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会社訪問アプリ「Wantedly Visit」における 人・仕事・会社の関係性を考慮した相互推...

Shuhei Goda
March 06, 2023

会社訪問アプリ「Wantedly Visit」における 人・仕事・会社の関係性を考慮した相互推薦システム

2023年3月6日 DEIM2023 (https://event.dbsj.org/deim2023/) における技術報告の資料です。

3b-5: ドメイン指向推薦:企業・人材
3月6日 13:30 ~ 15:30
https://deim-management-system.github.io/deim2023_program/index.html#3b-5

既存研究ではあまり取り扱われていない求職者の会社に関する嗜好の重要性に着目し、人・仕事・会社の3要素に基づいた相互推薦システムの提案手法の性能を会社訪問アプリ「Wantedly Visit」の実データを用いて検証しています。

Shuhei Goda

March 06, 2023
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  1. ©2023 Wantedly, Inc. 
 ձࣾ๚໰ΞϓϦʮWantedly Visitʯʹ͓͚Δ 
 ਓɾ࢓ࣄɾձࣾͷؔ܎ੑΛߟྀͨ͠૬ޓਪનγεςϜ DEIM2023 3b-5:

    υϝΠϯࢦ޲ਪનɿاۀɾਓࡐʲٕज़ใࠂʳ Mar. 6 2023 - ߹ాपฏʢ΢ΥϯςουϦʔגࣜձࣾʣ
  2. ©2023 Wantedly, Inc. 1. اۀ঺հ • ձࣾͱϓϩμΫτͷ঺հ • ϓϩμΫτʹ͓͚ΔσʔλαΠΤϯεͷ׆༻ࣄྫ •

    ΞΧσϛΞʹ͓͚Δ׆ಈ 2. ૬ޓਪનγεςϜͱ͸ • ૬ޓਪનγεςϜͷ֓ཁ • HRྖҬʹ͓͚Δઌߦݚڀ 3. ਓɾ࢓ࣄɾձࣾͷؔ܎ੑΛߟྀͨ͠૬ޓਪનγεςϜ • ໰୊ఏى • ఏҊख๏ • ࣮ݧɾߟ࡯ ձࣾ๚໰ΞϓϦʮWantedly Visitʯʹ͓͚Δਓɾ࢓ࣄɾձࣾͷؔ܎ੑΛߟྀͨ͠૬ޓਪનγεςϜ
  3. ©2023 Wantedly, Inc. 1. اۀ঺հ • ձࣾͱϓϩμΫτͷ঺հ • ϓϩμΫτʹ͓͚ΔσʔλαΠΤϯεͷ׆༻ࣄྫ •

    ΞΧσϛΞʹ͓͚Δ׆ಈ 2. ૬ޓਪનγεςϜͱ͸ • ૬ޓਪનγεςϜͷ֓ཁ • HRྖҬʹ͓͚Δઌߦݚڀ 3. ਓɾ࢓ࣄɾձࣾͷؔ܎ੑΛߟྀͨ͠૬ޓਪનγεςϜ • ໰୊ఏى • ఏҊख๏ • ࣮ݧɾߟ࡯ ձࣾ๚໰ΞϓϦʮWantedly Visitʯʹ͓͚Δਓɾ࢓ࣄɾձࣾͷؔ܎ੑΛߟྀͨ͠૬ޓਪનγεςϜ
  4. ©2023 Wantedly, Inc. ࣗݾ঺հ ໊લ 
 ߹ా पฏ(Shuhei Goda) ܦྺ

    
 2016೥3݄ ๺ւಓେֶେֶӃཧֶӃӉ஦ཧֶઐ߈म࢜՝ఔमྃ ॴଐɾ໾ׂ 
 ΢ΥϯςουϦʔגࣜձࣾɾData Science Tech Lead ձࣾ๚໰ΞϓϦ Wantedly Visit ͷϚονϯά෦෼Λ୲͏ 
 ਪનγεςϜͷ։ൃΛਪਐ͍ͯ͠ΔɻKaggle Masterɻ @hakubishin3 @jy_msc
  5. ©2023 Wantedly, Inc. Create A World Where Work Drives Passion.

