会社訪問アプリ「Wantedly Visit」のデータで見る相互推薦システム / deim2022-rrs-wantedly-visit

2022年3月1日 DEIM2022 (https://event.dbsj.org/deim2022/) における技術報告の資料です。

[G33]知識グラフ・オントロジ活用-②　3月1日 13:00 ～ 15:05
https://cms.dbsj.org/deim2022/program/?oral#/G33

会社訪問アプリ「Wantedly Visit」の実データを用いて、相互推薦システムの既存手法の評価実験を行った上で、出てきた課題に対する改善手法を提案して評価実験を行いその有用性を検証しました。昨年の DEIM2021 における技術報告の続きとなる内容です。

性質の違いから発生する個人ユーザー間、企業ユーザー間の嗜好データの傾向の違いを、従来の推薦システムでもよく利用される Biased Matrix Factorization を単方向の嗜好である Preference Score の予測に利用したり、Preference Score を入力として最終的な正解（マッチ）を予測する回帰問題として重みを学習したモデルを Aggregation Function として利用する手法を用いた評価実験について紹介しました。

Transcript

1. ©2022 Wantedly, Inc. ձࣾ๚໰ΞϓϦʮWantedly Visitʯͷ σʔλͰݟΔ૬ޓਪનγεςϜ [G33-5] DEIM2022 [G33]஌ࣝάϥϑɾΦϯτϩδ׆༻-ᶄʲٕज़ใࠂʳ 1.March.2022 - দଜ༏໵ʢ΢ΥϯςουϦʔגࣜձࣾʣ @yu-ya4
2. ©2022 Wantedly, Inc. 1. ͸͡Ίʹ • ࣗݾ঺հɺձࣾͱϓϩμΫτͷ঺հ • ϓϩμΫτʹ͓͚ΔσʔλαΠΤϯεͷऔΓ૊Έࣄྫ • ΞΧσϛΞʹ͓͚Δ׆ಈ 2. ૬ޓਪનγεςϜͱ͸ • ૬ޓਪનγεςϜͷ֓ཁɾಛ௃ • طଘख๏ͷ঺հ 3. ձࣾ๚໰ΞϓϦʮWantedly VisitʯͷσʔλͰݟΔ૬ޓਪનγεςϜ • Wantedly Visit ͷσʔλʹରͯ͠طଘख๏Λద༻࣮ͨ͠ݧɾߟ࡯ • վྑख๏ͷఏҊɾ࣮ݧɾߟ࡯ ձࣾ๚໰ΞϓϦʮWantedly VisitʯͷσʔλͰݟΔ૬ޓਪનγεςϜ
3. ©2022 Wantedly, Inc. 1. ͸͡Ίʹ • ࣗݾ঺հɺձࣾͱϓϩμΫτͷ঺հ • ϓϩμΫτʹ͓͚ΔσʔλαΠΤϯεͷऔΓ૊Έࣄྫ • ΞΧσϛΞʹ͓͚Δ׆ಈ 2. ૬ޓਪનγεςϜͱ͸ • ૬ޓਪનγεςϜͷ֓ཁɾಛ௃ • طଘख๏ͷ঺հ 3. ձࣾ๚໰ΞϓϦʮWantedly VisitʯͷσʔλͰݟΔ૬ޓਪનγεςϜ • Wantedly Visit ͷσʔλʹରͯ͠طଘख๏Λద༻࣮ͨ͠ݧɾߟ࡯ • վྑख๏ͷఏҊɾ࣮ݧɾߟ࡯ ձࣾ๚໰ΞϓϦʮWantedly VisitʯͷσʔλͰݟΔ૬ޓਪનγεςϜ
