Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
ジョブマッチングサービスにおける相互推薦システムの応用事例と課題
Search
Sponsored
·
SiteGround - Reliable hosting with speed, security, and support you can count on.
→
Shuhei Goda
November 07, 2024
Technology
1.2k
3
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
ジョブマッチングサービスにおける相互推薦システムの応用事例と課題
第27回情報論的学習理論ワークショップ (IBIS2024)
企画セッション3:ビジネスと機械学習
https://ibisml.org/ibis2024/os/
Shuhei Goda
November 07, 2024
More Decks by Shuhei Goda
See All by Shuhei Goda
Turing × atmaCup #18 - 1st Place Solution
hakubishin3
0
1.3k
とある事業会社にとっての Kaggler の魅力
hakubishin3
9
3.2k
課題の解像度が荒かったことで意図した改善ができなかった話
hakubishin3
3
1.1k
Wantedly におけるマッチング体験を最大化させるための推薦システム
hakubishin3
4
1.4k
Recommendation Industry Talks #1 Opening
hakubishin3
1
480
会社訪問アプリ「Wantedly Visit」での シゴトに関する興味選択機能と推薦改善
hakubishin3
0
780
論文紹介: Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment(Xin Xin et al., 2023)
hakubishin3
0
730
Feedback Prize - English Language Learning における擬似ラベルの品質向上の取り組み
hakubishin3
1
1.2k
ウォンテッドリーにおける推薦システムのオフライン評価の仕組み
hakubishin3
7
7.5k
Other Decks in Technology
See All in Technology
Webの技術とガジェットで子どもも大人も楽しめるワクワク体験を提供する / Qiita Tech Festa Day 2026
you
PRO
1
310
人とエージェントが高め合う協業設計
kintotechdev
0
1k
Claude Mythos、Fable...フロンティアAIの最新動向と企業のセキュリティ対策
flatt_security
0
170
StepFunctionsとGraphRAGを活用した暗黙知活用のためのRAG基盤
yakumo
0
180
データ活用研修 問いの発見と仮説構築【MIXI 26新卒技術研修】
mixi_engineers
PRO
1
590
AI Native なプロダクト組織の立ち上げ方 : 生産性 100 倍への挑戦
mikesorae
0
1.7k
害獣害虫を自動判別! ペストコントロール支援ビジネス成功のヒント【SORACOM Discovery 2026】
soracom
PRO
0
120
AI時代におけるエンジニアの新たな役割──FDEとクオリアの探求/登壇資料(戸井田 裕貴)
hacobu
PRO
0
630
エンタープライズデータへ安全につなぐ Production-ready なエージェント設計 ― AI × MCP リファレンスアーキテクチャ ― #AIDevDay
cdataj
1
200
AI驚き屋発見器
yama3133
1
380
1台から試せる!Edge IoTを使った位置情報の活用設計【SORACOM Discovery 2026】
soracom
PRO
0
100
AIQAのナレッジ構築について
qatonchan
1
110
Featured
See All Featured
Thoughts on Productivity
jonyablonski
76
5.3k
Scaling GitHub
holman
464
140k
GitHub's CSS Performance
jonrohan
1033
470k
Why Our Code Smells
bkeepers
PRO
340
58k
RailsConf 2023
tenderlove
30
1.5k
brightonSEO & MeasureFest 2025 - Christian Goodrich - Winning strategies for Black Friday CRO & PPC
cargoodrich
3
760
Self-Hosted WebAssembly Runtime for Runtime-Neutral Checkpoint/Restore in Edge–Cloud Continuum
chikuwait
0
670
Ethics towards AI in product and experience design
skipperchong
2
330
Max Prin - Stacking Signals: How International SEO Comes Together (And Falls Apart)
techseoconnect
PRO
0
330
Put a Button on it: Removing Barriers to Going Fast.
kastner
60
4.5k
Mobile First: as difficult as doing things right
swwweet
225
10k
We Have a Design System, Now What?