    γΰτͰίίϩΦυϧͻͱΛ;΍͢ ձࣾ঺հ ࢲͨͪͷϛογϣϯ ©2023 Wantedly, Inc.
  6. ©2023 Wantedly, Inc. 1. اۀ঺հ • ձࣾͱϓϩμΫτͷ঺հ • ϓϩμΫτʹ͓͚ΔσʔλαΠΤϯεͷ׆༻ࣄྫ •

    ΞΧσϛΞʹ͓͚Δ׆ಈ 2. ૬ޓਪનγεςϜͱ͸ • ૬ޓਪનγεςϜͷ֓ཁ • HRྖҬʹ͓͚Δઌߦݚڀ 3. ਓɾ࢓ࣄɾձࣾͷؔ܎ੑΛߟྀͨ͠૬ޓਪનγεςϜ • ໰୊ఏى • ఏҊख๏ • ࣮ݧɾߟ࡯ ձࣾ๚໰ΞϓϦʮWantedly Visitʯʹ͓͚Δਓɾ࢓ࣄɾձࣾͷؔ܎ੑΛߟྀͨ͠૬ޓਪનγεςϜ
  7. ©2023 Wantedly, Inc. ૬ޓਪનγεςϜ(Reciprocal Recommeder Systems)ͱ͸ʁ User Item ैདྷͷҰൠతͳਪનγεςϜ ૬ޓਪનγεςϜ

    e.g. Amazon, Netflix ਪન͞ΕΔ ʮαʔϏε಺ͷϢʔβ͕ޓ͍ʹਪન͞Ε߹͏γεςϜʯ User(Job Seeker) User(Recruiter/Company) e.g. Wantedly, LinkedIn ਪન͞Ε߹͏ User(Female) User(Male) e.g. Tinder, Pairs ਪન͞Ε߹͏
  8. ©2023 Wantedly, Inc. ૬ޓਪનγεςϜͷ࿮૊Έ 1. γεςϜ಺ͷϢʔβ͸ A ͱ B ͷ̎ͭͷάϧʔϓʹ෼͔Ε͓ͯΓɺҟͳΔάϧʔϓͷϢʔβ͕ޓ͍ਪન͞ΕΔ

    
 ΋ͷͱ͢Δʢe.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 Predict User A to User B Preference Score Predict User B to User A Preference Score Aggregate 
 Preference Scores Reciprocal 
 Preference Score
  9. ©2023 Wantedly, Inc. 1. اۀ঺հ • ձࣾͱϓϩμΫτͷ঺հ • ϓϩμΫτʹ͓͚ΔσʔλαΠΤϯεͷ׆༻ࣄྫ •

    ΞΧσϛΞʹ͓͚Δ׆ಈ 2. ૬ޓਪનγεςϜͱ͸ • ૬ޓਪનγεςϜͷ֓ཁ • HRྖҬʹ͓͚Δઌߦݚڀ 3. ਓɾ࢓ࣄɾձࣾͷؔ܎ੑΛߟྀͨ͠૬ޓਪનγεςϜ • ໰୊ఏى • ఏҊख๏ • ࣮ݧɾߟ࡯ ձࣾ๚໰ΞϓϦʮWantedly Visitʯʹ͓͚Δਓɾ࢓ࣄɾձࣾͷؔ܎ੑΛߟྀͨ͠૬ޓਪનγεςϜ
  10. ©2023 Wantedly, Inc. ਓ͸ԿΛߟྀ͠ͳ͕Β࢓ࣄΛ୳͢ͷ͔ Ұൠతʹਓ͕࢓ࣄΛ୳࣌͢ʹߟྀ͢Δͷ͸ʮδϣϒɾσΟεΫϦϓγϣϯʯ͚ͩͰͳ͍͸ͣ • ྫ͑͹ɺձࣾͷϛογϣϯ΍ձࣾͰಇ͍͍ͯΔϝϯόʔͷงғؾ • ձࣾͷن໛΍੡඼ɺۀքɺઓུͳͲ΋ॏཁͩΖ͏ Job

