͏ҟͳΔੑ࣭Λ࣋ͭϢʔβ͕͓ΓɼͦΕΒ͕ߪങؔ܎΍ϑΥϩʔؔ܎ͱ͍ͬͨෳ਺ͷෳࡶͳؔ܎Ͱ݁͹Ε͍ͯΔωοτϫʔΫσʔλͰ͋ΔͱΈͳ͢͜ͱ͕Ͱ͖Δɽۙ೥ɼ͜ͷΑ͏ʹෳࡶͳωοτϫʔΫσʔλΛϞσϦϯά͢Δख๏ͷ‫͕ڀݚ‬੝ΜʹߦΘΕ͍ͯΔɽۙ೥ɼϊʔυ෼ྨ΍ϦϯΫ༧ଌ΁ͷΞϓϩʔνͱͯ͠େ͖ͳ஫໨ΛूΊ͍ͯΔख๏͕ Graph Neural Networks(GNNs) Ͱ͋ΔɽGNNs ͷ෼ྨ΍Ԡ༻ྫʹ͍ͭͯ͸ෳ਺ͷαʔϕΠ࿦จ͕‫ݟ‬ΒΕΔ [Zhou 18, Wu 19]ɽGNNs ͸ Network Embedding ͱ͸ҟͳΓɼ௨ৗ͸‫͋ࢣڭ‬Γֶशͱͯ͠ѻΘΕΔख๏Ͱ͋Δɽϊʔυಛ௃ྔͷม‫ʹ܎ؔ઀ྡͱ׵‬ΑΔू໿ͱ͍͏Ұ࿈ͷྲྀΕΛ‫ࢣڭ‬৘ใΛ༻͍ͯ End-to-End Ͱֶश͢Δ͜ͱʹΑΓɼωοτϫʔΫؔ࿈ͷछʑͷλεΫͰߴ͍ύϑΥʔϚϯεΛൃ‫[ ͖ͨͯ͠ش‬Kipf 16,Hamilton 17, Ying 18]ɽSchlichtkrull Β [Schlichtkrull 18] ͸Τοδͷछྨ͕ෳ਺͋ΔΑ͏ͳωοτϫʔΫʹରͯ͠ޮՌతͳ
Relational Graph Convolutional Networks(R-GCN) ΛఏҊ͠ɼϊʔυ෼ྨͱϦϯΫ༧ଌͰߴ͍ੑೳΛൃ‫͢ش‬Δ͜ͱΛࣔ͠
ͨɽVeličković Β [Veličković 17] ͕ఏҊͨ͠ Graph Attention
Networks (GAT) ͸ɼྡ઀ϊʔυͷू໿Λߦ͏ࡍͷॏΈ෇͚Λಉ࣌ʹֶशΛߦ͏͜ͱͰɼΤοδͷͳ͔Ͱ΋ຊ࣭తͳ΋ͷ͚ͩΛ࢖ͬͯϊʔυද‫ݱ‬ͷߋ৽Λߦ͏͜ͱ͕Ͱ͖Δɽ·ͨɼֶशʹΑͬͯऔಘ͞ΕͨॏΈΛ࢖ͬͯϞσϧͷղऍΛߦ͏͜ͱ͕Ͱ͖Δɽຊ‫Ͱڀݚ‬͸ɼR-GCN[Schlichtkrull 18] Λϕʔεͱͯ͠ϞσϧͱɼGAT[Veličković 17] Λϕʔεͱͨ͠ϞσϧΛϢʔβఆணɾ཭୤༧ଌʹద༻͠ɼͦͷଞͷϕʔεϥΠϯख๏ͱͷൺֱΛߦ͏ͱͱ΋ʹɼϞσϧͷղऍੑΛ‫ͨ͠༻׆‬Ϣʔβ܏޲ͷ෼ੳΛ
ߦ͏ɽ

3.

4.