morganepeng
55
8.2k
Transcript
© 2024 Wantedly, Inc. δϣϒϚονϯάαʔϏεʹ͓͚Δ ૬ޓਪનγεςϜͷԠ༻ࣄྫͱ՝ Nov. 7 2024
- Shuhei Goda ୈ27ճใతֶशཧϫʔΫγϣοϓ (IBIS2024) اըηογϣϯ3ɿϏδωεͱػցֶश
© 2024 Wantedly, Inc. ໊લɿ ߹ా पฏ Shuhei Goda
ॴଐͱׂɿ ΥϯςουϦʔגࣜձࣾ ɾData Team Manager ɾMachine Learning Tech Lead ɾProduct Manager Kaggle Tierɿ Kaggle Competitions Grandmaster @jy_msc ࣗݾհ https://www.kaggle.com/shuheigoda
©2024 Wantedly, Inc. ڀۃͷదࡐదॴʹΑΓɺ γΰτͰίίϩΦυϧͻͱΛ;͢ ࢲͨͪͷϛογϣϯ ©2024 Wantedly, Inc.
© 2024 Wantedly, Inc. iOS, Android and Web ؾܰʹձࣾ๚ ϛογϣϯՁ؍ͷڞײͰϚονϯά
• څ༩རްੜͳͲͷ݅Ͱͳ͘ɺ͍͕͋Εձࣾͷ نʹͱΒΘΕͳ͍ ·ͣʮΛฉ͖ʹߦ͘ʯͱ͍͏৽͍͠ମݧ • ݸਓͱاۀ͕ϑϥοτͳઢͰग़ձ͑Δ͜ͱͰɺΑΓັྗ తͳॴΛݟ͚ͭΔ͜ͱ͕Մೳʹ ձࣾ๚ΞϓϦʮWantedly Visitʯ
© 2024 Wantedly, Inc. తΛୡ͢ΔͨΊʹɺ֤ొਓҎԼͷΑ͏ʹߦಈ͢Δ αʔϏεΛར༻͢Δਓͷతͱߦಈ ొਓ αʔϏεΛར༻͢Δత Ϣʔβʔ
ઓతͰΓ͕͍ͷ͋Δࣄʹͭ͘ اۀ ࣗࣾͰ׆༂Ͱ͖ΔਓࡐΛ࠾༻͢Δ ࣗͷίϯςϯπ Λ࡞͢Δ ૬खͷίϯςϯπ ΛӾཡ͢Δ ૬खʹ໘ஊͷػձ Λਃ͠ࠐΉ
© 2024 Wantedly, Inc. ϓϥοτϑΥʔϜߏʢίϯςϯπʣ Ϣʔβʔ ձࣾһ ෭ۀ ϑϦʔϥϯε ֶੜ
اۀ ܦӦ ࣾһ ਓࣄ Wantedly Visit ϓϩϑΟʔϧ ࡞ Ӿཡ ձࣾϖʔδɾืू ࡞ Ӿཡ
© 2024 Wantedly, Inc. ϓϥοτϑΥʔϜߏʢϚονϯάʣ Ϣʔβʔ ձࣾһ ෭ۀ ϑϦʔϥϯε ֶੜ
اۀ ܦӦ ࣾһ ਓࣄ Wantedly Visit Ԡื Λฉ͍ͯΈ͍ͨͰ͢ ͥͻ͓͠·͠ΐ͏ʂ εΧτ ͓͠·ͤΜ͔ʁ ͓Λฉ͔͍ͤͯͩ͘͞
© 2024 Wantedly, Inc. ϓϥοτϑΥʔϜͷϞσϧʢུ֓ʣ ܧଓɾ෮ؼ ܧଓɾ෮ؼ Ԡื εΧτ ྲྀೖ
৽ن • 2-sided marketplaceͰ͋ΓɺاۀͱϢʔβʔͷ྆ํ͕υϥΠόʔ • ྑ࣭ͳίϯςϯπͷੵɺΓͳ͍Ϛονϯάʢޭମݧʣ͕ॏཁ
© 2024 Wantedly, Inc. ϓϥοτϑΥʔϜͷ՝ - ͷϘτϧωοΫԿ͔ 20248݄ظͷొͱਪҠ 403ສਓ 4.1ສࣾ