    User ɾ৬छ ɾ࢓ࣄ಺༰ ɾۈ຿஍ ɾۈ຿࣌ؒ ɾ࠾༻ܗଶ ɾ଴۰ ɹ…. ʮ࢓ࣄʯʹؔ͢Δᅂ޷ Company ɾϛογϣϯ ɾҰॹʹಇ͘ਓͨͪ ɾձࣾن໛ ɾۀք ɾ੡඼ ɾઓུ ɹ… ʮձࣾʯʹؔ͢Δᅂ޷ Company಺
  11. ©2023 Wantedly, Inc. طଘݚڀͰߟྀͰ͖͍ͯͳ͍͜ͱ͸Կ͔ ͜Ε·Ͱʮਓʯͱʮ࢓ࣄʯͷؒͷΠϯλϥΫγϣϯ͔͠ѻΘΕͯ͜ͳ͔ͬͨͷͰɺ 
 ʮਓʯͱʮձࣾʯͷؔ܎Λද͢৘ใΛ໌ࣔతʹऔΓೖΕΔ͜ͱ͸Ͱ͖͍ͯͳ͍ • ࢓ࣄʹؔ͢Δᅂ޷͸நग़Ͱ͖Δ͕ɺձࣾʹؔ͢Δᅂ޷͸ʁ Job

    User ɾ৬छ ɾ࢓ࣄ಺༰ ɾۈ຿஍ ɾۈ຿࣌ؒ ɾ࠾༻ܗଶ ɾ଴۰ ɹ…. ʮਓʯͱʮ࢓ࣄʯͷؔ܎ੑ Company ɾϛογϣϯ ɾҰॹʹಇ͘ਓͨͪ ɾձࣾن໛ ɾۀք ɾ੡඼ ɾઓུ ɹ… ʮਓʯͱʮձࣾʯͷؔ܎ੑ Company಺
  12. ©2023 Wantedly, Inc. ձࣾ๚໰ΞϓϦ Wantedly Visit ͱ͸ ձࣾ΁ͷڞײΛ࣠ͱͨ͠࢓ࣄ୳͠ͷମݧΛఏڙ͢ΔαʔϏε • ࢓ࣄ΋ॏཁͳཁૉ͕ͩɺͦΕҎ্ʹձࣾʹڞײͰ͖Δ͔Λॏཁࢹ

    • ձࣾͷϛογϣϯ΍ͦ͜Ͱಇ͘ਓͷ૝͍͕఻ΘΓ΍͍͢ϓϩμΫτઃܭ Ϣʔβ ձࣾͷίϯςϯπ ɾձࣾϖʔδ ɾձࣾϒϩά ࢓ࣄͷίϯςϯπ ɾืू ճ༡ ืू΁ͷ 
 Ԡื ձࣾʹ࿩Λฉ͖ʹߦ͘
  13. ©2023 Wantedly, Inc. Wantedly Visit ʹطଘͷ૬ޓਪનγεςϜΛద༻͢Δࡍͷ՝୊ • ҰൠతͳδϣϒϚονϯάαʔϏεͱൺ΂ͯɺϢʔβʔମݧʹ͓͍ͯ 
 ձࣾͷཁૉ͕ॏཁͳ໾ׂΛ୲͍ͬͯΔ

    • ͦͷͨΊɺʮਓʯͱʮ࢓ࣄʯͷΠϯλϥΫγϣϯ͚ͩΛߟྀͨ͠طଘͷ૬ޓਪનγεςϜ 
 ͷ࿮૊ΈΛͦͷ··ద༻͢Δͷ͸ෆे෼ͩͱߟ͑Δ - ⭕Ϣʔβͷ࢓ࣄʹؔ͢Δᅂ޷ɺ࢓ࣄͷީิऀʹؔ͢Δᅂ޷ʢ৬຿ΛՌͨͤΔظ଴͕͋Δ͔Ͳ͏͔ʣ - ❌Ϣʔβͷձࣾʹؔ͢Δᅂ޷ɺձࣾͷީิऀʹؔ͢Δᅂ޷ʢՁ஋؍ͷҰகɺWillͱMustͷҰகɺͳͲʣ
  14. ©2023 Wantedly, Inc. Wantedly Visit ʹదͨ͠૬ޓਪનγεςϜͱ͸ ʮਓʯͱʮ࢓ࣄʯɺʮਓʯͱʮձࣾʯͷؔ܎ੑΛ໌ࣔతʹऔΓೖΕͨ૬ޓਪનγεςϜͷఏҊ • ఏҊख๏Λ Wantedly