Ϣʔβఆண/཭୤༧ଌ

4.1࣮‫֓ݧ‬ཁ

4.1.1໨త

ຊ‫Ͱڀݚ‬͸ɼ1) ߴ͍ਫ਼౓Ͱͷఆணɾ཭୤Ϣʔβͷ༧ଌͱɼ2)Ϣʔβͷఆண΍཭୤ͱؔ࿈͕‫͍ڧ‬ཁૉΛ໌Β͔ʹ͢Δ͜ͱͷ 2఺Λ໨తͱ͠ɼminne Ϣʔβͷఆணɾ཭୤Λ༧ଌ͢ΔϑϨʔϜϫʔΫΛ‫ݕ‬౼͢Δɽ

4.1.2

࣮‫ݧ‬ઃఆຊ࣮‫Ͱݧ‬͸ʮ2019 ೥ 3 ݄ʹग़඼ɾൢചɾඃϨϏϡʔɾඃϑΥϩʔΛߦͳͬͨ࡞ՈʯΛର৅ͱ͢Δɽ·ͨɼ܏޲ΛΑΓ໌֬ʹ೺Ѳ͢ΔͨΊɼ࡞ՈΛ৽‫࡞ن‬Ո (ొ࿥‫ؒظ‬: 2019 ೥ 1 ݄ʙ3 ݄)ͱ‫࡞ࢀݹ‬Ո (ొ࿥‫ؒظ‬: 2018 ೥ 1 ݄ʙ3 ݄) ʹ෼͚ͯͦΕͧΕͰֶशͱධՁΛߦ͏ɽಛ௃ྔͱͯ͠͸ɼ֤࡞Ոͷ 2019 ೥ 3 ݄ʹ͓͚Δൢച਺ɼग़඼਺ɼඃϑΥϩʔ਺ɼඃϨϏϡʔ਺ɼϦϓϥΠ਺ɼ࡞඼঺հจͷฏ‫ۉ‬௕ɼऔҾͷฏ‫ֹۚۉ‬ɼߪೖऀҰਓ౰ͨΓͷऔҾ਺ɼҰऔҾ͋ͨΓͷ࡞඼ͷ਺ɼϨϏϡʔͷฏ‫ۉ‬௕ɼϦϓϥΠͷฏ‫ۉ‬௕ɼϦϐʔτߪೖऀͷ਺ ∗3 ɼͷ߹‫ ܭ‬12 ‫ݸ‬ͷಛ௃ྔΛ࡞੒ͨ͠ɼ
໨తม਺͸ 2 ͭͷύλʔϯͰఆٛͨ͠ɽ1 ͭ͸ɼର৅ͷϢʔβ͕ఆண͢Δ͔ͦ͏Ͱͳ͍͔Λ൑ผ͢Δ͜ͱʹओ‫؟‬Λஔ͍ͨɼ“ఆண/ඇఆண”໨తม਺Ͱ͋Δɽͦͯ͠ɼ΋͏Ұͭ͸ɼର৅ͷϢʔβ͕཭୤͢Δ͔ͦ͏Ͱͳ͍͔Λ൑ผ͢Δ͜ͱʹओ‫؟‬Λ͓͍ͨɼ“཭୤/ඇ཭୤”໨తม਺Ͱ͋Δɽ“ఆண/ඇఆண”͸ɼʮ2019೥ 9 ݄Ҏ߱ʹग़඼·ͨ͸ൢചΛߦͬͨ/ߦΘͳ͔ͬͨʯͱఆٛ
͠ɼ“཭୤/ඇ཭୤”͸ʮ2019 ೥ 4 ݄Ҏ߱ʹग़඼·ͨ͸ൢചΛߦΘͳ͔ͬͨ/ߦͳͬͨʯͱఆٛͨ͠ɽ