4. ©2022 Wantedly, Inc. ✓ দଜ ༏໵ʢYuya Matsumuraʣ ✓ 2018೥3݄ ژ౎େֶେֶӃ৘ใֶݚڀՊ म࢜՝ఔमྃ ✓ ΢ΥϯςουϦʔגࣜձࣾ Recommendation νʔϜϦʔυ ✓ Wantedly Visit ʹ͓͚ΔਪનγεςϜͷ։ൃͳͲΛ୲౰ @yu-ya4 @yu__ya4 ࣗݾ঺հ
5. ©2022 Wantedly, Inc. ձࣾ঺հ "γΰτͰίίϩΦυϧͻͱΛ;΍͢" "CREATE A WORLD WHERE WORK DRIVES PASSION"
6. ©2022 Wantedly, Inc. ձࣾ๚໰ΞϓϦ Wantedly Visit
7. ©2022 Wantedly, Inc. ϓϩμΫτʹ͓͚ΔσʔλαΠΤϯεͷऔΓ૊Έࣄྫ
8. ©2022 Wantedly, Inc. Ϣʔβ͝ͱʹ࠷దԽ͞Εͨίϯςϯπͷਪન Ϣʔβʹద੾ͳίϯςϯπΛఏڙͯ͠ ཧ૝ͷϚονϯάΛ࣮ݱ͢ΔͨΊͷ༷ʑͳਪનγεςϜ ࣗવݴޠॲཧ΍ػցֶशͳͲ༷ʑͳٕज़Λ׆༻
9. ©2022 Wantedly, Inc. ϢʔβͷʮڵຯʯʹΑΔϚονϯά Ϣʔβ͕બ୒ͨ͠ʮڵຯʯʹجͮ͘ืूͱͷϚονϯά ʮ৬छʯͳͲʹΑΔϑΟϧλϦϯάͷΈͰ͸ݟ͚ͭΒΕͳ ͍ΑΓϢʔβͷᅂ޷ʹ߹ͬͨืूΛਪન͢Δ
10. ©2022 Wantedly, Inc. ΞΧσϛΞʹ͓͚Δ׆ಈ
11. ©2022 Wantedly, Inc. ࠃࡍֶձ΁ͷௌߨࢀՃ / ࿦จಡΈձΠϕϯτͷاըӡӦ
12. ©2022 Wantedly, Inc. ֶձซઃίϯϖςΟγϣϯͰͷೖ৆ɾ࿦จ౤ߘɾൃද RecSys’20 WSDM’21 SIGIR’21
13. ©2022 Wantedly, Inc. DEIM ΁ͷڠࢍ / ٕज़ใࠂ https://event.dbsj.org/deim2022/
14. ©2022 Wantedly, Inc. 1. ͸͡Ίʹ • ࣗݾ঺հɺձࣾͱϓϩμΫτͷ঺հ • ϓϩμΫτʹ͓͚ΔσʔλαΠΤϯεͷऔΓ૊Έࣄྫ • ΞΧσϛΞʹ͓͚Δ׆ಈ 2. ૬ޓਪનγεςϜͱ͸ • ૬ޓਪનγεςϜͷ֓ཁɾಛ௃ • طଘख๏ͷ঺հ 3. ձࣾ๚໰ΞϓϦʮWantedly VisitʯͷσʔλͰݟΔ૬ޓਪનγεςϜ • Wantedly Visit ͷσʔλʹରͯ͠طଘख๏Λద༻࣮ͨ͠ݧɾߟ࡯ • վྑख๏ͷఏҊɾ࣮ݧɾߟ࡯ ձࣾ๚໰ΞϓϦʮWantedly VisitʯͷσʔλͰݟΔ૬ޓਪનγεςϜ
15. ©2022 Wantedly, Inc. User Item ैདྷͷҰൠతͳਪનγεςϜ ૬ޓਪનγεςϜ User(Female) User(Male) User(Job Seeker) User(Recruiter/Company) ex. Amazon, Netflix ex. Tinder, Pairs ex. Wantedly, LinkedIn ૬ޓਪનγεςϜʢReciprocal Recommender Systemsʣͱ͸ʁ ʮαʔϏε಺ͷϢʔβΛޓ͍ʹਪન͢ΔγεςϜʯ ਪન ਪન ਪન