ෳࡶͷ૿ՃͱͦΕʹ͏ϚονϯάޮͷԼ Ϣʔβʔ૿Ճʹ͏՝ • ෳࡶͷ૿Ճ→ϚονϯάޮͷԼ →Ϛονϯά૿ՃͷಷԽ→ͷఀϦεΫ Ϛονϯάޮ ͷԼ Ϣʔβʔ Ϛονϯάޮ
© 2024 Wantedly, Inc. ϓϥοτϑΥʔϜͷʹඞཁͳऔΓΈ Ϣʔβʔ Ϛονϯάޮ Ϣʔβʔ Ϛονϯάޮ Ϛονϯάޮ
Ϛονϯάޮ Ϛονϯάޮ͕ߴ͘ҡ࣋͞ΕΔঢ়ଶΛ࡞Γ αʔϏεશମͷ࣋ଓతͳΛ࣮ݱ͢Δ Before After
© 2024 Wantedly, Inc. Ϛονϯάޮ͕ߴ͘ҡ࣋͞ΕΔঢ়ଶΛͲ͏࣮ݱ͢Δ͔ p(match = 1|c, j) =
p(exam = 1|c, j) × p(scout = 1|c, j, exam = 1) × p(reply = 1|c, j, scout = 1) اۀͲͷϢʔβʔΛ ݟΔ͔ اۀʹͱͬͯͦͷϢʔβʔ ັྗతʹײ͡ΒΕΔ͔ Ϣʔβʔʹͱͬͯͦͷืू ັྗతʹײ͡ΒΕΔ͔ ਪનγεςϜͰϢʔβʔɾاۀํͷίϯςϯπͷදࣔΛ੍ޚ͢Δ • To اۀɿاۀʹͱͬͯັྗతͰɺͦͷاۀͱϚονϯά͍͢͠ϢʔβʔΛදࣔ • To ϢʔβʔɿϢʔβʔʹͱͬͯັྗతͰɺͦͷϢʔβʔͱϚονϯά͍͢͠ืूΛදࣔ j … Job seeker c … Company ͜͜ʹհೖ͢Δ p(match = 1| j, c) = p(exam = 1| j, c) × p(apply = 1| j, c, exam = 1) × p(reply = 1| j, c, apply = 1) ϢʔβʔͲͷاۀΛ ݟΔ͔ Ϣʔβʔʹͱͬͯͦͷืू ັྗతʹײ͡ΒΕΔ͔ اۀʹͱͬͯͦͷϢʔβʔ ັྗతʹײ͡ΒΕΔ͔ εΧτ༝དྷ ͷϚονϯά֬ Ԡื༝དྷ ͷϚονϯά֬
© 2024 Wantedly, Inc. ղ͖͘ػցֶशλεΫ Ϛονϯά͕࠷େͱͳΔΑ͏ͳਪનϦετΛ֤ඃਪનऀʹରͯ͠࡞͢ΔλεΫ σ* c := argsortj∈
𝒥 m(c, j) σ* j := argsortc∈ 𝒞 m(c, j) →֤اۀ c ʹͱͬͯཧతͳϥϯΩϯάɺ m(c, j)ਅͷϚονϯά֬ →֤Ϣʔβʔ j ʹͱͬͯཧతͳϥϯΩϯά • ཧతͳϥϯΩϯάਅͷϚονϯά֬ʹґଘ͍ͯ͠Δ͕ɺͦͷ֬ͷ͔Βͳ͍ • ֶशσʔλʹج͍ͮͯਪఆͨ͠Ϛονϯά֬ΛͬͯϥϯΩϯάΛ࡞͠ɺ࠷దԽΛਤΔ
© 2024 Wantedly, Inc. ػցֶश͕αʔϏεͷίΞՁͰ͋ΔϚονϯάΛΓཱͨͤΔ ػցֶशϞσϧͷ༧ଌਫ਼্͕͢Δ΄ͲɺͦΕʹͬͯϓϩμΫτͷॏཁࢦ ඪ্͕͍ͯ͘͠ߏ → ػցֶश͕ϓϩμΫτͷՁΛఏڙ͢ΔͨΊͷ
“must have” ͳঢ়ଶ ػցֶशϞσϧͷੑೳ αʔϏεͷ ఏڙՁ
© 2024 Wantedly, Inc. Ϛονϯά༧ଌͷయܕతΞϓϩʔν ҎԼͷೋछྨʹେผͨ͠߹ɺجຊతʹΘΕΔͷ Predict-then-Aggregate ͷܗࣜ • Direct