CtoC αʔϏεσʔλ֓ཁຊ‫Ͱڀݚ‬͸ɼGMO ϖύϘ‫ࣜג‬ձࣾ ∗2 ΑΓɼಉ͕ࣾ 2012 ೥1 ݄͔ΒӡӦ͍ͯ͠ΔαʔϏεͰ͋Δ minne ʹ͍ͭͯͷσʔλఏ‫ڙ‬Λड͚ͨɽminne ͸ϋϯυϝΠυ࡞඼Λചങ͢Δ͜ͱ͕Ͱ͖Δ CtoCαʔϏεͰ͋ΔɽϢʔβؒͰऔҾ͞ΕΔ঎඼͸ϢʔβʹΑͬͯ૑࡞͞ΕͨϋϯυϝΠυ࡞඼ (ҎԼɼ࡞඼) Ͱ͋Δɽminne ͷϢʔβ͸ར༻ํ๏͔Β “࡞Ո”ɼ“ߪೖऀ”ͷ 2 छྨʹ෼ྨ͢Δ͜ͱ͕Ͱ͖Δɽ࡞Ոͱ͸ɼ࡞඼Λ૑࡞͠ग़඼͢ΔϢʔβΛࢦ͠ɼߪೖऀ͸ग़඼͞Εͨ࡞඼Λߪೖ͢ΔϢʔβΛࢦ͢ɽ·ͨɼ࡞Ոɾߪೖऀؒͷ઀৮ͱͯ͠ɼ࡞Ոͱߪೖऀؒͷ࡞඼ͷചങɼߪೖऀ͕‫ʹؾ‬ೖͬͨ࡞Ոͷొ࿥͢Δ “ϑΥϩʔ”‫ػ‬ೳ΍ߪೖऀ͕ߪೖͨ͠࡞඼ʹ͍ͭͯͷ‫ײ‬૝Λ౤ߘ͢Δ “ϨϏϡʔ”‫ػ‬ೳɼ౤ߘ͞ΕͨϨϏϡʔʹରͯ͠࡞Ո͕ฦ৴͢Δ “ϦϓϥΠ”‫ػ‬ೳͳͲ͕͋Δɼຊ‫͍༻ʹڀݚ‬Δσʔλ͸ 2018 ೥ 12 ݄ 1 ೔͔Β 2019 ೥ 12
݄ 1 ೔ͷؒ (ҎԼɼର৅‫ߦʹ )ؒظ‬ΘΕͨ࡞඼ͷചങऔҾཤྺɼର৅‫ʹؒظ‬औҾΛߦͳͬͨϢʔβͷશϑΥϩʔؔ܎ɼϨϏϡʔɾϦϓϥΠཤྺɼશग़඼ཤྺͰ͋Δɽͳ͓ɼminne ͷσʔλΛ‫ڀݚ‬໨తͰར༻͢Δࡍʹ͸ɼ࣮ࡍͷϢʔβͱඥ͔ͮͳ͍Α͏ʹϢʔβ ID ΛৼΓ௚ͯ͠ར༻͍ͯ͠Δɽ

4.2ख๏ຊ࣮‫Ͱݧ‬͸ GNNs Λϕʔεͱͨ͠ 2 ͭͷϞσϧ (R-GCN;
Relational Graph Convoutional Network[Schlichtkrull 18],GAT; Graph Attention Networks[Veličković 17]) Λఆணɾ཭୤༧ଌʹద༻͠ɼ3 ͭͷϕʔεϥΠϯख๏ (ܾఆ໦෼ੳ[Breiman 84]ɼϩδεςΟοΫճ‫ؼ‬෼ੳ (LR)[Cox 58]ɼଟ૚
χϡʔϥϧωοτϫʔΫ (ଟ૚ NN)) ͱͷൺֱΛߦ͏ɽ

4.3݁Ռ

ද 1 ʹɼ৽‫࡞ن‬Ոͷఆண༧ଌɼ৽‫࡞ن‬Ոͷ཭୤༧ଌɼ‫ࢀݹ‬࡞Ոͷఆண༧ଌɼ‫࡞ࢀݹ‬Ոͷ཭୤༧ଌʹ͍ͭͯͷ݁ՌΛࣔ͢ɽ৽‫࡞ن‬Ոʹର͢Δ෼ྨλεΫͰɼGNNs Λϕʔεͱͨ͠Ϟσϧ͸༗ޮͰ͋Δͱ͍͏͜ͱ͕໌Β͔ʹͳͬͨɽ∗2 https://pepabo.com∗32‫ؒظ‬தʹ 2 ճҎ্ͷऔҾ͕͋ͬͨߪೖऀͷϢχʔΫ਺

経営課題にAIを!ビジネスインフォマティクス研究会 (第15回)
JSAI Special Interest Group on Business Informatics (SIG-BI #15)

ද 1: ࡞Ոʹ͍ͭͯͷ෼ྨλεΫͷ AUCख๏৽‫ن‬ఆண ৽‫཭ن‬୤ ‫ࢀݹ‬ఆண ‫཭ࢀݹ‬୤
ܾఆ໦
0.684
0.701
0.7670.804
LR
0.666
0.696
0.778
0.804
ଟ૚ NN
0.675
0.700
0.784
0.809
R-GCN
0.721
0.729
0.778
0.808
GAT
0.722
0.728
0.783
0.811