    Visit ͷ࣮σʔλΛར༻ͯ͠ݕূ͢Δ Job User ɾ৬छ ɾ࢓ࣄ಺༰ ɾۈ຿஍ ɾۈ຿࣌ؒ ɾ࠾༻ܗଶ ɾ଴۰ ɹ…. ࢓ࣄʹؔ͢Δᅂ޷ Company ɾϛογϣϯ ɾҰॹʹಇ͘ਓͨͪ ɾձࣾن໛ ɾۀք ɾ੡඼ ɾઓུ ɹ… ձࣾʹؔ͢Δᅂ޷ Company಺ Ϣʔβʹؔ͢Δᅂ޷ Ϣʔβʹؔ͢Δᅂ޷
  15. ©2023 Wantedly, Inc. ࣮ݧ֓ཁ Wantedly Visit ʹ͓͚ΔϢʔβͱืूͷ Matching Λ༧ଌ (ਪન୯Ґ͸ืूͰ͋ΔͨΊ)

    • Ϣʔβ͕Ԡื or ืू(اۀ)͕εΧ΢τૹ৴ͨ͠ࡍʹ૬ख͕ϝοηʔδΛฦ৴͢Ε͹ Match (positive) • Ԡื or εΧ΢τૹ৴Λ૬ख͕Ӿཡ্ͨ͠ͰϝοηʔδΛฦ৴͠ͳ͚Ε͹ Not Match (negative) ืू(اۀ) Ϣʔβ Ԡื ϝοηʔδฦ৴ ืू(اۀ) Ϣʔβ εΧ΢τૹ৴ ϝοηʔδฦ৴ ืू(اۀ) Ϣʔβ Ԡื ӾཡͷΈ ืू(اۀ) Ϣʔβ εΧ΢τૹ৴ ӾཡͷΈ Match Not Match
  16. ©2023 Wantedly, Inc. ࣮ݧσʔλ • Wantedly Visit ʹ͓͚Δ 2022೥04݄ ʙ

    2023೥02݄ ͷ໿1೥෼ͷߦಈϩά • ֘౰ظؒதʹ5݅Ҏ্ͷᅂ޷σʔλΛ༗͢ΔϢʔβͱืू(اۀ) • ֘౰ظؒதʹ100݅Ҏ্ͷᅂ޷σʔλΛ༗͢ΔϢʔβΛআ֎
  17. ©2023 Wantedly, Inc. ϕʔεϥΠϯख๏ Preference Scoreͷਪ࿦ʹߦྻ෼ղΛ༻͍ͯɺू໿ؔ਺͸ௐ࿨ฏۉΛ࠾༻(Neve 2019) • طଘݚڀʹ͓͚ΔHRͷ૬ޓਪનͰ͸ɺϢʔβͱ࢓ࣄͷΠϯλϥΫγϣϯΛߦྻ෼ղ΍GNNͰѻ͍ͬͯΔ •

    ݕূͷ୯७ԽͷͨΊʹɺࠓճ͸ߦྻ෼ղΛબ୒ Predict User to Project Preference Score Predict Project to User Preference Score Aggregate using 
 Harmonic Mean Ϣʔβ × ืू ͷ 
 ΠϯλϥΫγϣϯߦྻ
  18. ©2023 Wantedly, Inc. ఏҊख๏ 2छྨͷΠϯλϥΫγϣϯߦྻΛ༻͍ͯ૬ޓਪનείΞΛਪ࿦͢Δ • ϢʔβͱձࣾͷؒͷΠϯλϥΫγϣϯ͔Βɺձࣾʹؔ͢ΔϢʔβͷ܏޲Λநग़ • ձࣾʹؔ͢Δᅂ޷ͱ࢓ࣄʹؔ͢Δᅂ޷Λू໿(ௐ࿨ฏۉ or

    ࢉज़ฏۉ)͔ͯ͠Βɺ૬ޓਪનείΞΛࢉग़͢Δ Predict User to Company Preference Score Predict Company to User Preference Score Aggregate using 
 HM / AM Predict User to Project Preference Score Predict Project to User Preference Score Aggregate using 
 HM / AM Ϣʔβ × ืू ͷ 
 ΠϯλϥΫγϣϯߦྻ Ϣʔβ × ձࣾͷ 
 ΠϯλϥΫγϣϯߦྻ Aggregate using 
 Harmonic Mean
  19. ©2023 Wantedly, Inc. ࣮ݧ݁Ռᶃ ϕʔεϥΠϯʹൺ΂ͯɺఏҊख๏ʹΑΔ Reciprocal Preference Score ͷ༧ଌੑೳ͸޲্ •