Match Prediction (DMP) : ؍ଌ͞ΕͨϚονϯάʹج͍ͮͯϚον֬Λ༧ଌ͢Δ • Predict-then-Aggregate (PtA) : ํͷબΛಠཱʹϞσϧԽ͠ɺͦΕΒͷ༧ଌΛू͢Δ Direct Match Prediction Predict-then-Aggregate 𝒟 = {(ci , ji , yc→j i , yj→c i )}n i=1 ֶशσʔληοτɿ 𝒟 = {(ci , ji , mi )}n i=1 ؍ଌ͞ΕͨϚονϯά ̂ m = argmin ̂ m′  n ∑ i=1 ℓ( ̂ m′  (ci , ji ), mi ) Ϛονϯά֬ͷ ֶशɾ༧ଌɿ ֶशσʔληοτɿ اۀ͕ϢʔβʔʹΞΫγϣϯ͔ͨ͠ Ϣʔβʔ͔ΒΞΫγϣϯ͔͋ͬͨ ̂ pc→j = argmin ̂ p′  n ∑ i=1 ℓ( ̂ p′  (ci , ji ), yc→j i ) اۀ→Ϣʔβʔͷ બ֬ɿ Ϣʔβʔ→اۀͷ બ֬ɿ ̂ pj→c = argmin ̂ p′  n ∑ i=1 ℓ( ̂ p′  (ci , ji ), yj→c i ) ̂ m = M( ̂ pc→j, ̂ pj→c) Ϛονϯά֬ɿ ଛࣦؔ ूؔ • DMPతͳϚονϯάͷ༧ଌ͕ՄೳͰ͋Δ • ҰํͰϚονͷϥϕϧඇৗʹεύʔεͰ͋Γɺֶश͕ࠔ • PtAൺֱతີͳೋछྨͷϥϕϧΛͦΕͧΕ༧ଌ͢Δ λεΫʹׂ͢Δ͜ͱͰɺεύʔεੑͷʹରॲ ※ ؆୯ԽͷͨΊɺҎ߱ اۀଆͷέʔεͷΈΛߟ͑Δ
© 2024 Wantedly, Inc. ૬ޓਪનγεςϜ(Reciprocal Recommender Systems) ૬ޓਪનγεςϜͱʮαʔϏεͷϢʔβʔΛޓ͍ʹਪન͠߹͏γεςϜʯ • ਪનΛड͚औΔϢʔβʔͱਪન͞ΕͨϢʔβʔͷ྆ํ͕ຬͯ͠ਪનޭͱ͢Δ
• ૬ޓਪનγεςϜ Predict-then-Aggregate(PtA) ΞϓϩʔνΛ࠾༻͢Δ ઌߦݚڀͷΞϓϩʔν • ίϯςϯπϕʔε [Pizzato+, 2010] • ڠௐϑΟϧλϦϯάϕʔε [Xia+, 2015] [Neve+, 2019] • ϋΠϒϦοτϕʔε [Neve+, 2020] • DLϕʔε [Yıldırım+, 2021] [Luo+, 2020] [Liu+, 2024] ूؔ • 2ͭͷผʑͷ༧ଌΛΈ߹ΘͤΔׂΛ࣋ͭ • جຊతʹώϡʔϦεςΟοΫͳͷɻ୯७ੵɺௐ ฏۉɺزԿฏۉͳͲ [Pizzato+, 2010] [Neve+, 2019] جຊతͳߏ ̂ pa→b Preference Score from a to b M( ̂ pa→b, ̂ pb→a) Aggregation ̂ pb→a Preference Score from b to a ߦಈϩά ଐੑσʔλ ͳͲ