ਤ 2: GAT ͷ৽‫࡞ن‬Ոఆண༧ଌϞσϧʹ͓͚Δ Self-loop Ҏ֎΁ͷॏཁ౓ͷճ‫ؼ‬໦Self-loop
0.154

フォロー

フォロー

フォロー

0.340

0.022

0.484

buyer_239687

ਤ 1: GAT ͷ৽‫࡞ن‬Ոఆண༧ଌϞσϧʹ͓͚Δ Self-loop ΁ͷॏཁ౓ͷճ‫ؼ‬໦

4.4

seller_8896

buyer_340699

buyer_400135

ਤ 3: ৽‫࡞ن‬Ոఆண༧ଌʹ͓͚Δ attention ஋ͷྫ 1: Selfloop ΑΓ΋ϑΥϩʔʹେ͖ͳ attention ஋͕ࢉग़͞Εͨ࡞Ոɽ઀಄ͷ “seller”͸࡞ՈΛද͠ɼ“buyer”͸ߪೖऀΛද͢ɽͳ͓ɼ“seller”͓Αͼ “buyer”ʹ෇‫͞ه‬Εͨ ID ͸‫Ͱڀݚ‬ͷར༻ʹࡍͯ͠ৼΓ௚ͨ͠΋ͷͰɼ࣮ࡍͷϢʔβͱ͸ରԠ͠ͳ͍ɽߟ࡯