16. ©2022 Wantedly, Inc. ૬ޓਪનγεςϜʹ͓͚Δਪનͷ"੒ޭ" ͓ޓ͍ͷᅂ޷͕Ұகͯ͠ॳΊͯਪન͕"੒ޭ"ͨ͜͠ͱʹͳΔ User Item ैདྷͷҰൠతͳਪનγεςϜ ૬ޓਪનγεςϜ User User ߪೖ ⭕ ⭕ Like Nope User User Like Like
17. ©2022 Wantedly, Inc. User A to User B Preference Score User B to User A Preference Score Reciproca l Preference Score Aggregatio n 1. γεςϜ಺ͷϢʔβ͸ A ͱ B ͷ̎ͭͷάϧʔϓʹ෼͔Ε͓ͯΓɺҟͳΔάϧʔϓͷϢʔβ͕ޓ͍ਪન͞ΕΔ ΋ͷͱ͢Δɻʢe.g. σʔςΟϯάαʔϏεʹ͓͚ΔஉঁɺٻਓαʔϏεʹ͓͚Δٻ৬ऀͱاۀʣ 2. ୯ํ޲ͷᅂ޷ͷେ͖͞Λද͢ Preference Score ΛɺA ͔Β B ΁ͷϢʔβٴͼ B ͔Β A ͷϢʔβͷͦΕͧΕ ʹ͍ͭͯܭࢉ 3. Aggregation Function Λར༻ͯ͠ɺ̎ͭͷ Preference Score Λ૊Έ߹Θͤͯ૒ํ޲ͷᅂ޷ͷେ͖͞Λද͢ Reciprocal Preference Score Λܭࢉ ૬ޓਪનγεςϜʹ͓͚Δᅂ޷ͷ༧ଌ
18. ©2022 Wantedly, Inc. ✓ [RECON] (Pizzato 2010) ‣ ϢʔβͷϓϩϑΟʔϧ৘ใΛར༻ͨ͠ίϯςϯπϕʔεϑΟϧλϦϯάͰ Preference Score Λࢉग़ ‣ Aggregation Function ʹ͸ௐ࿨ฏۉΛར༻ ✓ [RCF] (Xia 2015 ) ‣ ߦಈཤྺʹجͮ͘ϝϞϦϕʔεͷϢʔβϕʔεڠௐϑΟϧλϦϯάʢk-ۙ๣ʣͰ Preference Score Λࢉग़ ✓ [LFRR](Neve 2019) ‣ ߦಈཤྺʹج͖ͮ࡞੒ͨ͠ User-User ߦྻʹ Matrix Factorization Λద༻ͯ͠ Preference Score Λࢉग़ ‣ Aggregation Function ʹ͍ͭͯɼௐ࿨ฏۉҎ֎ͷؔ਺ʹ͍ͭͯ΋ൺֱ࣮ݧ ✓ [ImRec](Neve 2020 ) ‣ ϢʔβͷϓϩϑΟʔϧը૾Λར༻ͨ͠ίϯςϯπϕʔεϑΟϧλϦϯάͰ Preference Score Λࢉग़ ૬ޓਪનγεςϜʹ͓͚Δᅂ޷ͷ༧ଌͷطଘख๏
19. ©2022 Wantedly, Inc. 1. ͸͡Ίʹ • ࣗݾ঺հɺձࣾͱϓϩμΫτͷ঺հ • ϓϩμΫτʹ͓͚ΔσʔλαΠΤϯεͷऔΓ૊Έࣄྫ • ΞΧσϛΞʹ͓͚Δ׆ಈ 2. ૬ޓਪનγεςϜͱ͸ • ૬ޓਪનγεςϜͷ֓ཁɾಛ௃ • طଘख๏ͷ঺հ 3. ձࣾ๚໰ΞϓϦʮWantedly VisitʯͷσʔλͰݟΔ૬ޓਪનγεςϜ • Wantedly Visit ͷσʔλʹରͯ͠طଘख๏Λద༻࣮ͨ͠ݧɾߟ࡯ • վྑख๏ͷఏҊɾ࣮ݧɾߟ࡯ ձࣾ๚໰ΞϓϦʮWantedly VisitʯͷσʔλͰݟΔ૬ޓਪનγεςϜ
20. ©2022 Wantedly, Inc. • δϣϒϚονϯάαʔϏεʹ͓͚ΔਪનγεςϜͷॏཁੑ • ॏཁͳҰํͰݚڀ͕·ͩଟ͍Θ͚Ͱͳ͘ɺ͞ΒͳΔվળͷ༨஍ • Wantedly ʹे෼ͳσʔλ͕஝ੵ͞Ε͖ͯͨ Ϟνϕʔγϣϯ Why