© 2024 Wantedly, Inc. ࣮ݧ݁Ռͷ֓ཁ ϓϩμΫτͷ࣮ࡍͷσʔλΛ࣮ͬͨݧͷ࣮ࢪ • ΦϑϥΠϯɿDMP ͱෳͷूؔͷύλʔϯͷ PtA
Λൺֱɻํͷᅂͷूͷ༗ޮੑΛ֬ೝ • ΦϯϥΠϯɿPtA(Scout-Only)ͱൺֱͨ͠ PtA(Harmonic Mean) ͷੑೳΛݕূɺେ෯ͳKPIͷ্Λ֬ೝ ϕʔεϥΠϯ M( ̂ pc→j, ̂ pj→c) = ̂ pc→j • PtA (Scout-Only)ɿ M( ̂ pc→j, ̂ pj→c) = ̂ pj→c • PtA (Reply-Only)ɿ ݕ౼ख๏ M( ̂ pc→j, ̂ pj→c) = ̂ pc→j ⋅ ̂ pj→c • PtA (Multiplication)ɿ M( ̂ pc→j, ̂ pj→c) = 2 ̂ pc→j ⋅ ̂ pj→c ̂ pc→j + ̂ pj→c • PtA (Harmonic Mean)ɿ ΦϑϥΠϯධՁͷҰࣄྫ
© 2024 Wantedly, Inc. ٕज़త՝ - ਪનػձͷภΓʹΑΔҰ෦Ϣʔβʔͷूத ਪનػձͷภΓ͕ੜ͡Δ͜ͱͰɺϓϥοτϑΥʔϜશମͷརӹ(Ϛον૯)͕େ͖͘ͳΒͳ͍ • ֤ϢʔβʔʹΩϟύγςΟ(Ϛονͷ্ݶ)͕ଘࡏɺͦΕΛ͑ΔҙΛΒͬͯରԠͰ͖ͳ͍
• ඃਪનػձͷগͳ͍ϢʔβʔɺޭମݧͱͳΔϚονϯάΛ࣮ݱ͢Δػձ͕ݶΒΕͯ͠·͏ • طଘͷ૬ޓਪનγεςϜݸผͷϚονΛ࠷దԽ͠ϥϯΩϯά͝ͱʹಠཱͯ͠ܭࢉ͍ͯ͠ΔͨΊɺਪ નػձͷภΓΛੜͤͯ͡͞͠·͏ શ෦ରԠ Ͱ͖ͳ͍… εΧτ͕ དྷͳ͍… ՝ʹର͢ΔΞϓϩʔν • ٻ৬ऀ͕اۀ͔ΒͷεΧτʹԠ͢Δ͕֬ɺ ٻ৬ऀ͕ΑΓଟ͘ͷεΧτΛड͚ΔʹͭΕͯ ݮগ͢ΔՄೳੑΛߟྀ͠ɺϚον૯͕࠷େԽ ͞ΕΔΑ͏ϥϯΩϯάΛ࠷దԽ [Su+, 2022] • Ϛονϯάཧʹج͖ͮɺํͷϢʔβͷᅂ ͚ͩͰͳ͘ΩϟύγςΟΛߟྀͨ͠ूΛߦ͏ [Tomita+, 2022]
© 2024 Wantedly, Inc. ٕज़త՝ - ํͷᅂͷूํ๏ ᅂͷूํ๏αʔϏεͦΕΛར༻͢ΔϢʔβʔͷੑ࣭ʹԠͯ͡ઃܭ͢Δඞཁ͕͋Δ • ํͷᅂ༧ଌ݁ՌΛͲͷΑ͏ʹू͢Δ͔ࣗ໌Ͱͳ͍
• Ұൠతʹɺௐฏۉͱ͍ͬͨɺͲͪΒ͔ยํͷείΞ͕͍ͱूͨ͠είΞ͘ͳΔͱ͍͏ੑ࣭ Λ࣋ͭ͜ͱ͕·͍͠ͱ͞Ε͍ͯΔ [Palomares+, 2021] [Neve+, 2019] • ᘳʹ֬ΛਪఆͰ͖ͨͷͰ͋Εɺू ͍ؔΒͳ͍ͣ ɻ ֤ଆͷ༧ଌͷζϨΛमਖ਼͢ΔΛ ू͕ؔ୲͍ͬͯΔɺͱղऍͰ͖Δɻ ՝ʹର͢ΔΞϓϩʔν • ํͷᅂͷॏΈΛϢʔβʔ͝ͱʹ࠷దԽ͢Δ ख๏ΛఏҊ [Kleinermann+, 2018] ूํ๏ͷ·ͱΊ [Palomares+, 2021]
© 2024 Wantedly, Inc. ٕज़త՝ - Ϛονϯάͷεύʔεੑͷରॲ ϚονϯάϓϥοτϑΥʔϜͰϚονϯάͱ͍͏ใ͕ಘʹ͍͘ಛੑ͕͋Δ • δϣϒϚονϯάͷ߹ʮస৬ʯཱ͕͢Δͱɺ࣍ͷߦಈΛى͜͢·Ͱʹ͍͕͔͔࣌ؒΔ