    ௐ࿨ฏۉΑΓ΋ࢉज़ฏۉͷ΄͏͕ߴ͍ੑೳʹɺ͔͠͠ू໿ؔ਺ͷҧ͍ʹΑΔࠩ͸େ͖͘ͳ͍ • େ͖ͳ޲্ͷ༨஍͸͋Γͦ͏ɺ཈͑Δ΂͖ϙΠϯτ͕͋ΔͷͰ͸ʁ AUC Reciprocal Preference Score Rate of change (%) ϕʔεϥΠϯ 0.7122 - ఏҊख๏(ௐ࿨ฏۉ) 0.7269 +2.08% ఏҊख๏(ࢉज़ฏۉ) 0.7301 +2.52%
  20. ©2023 Wantedly, Inc. Ծઆग़͠ͱͦΕʹجͮ͘ఏҊख๏ͷमਖ਼ ϢʔβʹΑͬͯ࢓ࣄͷᅂ޷ͱձࣾͷᅂ޷ͷॏΈͷڧ͕͞ҟͳΔͷͰ͸ʁ • ྫ͑͹ɺస৬ҙཉͷߴ͍Ϣʔβ΄Ͳձࣾʹؔ͢ΔཁૉΛॏཁࢹ͢Δ͔΋͠Εͳ͍ • Ϣʔβͷᅂ޷ͷू໿ؔ਺ʹϩδεςΟοΫճؼΛ࠾༻͠ɺ࠷దͳॏΈͰू໿Ͱ͖ΔΑ͏ʹ͢Δ Predict

    User to Company Preference Score Predict Company to User Preference Score Aggregate using 
 HM / AM Predict User to Project Preference Score Predict Project to User Preference Score Aggregate using 
 Logistic Reg Ϣʔβ × ืू ͷ 
 ΠϯλϥΫγϣϯߦྻ Ϣʔβ × ձࣾͷ 
 ΠϯλϥΫγϣϯߦྻ Aggregate using 
 Harmonic Mean
  21. ©2023 Wantedly, Inc. ࣮ݧ݁Ռᶄ Ϣʔβ͕ઃఆͨ͠స৬ҙཉΛ༻͍ͯɺϢʔβͷᅂ޷ΛϩδεςΟοΫճؼͰू໿ͤ͞Δ • ͜ͷ΍Γํ͸ޮՌͳ͠ AUC Reciprocal Preference

    Score Rate of change (%) ϕʔεϥΠϯ 0.7122 - ఏҊख๏(ௐ࿨ฏۉ) 0.7269 +2.08% ఏҊख๏(ࢉज़ฏۉ) 0.7301 +2.52% ఏҊख๏(ϩδճؼ) 0.7255 +2.07%
  22. ©2023 Wantedly, Inc. ߟ࡯ᶃ ϕʔεϥΠϯͷ࣌఺Ͱ͢ͰʹɺձࣾͷཁૉΛ͋Δఔ౓நग़Ͱ͖͍ͯΔͷͰ͸ͳ͍͔ • Wantedly Visit ʹ͓͚Δืूϖʔδͷߏ੒͸ҎԼͷ௨Γɺձࣾͷཁૉͷํ͕༏ઌ౓ߴ͍ •

    Ϣʔβ͸͜ͷߏ੒ʹڧ͘Өڹ͞ΕɺืूͱϢʔβͷΠϯλϥΫγϣϯߦྻʹ΋ձࣾͷཁૉؚ͕·ΕΔͷͰ͸ Χόʔࣸਅ ձࣾͷϝϯόʔ঺հ ձࣾͷ8)"5 ࣄۀ಺༰ͷઆ໌ͳͲ ձࣾͷ8): ϏδϣϯɺϛογϣϯͳͲ ձࣾͷ)08 ࣄۀͷಛ௃ɺಇ͘஥ؒͳͲ ϙδγϣϯͷৄࡉɺۀ຿಺༰ͳͲ ืूͷλΠτϧɺ৬छɺ࠾༻ܗଶɺձ໊ࣾ ืूϖʔδͷߏ੒ ϖʔδ্෦ ϖʔδԼ෦ ձࣾͷཁૉ ࢓ࣄͷཁૉ ྆ํͷཁૉ
  23. ©2023 Wantedly, Inc. ߟ࡯ᶄ ఏҊख๏Ͱ͸ɺձࣾͷཁૉΛ͏·͘நग़Ͱ͖͍ͯͳ͍ͷͰ͸ͳ͍͔ • Wantedly Visit ʹ͓͍ͯϢʔβ͸࢓ࣄʹؔ͢Δ࠷௿৚݅ʢޏ༻ܗଶɺۈ຿஍ɺ৬छͳͲʣΛ 