21. ©2022 Wantedly, Inc. Ϟνϕʔγϣϯ δϣϒϚονϯάαʔϏεʹ͓͚ΔਪનγεςϜͷॏཁੑ • ʮಇ͘ʯΛऔΓר͘؀ڥ͸೔ʑมԽɺෳࡶԽ • ಇ͖ํͷଟ༷Խʢe.g. ϦϞʔτϫʔΫɺ෭ۀɾϑϦʔϥϯεɺ৽ଔҰׅ࠾༻ͷഇࢭʣ • ৬छͷଟ༷Խʢe.g. σʔλαΠΤϯςΟετɾPdMʣ • ͦ΋ͦ΋ਓ͸ࣗ෼͕ຊ౰ʹ΍Γ͍ͨ͜ͱΛࣗ෼Ͱ෼͔͍ͬͯͳ͍ →ਪનγεςϜʹΑΔҙࢥܾఆͷิॿͷॏཁੑ͕ߴ·Δ
22. ©2022 Wantedly, Inc. Ϟνϕʔγϣϯ ॏཁͳҰํͰݚڀ͕·ͩଟ͍Θ͚Ͱͳ͘ɺ͞ΒͳΔվળͷ༨஍ • ηϯγςΟϒͳྖҬͳͷͰσʔλ΍։ൃɾݚڀ੒ՌΛެ։͢Δ͜ͱ͕ࠔ೉ • ૬ޓਪનγεςϜͷطଘݚڀ΋σʔςΟϯάαʔϏεʹ͍ͭͯͷ΋ͷ͕ଟ͍ • طଘݚڀͷධՁ࣮ݧͰར༻͞ΕΔख๏͸·ͩ·ͩൃల్্ • ҰൠతͳਪનγεςϜʹͯ׆༻͞ΕΔख๏Λͦͷ··ྲྀ༻Ͱ͖Δ෦෼΋ • ૬ޓਪનγεςϜͳΒͰ͸ͷख๏΋͜Ε͔ΒͲΜͲΜग़ͯ͘ΔͰ͋Ζ͏
23. ©2022 Wantedly, Inc. Ϟνϕʔγϣϯ Wantedly ʹे෼ͳσʔλ͕஝ੵ͞Ε͖ͯͨ • ొ࿥اۀϢʔβ3.2ສࣾҎ্ɺݸਓϢʔβ 330ສਓҎ্ • ΧδϡΞϧͳձࣾ๚໰ΞϓϦͰ͋Δ͔Βͦ͜ɺҰൠతͳస৬αʔϏεΑΓ΋େ ྔͷଟ༷ͳσʔλ͕ू·Δ • స৬׆ಈ࣌ʹʮબߟʯͷલʹͱΓ͋͑ͣͨ͘͞Μͷاۀͱ࿩͢ • ඇస৬࣌ʹ΋৘ใऩू໨తͳͲͰͱΓ͋͑ͣاۀͱ࿩ͯ͠ΈΔ • اۀͱ࿩͢ɺҎ֎ͷ໨తʢe.g. ϓϩϑΟʔϧɾϒϩάػೳɾϛʔτΞοϓػ ೳʣͰ΋ීஈ͔Βར༻͞ΕΔ
24. ©2022 Wantedly, Inc. ࣮ݧ֓ཁ Wantedly Visit ʹ͓͚ΔϢʔβͱاۀͷ Matching Λ༧ଌ Company User Ԡื ϝοηʔδฦ৴ Company User εΧ΢τૹ৴ ϝοηʔδฦ৴ Company User Ԡื ӾཡͷΈ Company User εΧ΢τૹ৴ ӾཡͷΈ • Ϣʔβ͕Ԡื or اۀ͕εΧ΢τૹ৴ͨ͠ࡍʹ૬ख͕ϝοηʔδΛฦ৴͢Ε͹ Match (positive ) • Ԡื or εΧ΢τૹ৴Λ૬ख͕Ӿཡ্ͨ͠ͰϝοηʔδΛฦ৴͠ͳ͚Ε͹ Not Match (negative) Match Not Match How
25. ©2022 Wantedly, Inc. ᅂ޷σʔλ/ධՁ஋ • Ϣʔβͷᅂ޷σʔλ • اۀ΁ͷԠื • اۀ͔ΒͷεΧ΢τૹ৴ʹର͢Δϝοηʔδฦ৴ • اۀͷᅂ޷σʔλ • Ϣʔβ΁ͷεΧ΢τૹ৴ • Ϣʔβ͔ΒͷԠืʹର͢Δϝοηʔδฦ৴ • ධՁ஋: Ϣʔβͱاۀͷ૊ʹରͯ͠༩͑ΒΕΔ (boolean ) • Ϣʔβ͕Ԡื or اۀ͕εΧ΢τૹ৴ͨ͠ࡍʹ૬ख͕ϝοηʔδΛฦ৴͢Ε͹ Match (positive ) • Ԡื or εΧ΢τૹ৴Λ૬ख͕Ӿཡ্ͨ͠ͰϝοηʔδΛฦ৴͠ͳ͚Ε͹ Not Match (negative ) • Ԡื or εΧ΢τૹ৴͕ൃੜ͕ͨ͠૬ख͕Ӿཡ͍ͯ͠ͳ͍΋ͷͷධՁ஋͸ෆ໌ʢະධՁʣ