• ਪનଆͱඃਪનଆͷํͷҙͱߦಈ͕߹கͯ͠ॳΊͯϚονϯάཱ͕͢Δ • Ϛονϯάͷ༧ଌਫ਼Λ্͛ΔͨΊʹɺͲͷΑ͏ͳใΛͲ͏ѻ͏͖͔͕՝ͱͳΔ ՝ʹର͢ΔΞϓϩʔν • ࣝάϥϑ͔ΒϝλύεΛநग़ͯ͠Ϛονϯάͷ ϞσϦϯάʹऔΓೖΕΔ͜ͱͰɺΠϯλϥΫγϣ ϯ͚ͩͰͳ͘ίϯςϯπใΛ༗ޮతʹ׆༻͢Δ [Lai+, 2024]
© 2024 Wantedly, Inc. ݚڀ։ൃ - Ϛον༧ଌਫ਼ͷ্ • ϚονϥϕϧΛֶश͢ΔతͳΞϓϩʔν͕ͩɺ
Ϛονϥϕϧͷۃͳεύʔεੑ͕ͱͳΓɺ ੑೳͷߴ͍ϞσϧΛ࡞Εͳ͍ →ΞΠσΞɿҟͳΔੑ࣭Λ࣋ͭ2छྨͷใΛޮՌతʹΈ߹Θͤͯɺີͱਫ਼ͷʮ͍͍ͱ ͜औΓʯΛ࣮ݱ͢Δ Predict-then-Aggregate(PtA)ͷ՝ Direct Match Prediction(DMP)ͷ՝ • Ϛονϯάͱ͍͏ϞσϧԽΛɺಠཱͨ͠2छྨͷϞσ ϧʹׂ͢Δ͜ͱʹΑ͕ͬͯੜ͡Δ • σʔλλεΫͷੑ࣭ʹ߹ΘͤͨूؔΛదʹઃ ܭ͠ͳ͍ͱύϑΥʔϚϯε͕ෆे • ֤Ϟσϧͷ༧ଌޡࠩͷ͕࠷ऴతͳϥϯΩϯάύ ϑΥʔϚϯεʹӨڹ͢Δ छྨ ਫ਼ ີ ਅͷϚονϥϕϧ ਖ਼֬ ↑ ૄ ↓ Ϛον༧ଌ ൺֱతෆਖ਼֬ ↓ ີ ↑ S. Goda, Y. Hayashi, Y. Saito, A Best-of-Both Approach to Improve Match Predictions and Reciprocal Recommendations for Job Search. arXiv preprint arXiv:2409.10992 (2024).
© 2024 Wantedly, Inc. ݚڀ։ൃ - Ϛον༧ଌਫ਼ͷ্ ఏҊख๏ spseudo (c,
j; αc,j ) = αc,j ⋅ m(c, j) + (1 − αc,j ) ⋅ ̂ pc→j ⋅ ̂ pj→c ਅͷϚονϥϕϧͱϚον༧ଌΛΈ߹Θͤͨ Pseudo Match Scores Λੜ͠ɺϝλϞσϧΛֶश ਅͷϚονϥϕϧ Ϛον༧ଌ ̂ f = argminf′  ∑ (c,j) ℓ( f′  (c, j), spseudo (c, j; α)) ΦϑϥΠϯධՁ݁Ռ • ϝλϞσϧΛ༻͢Δ͜ͱͰɺैདྷͷPtAͷूϑΣʔζͰ ൃੜ͢ΔΤϥʔͷӨڹΛݮ͍ͯ͠ΔՄೳੑ͕͋Δ • ҟͳΔείΞใΛΈ߹ΘͤΔ͜ͱʹΑͬͯɺΞϯαϯϒϧ తͳޮՌ͕ಘΒΕ͍ͯΔՄೳੑ͕͋Δ ղऍ S. Goda, Y. Hayashi, Y. Saito, A Best-of-Both Approach to Improve Match Predictions and Reciprocal Recommendations for Job Search. arXiv preprint arXiv:2409.10992 (2024).