    ઃఆͯ͠ϑΟϧλϦϯάΛ͔ͯ͠ΒձࣾΛ୳͢ • ྫ͑͹ɺձࣾͷཁૉ͕ຬͨ͞Ε͍ͯͯ΋ɺ΍Γ͍ͨ࢓ࣄ͕ͳ͍ͳΒԠื͠ͳ͍ͩΖ͏ • ձࣾ - ϢʔβͷΠϯλϥΫγϣϯʹ͸ɺ࢓ࣄͷཁૉ΋େؚ͖͘·Ε͍ͯΔͷͰ͸ͳ͍͔ Wantedly Visit ʹ͓͚Δืूͷݕࡧ৚݅ ޏ༻ܗଶɺۈ຿஍ɺ৬छͳͲͷ৚݅ઃఆ͕Ͱ͖Δ
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    - ਓͷΠϯλϥΫγϣϯཤྺΛϕʔεʹɺձࣾͷ৘ใΛ෇༩͢ΔʢFactorization MachineͳͲʣ • ͜ͷߏ੒ͷ৔߹ɺձࣾͷಛ௃நग़Λ໌ࣔతʹߦ͏ඞཁ͕͋Δʢϛογϣϯ΍ձࣾͷงғؾɺ 
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    ͜ͱΛఏҊɺେ͖ͳվળ͸ݟࠐΊͳ͔ͬͨ ߟ࡯: ձࣾͱ࢓ࣄͷᅂ޷Λ͏·͘੾Γ෼͚ΒΕ͍ͯͳ͍Մೳੑ͕͋Δ 1. αʔϏεʹ͓͚Δืूίϯςϯπͷಛ௃: ձࣾͷཁૉ͕ଟؚ͘·Ε͍ͯΔ 2. αʔϏεʹ͓͚ΔϢʔβߦಈͷಛ௃: ࢓ࣄʹؔ͢Δ࠷௿৚݅Λઃఆ͔ͯ͠ΒձࣾΛ୳͢ ࠓޙ: ਓɾ࢓ࣄɾձࣾͷؔ܎ੑΛ׆͔͠੾ΔͨΊʹɺͲ͏͍͏ߏ੒͕ద੾ͳͷ͔ • ࢓ࣄ - ਓͷΠϯλϥΫγϣϯཤྺΛϕʔεʹɺձࣾͷ৘ใΛ෇͚Ճ͑Δ • ը૾΍ςΩετͳͲͷඇߏ଄Խσʔλ͔Βɺཉ͍͠ձࣾͷಛ௃Λ͏·͘৘ใநग़͢Δඞཁ͕͋Δ
  26. ©2023 Wantedly, Inc. 3FGFSFODFT • (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, 207-214. • (Hongtao 2011)Hongtao Yu, C. Liu and Fuzhi Zhang. 2011. Reciprocal Recommendation Algorithm for the Field of Recruitment. Journal of Information & Computational Science, 4061-4068. • (Tsunenori 2013)Tsunenori Mine, Tomoyuki Kakuta, Akira Ono. 2013. Reciprocal Recommendation for Job Matching with Bidirectional Feedback. 2013 Second IIAI International Conference on Advanced Applied Informatics, 39-44. • (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. • (Yang 2022)Yang, Chen and Hou, Yupeng and Song, Yang and Zhang, Tao and Wen, Ji-Rong and Zhao and Wayne Xin. 2022. Modeling Two-Way Selection Preference for Person-Job Fit. Proceedings of the 16th ACM Conference on Recommender Systems,102-112.