26. ©2022 Wantedly, Inc. ࣮ݧσʔλ • Wantedly Visit ʹ͓͚Δ 2019/11 - 2020/10 ͷ1೥෼ͷߦಈϩά • ৬छΛʮΤϯδχΞʯʹઃఆ͍ͯ͠ΔϢʔβ • ืू৬छΛʮΤϯδχΞʯʹઃఆ͍ͯ͠Δاۀʢืूʣ • ֘౰ظؒதʹ5݅Ҏ্ͷᅂ޷σʔλΛ༗͢ΔϢʔβͱاۀ • ֘౰ظؒதʹ100݅Ҏ্ͷᅂ޷σʔλΛ༗͢ΔϢʔβΛআ֎
27. ©2022 Wantedly, Inc. ධՁ • ධՁ஋͕ෆ໌Ͱͳ͍Ϣʔβͱاۀͷ૊ͷ͏ͪ10%Λςετσʔλͱͯ͠ར༻ • ༧ଌ͞Εͨ Reciprocal Preference Score Λ AUC ͰධՁ
28. ©2022 Wantedly, Inc. • Preference Score ༧ଌܭࢉͷͨΊͷΞϧΰϦζϜ • ߦಈཤྺʹجͮ͘ϝϞϦϕʔεͷϢʔβϕʔεڠௐϑΟϧλϦϯά [RCF] (Xia 2015 ) • ߦಈཤྺʹج͖ͮ࡞੒ͨ͠ User-User ߦྻʹ Matrix Factorization Λద༻ [LFRR](Neve 2019 ) • Aggregation Function • (Neve 2019)Ͱ࣮ݧ͞Ε͍ͯͨ4छྨ • Arithmetic Mean (AM ) • Geometric Mean (GM ) • Harmonic Mean (HM ) • Cross-Ratio Uninorm (CRU) طଘख๏ᶃ User A to User B Preference Score User B to User A Preference Score Reciproca l Preference Score Aggregatio n Function CRU:
29. ©2022 Wantedly, Inc. ࣮ݧ݁Ռᶃ AUC AM GM HM CRU RCF 0.555 0.605 0.623 0.601 LFRR 0.549 0.559 0.566 0.513 RCF LFRR • طଘݚڀͱಉ༷ɺHM(ௐ࿨ฏۉ)͕΋ͬͱ΋ߴ͍ੑೳʹ • طଘݚڀͱҟͳΓɺLFRR ΑΓ΋ RCF ͷํ͕ߴ͍ੑೳʹ Aggregation Function Algorithm
30. ©2022 Wantedly, Inc. طଘख๏ᶄ DEIM2021 ͷٕज़ใࠂ https://speakerdeck.com/yuya4/deim2021-rrs-wantedly-visit • ᅂ޷σʔλͷྔͱ࣭Λ্͛ΔͨΊʹෛͷᅂ޷σʔλΛ׆༻ʢNegʣ • Ԡื/εΧ΢τૹ৴͞ΕͯӾཡ͕ͨ͠ϝοηʔδฦ৴͠ͳ͔ͬͨͱ͍͏ෛͷᅂ޷σʔλ΋ར༻ • ਖ਼ͷᅂ޷σʔλΛར༻ͨ͠ਖ਼ͷ Preference Score ͷ༧ଌʹՃ͑ɺෛͷ Preference Score Λ༧ ଌɺ̎छྨͷ Preference Score Λ଍͠߹Θͤͯ Preference Score ΛಘΔ • ݸਓϢʔβͱاۀϢʔβͷ Preference Score ͷ܏޲ͷࠩΛิਖ਼ʢScalerʣ • ݸਓϢʔβͱاۀϢʔβͷ Preference Score ͦΕͧΕʹ MinMaxScaler Λద༻্ͨ͠Ͱ Aggregation Function Λར༻ͯ͠ Reciprocal Preference Score ΛಘΔ