4.3 અͷ݁Ռ͔Βɼಛʹ GNNs ϕʔεͷख๏Ͱͷੑೳվળ͕ஶ͔ͬͨ͠৽‫࡞ن‬Ոఆண༧ଌʹ͍ͭͯɼGAT ͷϞσϧͷղऍΛ௨ͯ͠ߟ࡯͢Δɽ
ਤ 1 ʹɼGAT ʹΑֶͬͯश͞Εͨ Self-loop∗4 ͷॏཁ౓ࢉग़ʹ͍ͭͯɼ࡞Ոͷಛ௃ྔ͔Βॏཁ౓Λઆ໌͢Δճ‫ؼ‬໦Λࣔ͢ɽ͜ΕΑΓɼ࡞Ո͕‫ͳൃ׆‬৔߹͸ Self-loop ͷॏཁ౓͕ߴ͘ͳ͍ͬͯΔ͜ͱ͕Θ͔Δɽਤ 2 ʹɼSelf-loop Ҏ֎ͷΤοδʹ͍ͭͯɼ࡞Ոɾߪೖऀͷಛ௃ྔ͓ΑͼΤοδͷλΠϓ͔Βॏཁ౓Λઆ໌͢Δճ‫ؼ‬໦Λࣔ͢ɽ͜ΕΑΓɼߪೖऀͷϑΥϩʔ਺΍ϨϏϡʔ਺͕ଟ͍৔߹ʹ‫͍ڧ‬ॏཁ౓͕ࢉग़͞Ε͍ͯΔ͜ͱ͕Θ͔ΔɽҎ্ͷॏཁ౓ͷղੳΛཁ໿͢ΔͱɼҎԼͷ͜ͱ͕‫͑ݴ‬Δɽͱͷ “ϑΥϩʔ”Τοδʹେ͖ͳ attention ͕ࢉग़͞Ε͍ͯΔ͜ͱ͕Θ͔Δɽ͜ͷ͜ͱ͔ΒɼGAT ͸ɼ͜ͷ࡞ՈͷఆணΛ༧ଌ͢Δʹ͋ͨͬͯ͸࡞Ոࣗ਎ͷಛ௃ྔΑΓ΋ϑΥϩʔͰ‫͕ͨͬܨ‬2 ਓͷߪೖऀͷಛ௃ྔΛॏࢹͨ͠ͱ‫͑ݴ‬Δɽͳ͓ɼ͜ͷ࡞Ոʹ෇༩͞Εͨ໨తม਺ϥϕϧ͸ “ఆண”Ͱ͋ΓɼGAT ͕ఆண֬཰0.926 ͱద੾ʹ༧ଌΛߦ͑ͨͷʹର͠ɼଟ૚χϡʔϥϧωοτϫʔΫ͸ 0.463 ͱ௿͍֬཰Λ༧ଌͯ͠͠·͍ͬͯΔɽ͜ͷ݁Ռ͔Βɼ͜ͷ࡞Ո͸࡞Ոࣗ਎ͷಛ௃ͱͯ͠͸཭୤ͦ͠͏͕ͩɼϑΥϩʔ͞Εͨߪೖऀͷಛ௃ྔ͔Βఆணͱ൑அͰ͖Δ࡞ՈͰ͋ͬͨͱߟ͑ΒΕΔɽ·ͨɼ΋͏Ұͭண໨͢΂͖఺ͱͯ͠ɼ઀৮͠
ͨ 3 ਓͷߪೖऀ͸શͯϑΥϩʔͰ‫͍͕ͯͬܨ‬Δ͕ɼࢉग़͞Εͨ
attention ஋ʹ͸͕ࠩ͋Δ͜ͱ͕‫͛ڍ‬ΒΕΔɽ͜ͷঢ়‫گ‬ԼͰ͸ɼ
ߴ͍ attention ஋͕ࢉग़͞Εͨ buyer 239687, buyer 400135
͕࡞Ոͷఆணʹ‫د‬༩ͨ͠ߪೖऀͰ͋Δͱ൑அͰ͖ΔɽҎ্ΑΓɼGAT ʹ͸࣮ӡ༻্Ͱ 2 ఺ͷར఺͕͋Δͱ‫͑ݴ‬ΔɽҰͭ͸ɼ༧ଌର৅ϢʔβͷߦಈཤྺΛू໿ͯ͠࡞ͬͨಛ௃ྔͷΈΛߟྀ͢ΔϞσϧͰ͸ัଊͰ͖ͳ͍‫ݸ‬ʑͷ઀৮૬खͷಛ௃·Ͱߟྀͨ͠ϞσϧͰ͋ΔͨΊߴ͍ੑೳͰ༧ଌΛߦ͑Δͱ͍͏఺Ͱ͋Δɽ΋͏Ұͭ͸ɼਤ 3 Ͱࣔͨ͠Α͏ʹ‫ݸ‬ʑͷϢʔβͱͷ઀৮୯ҐͰ༧ଌ΁ͷ‫د‬༩ΛղऍՄೳͰ͋Δͱ͍͏఺Ͱ͋ΔɽϢʔβͷఆணʹ‫د‬༩ͨ͠ϢʔβΛચ͍ग़ͨ͠ΓɼγϛϡϨʔγϣϯͷΞϓϩʔνͰ཭୤ͦ͠͏ͳϢʔβʹର͢ΔΞΠςϜ΍ଞϢʔβͷਪનΛߦ͏ͱ͖ʹࢀߟʹͨ͠Γ͢Δ͜ͱ΋Ͱ͖Δɽ• ৽‫࡞ن‬Ոఆண༧ଌͰ͸ɼ࡞Ոࣗ਎͕‫ͳൃ׆‬৔߹͸࡞Ոͷಛ௃ྔͰఆண͔Ͳ͏͔ओʹ൑அ͢Δɼ• ৽‫࡞ن‬Ոఆண༧ଌʹ͓͍ͯɼ࡞Ոࣗ਎͕‫Ͱൃ׆‬͸ͳ͍৔߹͸૬ରతʹ઀৮ͨ͠ߪೖऀͷಛ௃Λߟྀ͢Δൺ཰͕ߴ
·Δɽಛʹɼ઀৮ͨ͠ߪೖऀͷϑΥϩʔ΍ϨϏϡʔͷճ਺͕ଟ͍৔߹͸ॏཁ౓͕ߴ͘ͳΔ܏޲͕͋Δɽ઀৮૬खͷಛ௃Λద੾ʹଊ͑Δ͜ͱʹΑΔੑೳվળʹՃ͑ɼղऍՄೳੑ΋ GAT ͷར఺Ͱ͋ΔɽGAT Ͱֶश͞Εͨ attentionͷ஋Λ‫ݟ‬Δ͜ͱʹΑͬͯɼ‫ݸ‬ʑͷϢʔβʹ͍ͭͯ༧ଌλεΫʹॏཁͩͬͨ઀৮ͱ઀৮૬खΛ໌Β͔ʹ͢Δ͜ͱ͕Ͱ͖ΔɽҎԼͰ͸ྫͱͯ͠ɼ৽‫࡞ن‬Ոఆண༧ଌͷର৅ͱͨ͠ 2 ਓͷ࡞Ոʹ͍֤ͭͯ઀৮ʹࢉग़͞Εͨ attention ͷ஋ΛՄࢹԽ͢Δɽ
ਤ 3 ͸ɼಛ௃ྔ࡞੒‫ ʹؒظ‬3 ਓͷߪೖऀ͔ΒϑΥϩʔ͞Εͨ͋Δ࡞Ոʹର͢Δ attention ͷ஋Λࣔͨ͠΋ͷͰ͋Δɽ͜͜Ͱ͸Self-loop ΑΓ΋ 2 ਓͷߪೖऀ (buyer 239687, buyer 400135)∗4 Self-loop ͸ɼ࡞Ո-ߪೖऀؒͷωοτϫʔΫʹ͓͚Δࣗ‫ݾ‬ϧʔϓΛࣔ͢ɽSelf-loop ͷॏཁ౓͕ߴ͍͜ͱ͸ɼఆணɾ཭୤༧ଌϞσϧ͕ͦͷ࡞Ոࣗ਎ͷಛ௃ྔΛ΋ͱʹ൑அ͢Δൺ཰͕ߴ͔ͬͨ͜ͱΛҙຯ͢Δ3