31. ©2022 Wantedly, Inc. ࣮ݧ݁Ռᶄ AUC AM GM HM CRU RCF 0.555 0.605 0.623 0.601 LFRR 0.549 0.559 0.566 0.513 LFRR + Scaler + Neg 0.651 0.692 0.713 0.597 LFRR + Scaler + Neg Aggregation Function Algorithm ఏҊख๏Ͱ͋Δ LFRR + Scaler + Neg ͱ HM ͷ૊Έ߹Θ͕ͤ࠷΋ߴ͍ੑೳʹ طଘख๏ᶃͱͷൺֱ
32. ©2022 Wantedly, Inc. • Scaler ʹΑͬͯݸਓϢʔβͱاۀϢʔβͷશମͷࠩ͸ߟྀ͕ͨ͠ɺݸਓϢʔβ ͝ͱ/اۀϢʔβ͝ͱͷධՁ܏޲ͷҧ͍ΛߟྀͰ͖͍ͯͳ͍ • ৻ॏʹߟ͑ͯ਺݅ʹԠื͢Δ/ؾܰʹେྔʹԠื͢ΔݸਓϢʔβ • ͓ۚͱਓతϦιʔεͷ५୔ͳاۀ΄ͲεΧ΢τૹ৴ྔ͕େ͖͘ • ༗໊ɾਓؾͳاۀʹ͸Ԡื͸ूத͢Δ • ࿩୊ੑΛूΊΔૂ͍ͳͲͷઑͬͨืूΛग़ͨ͠اۀʹ΋Ԡื͸ूத • ෛͷᅂ޷σʔλͷ׆༻ʢNegʣ ʹΑ͕ͬͯࠩΑΓݦஶʹ • ݸਓϢʔβͷෛͷᅂ޷σʔλ͸εΧ΢τΛड͚औͬͨࡍʹ͔͠ൃੜ͠ͳ͍ • εΧ΢τΛड͚औΕΔͷ͸Ұ෦ͷݸਓϢʔβʔͰ͋ΓɺͦͷதͰภΓ΋ • ԠืΛड͚ͨΒϚον͍ͯ͠ͳ͘ͱ΋ͱΓ͋͑ͣฦ৴͢Δӡ༻ͷاۀ ՝୊
33. ©2022 Wantedly, Inc. 1. Preference Score ͷ༧ଌʹ Biased Matrix Factorization Λద༻ʢBiasʣ 2. Reciprocal Preference Score ܭࢉ࣌ͷ Aggregation Function ʹϩδεςΟ ΫεճؼΛ࠾༻͠ɺϚονʹ࠷దͳॏΈΛֶशͯ֫͠ಘʢLR()ʣ ࣮ݧ͢Δख๏ ՝୊Ͱ͋ΔݸਓϢʔβ͝ͱ/اۀϢʔβ͝ͱͷධՁ܏޲ͷҧ͍Λߟྀ͢ΔͨΊ
34. ©2022 Wantedly, Inc. • ैདྷͷਪનγεςϜͷݚڀͰߴ͍ੑೳΛތΔɺݸਓϢʔβʔ͝ͱاۀϢʔβʔ͝ ͱͷόΠΞε΋ֶश͢Δ Biased Matrix Factorization Λར༻ • MF : • Biased MF: ࣮ݧ͢Δख๏ᶃ Preference Score ͷ༧ଌʹ Biased Matrix Factorization Λద༻ʢBiasʣ min p,q ∑ u,i∈ℝ+ (rui − pT u qi )2 + λ ( ||pu ||2 + ||qi ||2 ) min p,q ∑ u,i∈ℝ+ (rui − (pT u qi + bui )2 + λ ( ||pu ||2 + ||qi ||2 + b2 u + b2 i ) bui = μ + bu + bi
35. ©2022 Wantedly, Inc. • Reciprocal Preference Score ͷ༧ଌੑೳ͕େ͖͘޲্ • ҰํͰɺͦΕͧΕͷ Preference Score ͷ༧ଌੑೳ͸௿Լ • ͜Ε·Ͱ͸ภΓͷେ͖͍ “౰ͯ΍͍͢” ݸਓϢʔβʔ΍اۀϢʔβʔͷᅂ޷Λਖ਼͘͠ ༧ଌ͢Δ͜ͱͰߴ͍༧ଌੑೳͱͳ͍͔ͬͯͨ ࣮ݧ݁Ռ AUC Reciprocal PS User PS Company PS ఏҊख๏ᶄ 0.713 0.723 0.721 ఏҊख๏ᶄ + Bias 0.780 0.657 0.702 Preference Score ͷ༧ଌʹ Biased Matrix Factorization Λద༻ʢBiasʣ
36. ©2022 Wantedly, Inc. ࣮ݧ͢Δख๏ᶄ Reciprocal Preference Score ܭࢉ࣌ͷ Aggregation Function ʹϩδεςΟΫεճؼΛ࠾༻͠ɺ Ϛονʹ࠷దͳॏΈΛֶशͯ֫͠ಘʢLR()ʣ • ݸਓϢʔβʔͱاۀϢʔβʔͷ૊ʹରͯ͠ɺͦΕͧΕͷ༧ଌ Preference Score ೖྗͱͯ͠ɺϚον ͔ͨ͠Ͳ͏͔Λ༧ଌ͢ΔϩδεςΟοΫճؼʹΑΓॏΈΛֶशʢLR(preds)ʣ logit(p) = a + b1 * PSu + b2 * PSc PSu PSc : ݸਓϢʔβʔͷ༧ଌ Preference Score : اۀϢʔβʔͷ༧ଌ Preference Score • ݸਓϢʔβʔɺاۀϢʔβʔ͝ͱʹҟͳΔॏΈΛಘΔͨΊʹɺone-hot ϕΫτϧΛೖྗʹՃֶ͑ͯश ʢLR(preds + one-hot)ʣ logit(p) = a + b1 * PSu + b2 * PSc + ∑ u∈ 𝕌 bu * onehotu + ∑ c∈ℂ bc * onehotc logit(p) = a + b1 * PSu + b2 * PSc + b3 * PSu * PSc + ∑ u∈ 𝕌 bu * onehotu + ∑ c∈ℂ bc * onehotc • ௐ࿨ฏۉͷੑೳ͕ྑ͍͜ͱ͔Βɺަޓ࡞༻Λߟྀ͢ΔͨΊʹݸਓͱاۀͷ Preference Score ͷੵΛ ೖྗʹՃֶ͑ͯशʢLR(preds + one-hot + interaction)ʣ
37. ©2022 Wantedly, Inc. ࣮ݧ݁Ռᶅ AUC طଘख๏ᶃʢRCFʣ 0.623 طଘख๏ᶃʢLFRRʣ 0.566 طଘख๏ᶄʢLFRR + Scaler + Negʣ 0.713 طଘख๏ᶄ + Bias 0.780 طଘख๏ᶄ + Bias + LR(preds) 0.772 طଘख๏ᶄ + Bias + LR(preds + one-hot) 0.803 طଘख๏ᶄ + Bias + LR(preds + one-hot + interaction) 0.817 ఏҊख๏ͱطଘख๏ᶃᶄͱͷൺֱ·ͱΊ • Aggregation Function ͷॏΈΛϩδεςΟοΫճؼʹΑΓಘΔ͜ͱͰશମతʹ༧ଌ ੑೳ͸޲্ • ࠷ऴతʹɺࠓճͷఏҊख๏Λ͢΂ͯՃ͑ͨ΋ͷͷੑೳ͕࠷΋ߴ͘