経営課題にAIを!ビジネスインフォマティクス研究会 (第15回)
JSAI Special Interest Group on Business Informatics (SIG-BI #15)

5.

Mobile, Wearable and Ubiquitous Technologies, Vol. 3,No. 3, pp. 1–29 (2019)݁࿦ͱࠓ‫ޙ‬ͷల๬ຊ‫Ͱڀݚ‬͸ɼϋϯυϝΠυ࡞඼ΛചങͰ͖Δ CtoC ϓϥοτϑΥʔϜΛର৅ʹɼϢʔβͷ࣌‫ྻܥ‬తͳ܏޲ͷ෼ੳͱϢʔβఆணɾ཭୤༧ଌΛߦͬͨɽGraph Neural Networks Λϕʔεͱͨ͠ 2 ͭͷϞσϧΛ
Ϣʔβͷఆணɾ཭୤༧ଌʹద༻͠ɼϕʔεϥΠϯख๏ͱͷൺֱΛߦͬͨɽ࣮‫݁ݧ‬Ռ͓Αͼߏஙͨ͠ϞσϧͷղऍΛ௨ͯ͠ɼ༧ଌର৅ͱͳΔϢʔβͷಛ௃͚ͩͰ͸ͳ͘઀৮૬खͷಛ௃ʹ΋༗ӹͳ৘ใ͕‫·ؚ‬ΕΔͱ͖ɼGraph Neural Network Λϕʔεͱͨ͠ख๏͕༗ޮͰ͋Δ͜ͱ͕ࣔ͞Εͨɽ͞ΒʹɼGAT Λ༻͍Ε͹‫ݸ‬ʑͷఆணɾ཭୤༧ଌʹରͯ͠ॏཁͳҙຯ࣋ͬͨ઀৮΍઀৮૬खΛՄࢹԽͰ͖Δ͜ͱ΋ࣔͨ͠ɽࠓ‫ޙ‬ͷల๬ͱͯ͠͸ɽ༧ଌ݁Ռ͓ΑͼղऍΛར༻͠ɼ࣮ࡍʹ཭୤Λ๷ࢭ͢ΔࢪࡦΛߦ͍ɼͦͷޮՌΛ‫͢ূݕ‬Δ͜ͱ͕ओཁͳํ޲ੑͰ͋Δɽղੳͷ݁ՌॏཁͰ͋ͬͨ઀৮ʹ͍ͭͯͷ‫ػ‬ೳΛॆ࣮ͤ͞Δ͜ͱ΍ɼ‫ݸ‬ʑͷϢʔβʹରͯ͠ͷϦίϝϯσʔγϣϯʹ཭୤༧ଌϞσϧΛ‫͢༻׆‬Δ͜ͱͰύʔιφϥΠζ͞Εͨ཭୤๷ࢭࢪࡦΛߦ͍ɼ࣮ࡍͷޮՌΛධՁ͢Δ͜ͱ͕ඞཁͰ͋Δͱߟ͑ΒΕΔɽͦͷͨΊʹ͸ɼϞσϧͷղऍੑΛ༻͍ͨ෼ੳΛߴ౓Խ͠ɼఆணɾ཭୤ͱ઀৮ͷҼՌΛղ໌͢ΔΞϓϩʔν͕ඞཁͰ͋Δͱߟ͑ΒΕΔɽ

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