38. ©2022 Wantedly, Inc. • ૬ޓਪનγεςϜͷ঺հ • ʮαʔϏε಺ͷϢʔβΛޓ͍ʹਪન͢ΔγεςϜʯ • ͓ޓ͍ͷᅂ޷͕Ұகͯ͠ॳΊͯਪન͕"੒ޭ"ͨ͜͠ͱʹͳΔ • ݚڀ͸·ͩ·ͩ͜Ε͔Βൃలͷ༨஍͋Γ • ձࣾ๚໰ΞϓϦ Wantedly Visit ͷ࣮σʔλΛར༻ͨ͠ݕূ࣮ݧͷ঺հ • طଘݚڀͷख๏ΛϕʔεϥΠϯͱͨ͠վળख๏ͷධՁ࣮ݧ • γεςϜ಺ͷ༷ʑͳϢʔβʔͷੑ࣭Λߟྀͨ͠վળख๏ʹΑΓੑೳͷ޲্Λ֬ೝ • Biased Matrix Preference Score ͷ༧ଌʹ Biased Matrix Factorization Λద༻ʢBiasʣ • Reciprocal Preference Score ܭࢉ࣌ͷ Aggregation Function ʹϩδεςΟΫεճؼΛ࠾༻͠ɺ Ϛονʹ࠷దͳॏΈΛֶशͯ֫͠ಘʢLR()ʣ ·ͱΊ
39. ©2022 Wantedly, Inc. 3FGT • (Pizzato 2010) Luiz Pizzato, Tomek Rej, Thomas Chung, Irena Koprinska, and Judy Kay. 2010. RECON: a reciprocal recommender for online dating. Proceedings of the fourth ACM conference on Recommender systems P. 207-214 . • (Pizzato 2012) Luiz Pizzato, Tomasz Rej, Joshua Akehurst, Irena Koprinska, Kalina Yacef, and Judy Kay. 2012. Recommending people to people: the nature of reciprocal recommenders with a case study in online dating. User Model User-Adap Inter (2013) 23: 447 . • (Xia 2015) Peng Xia, Benyuan Liu, Yizhou Sun, and Cindy Chen. 2015. Reciprocal Reciprocal recommendation System for Online Dating. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining P. 234-241. • (Neve 2019) J Neve, I Palomares.2019. Latent Factor Models and Aggregation Operators for Collaborative Filtering in Reciprocal Recommender Systems Proceedings of the 13th ACM Conference on Recommender Systems, 219-227 . • (Neve 2020) J Neve, R McConville.2020. ImRec: Learning Reciprocal Preferences Using Images. Proceedings of the 14th ACM Conference on Recommender Systems, 170-179.