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user-behaviour-vol2

D8b77b4b3b0373eaee6c6077c4d7330a?s=47 KARAKURI Inc.
November 16, 2021

 user-behaviour-vol2

ユーザー行動予測に関する調査-vol2

D8b77b4b3b0373eaee6c6077c4d7330a?s=128

KARAKURI Inc.

November 16, 2021
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  1. Ϣʔβʔߦಈ༧ଌʹؔ͢Δௐࠪ-vol2 ∁໦ࢤ࿠

  2. Ϣʔβʔߦಈ෼ੳ Web্ͰϢʔβʔ͸༷ʑͳߦಈΛߦ͏ ͦͷߦಈͷϩάͷσʔλ͔ΒϢʔβʔ͕࣍ʹͲͷΑ͏ͳߦಈΛ͢Δͷ͔Λ༧ଌ͢Δ͜ͱ͸ɼϏδωε ্େ͖ͳՁ஋Λ࣋ͭ https://medium.com/@yahavofir/paid-information-is-everywhere-and-it-changes-the-way-we-make-decisions-ca82679ed2bd

  3. SessionͱϢʔβʔߦಈ Ϣʔβʔߦಈ͸Ұൠʹsessionͱ͍͏·ͱ·ΓͰѻΘΕΔ Sessionͱ͸ಛఆظؒ಺ʹى͖ͨҰ࿈ͷϢʔβʔߦಈͷ·ͱ·Γͷ͜ͱ [https://support.google.com/analytics/answer/2731565? hl=ja#zippy=%2C%E3%81%93%E3%81%AE%E8%A8%98%E4%BA%8B%E3%81%AE%E5%86%85%E5%AE%B9]

  4. Sequence-aware vs session-aware vs session-based session session session session session

    session 2021/11/2 2021/11/6 session-based sequence-aware ੲͷηογϣϯͷ৘ใ΋༻͍ͯߦಈ༧ଌΛߦ͏৔߹Λɼsession-awareɼಉҰsession಺ͷ৘ใͷΈΛ༻͍ͯߦಈ༧ଌΛߦ͏৔ ߹Λɼsession-basedͱݺͿɽ·ͨɼҰൠʹsessionΛ੾Βͣաڈͷߦಈ͔Β༧ଌΛߦ͏৔߹Λɼsequence-awareͱݺͿɽ session-aware [Ludwelg 2020] user 1
  5. ຊษڧձͰ΍Δ͜ͱ Session-awareͳϢʔβߦಈ༧ଌʹؔ͢Δݚڀͷ֓ཁͷ঺հ աڈͷϢʔβߦಈΛͲ͏࢖͏͔ ֘౰Ϣʔβͷ௚ۙͷηογϣϯ৘ใ ֘౰Ϣʔβͷ͜Ε·ͰͷશͯͷཤྺΛूܭͨ͠৘ใ ಛ௃ྔΤϯδχΞϦϯά SIGIR eCom 21 Data

    Challengeʹ͓͚Δσʔλͷѻ͍ͷ঺հ Session-basedͳϢʔβʔߦಈ༧ଌʹؔ͢Δίϯϖ ͦͷଞsession-basedͳ༧ଌʹ͍ͭͯͷݚڀྫͷ঺հ ಛʹσʔλͷલॲཧ΍࢖༻͍ͯ͠Δಛ௃ྔʹ஫໨͠ͳ͕Βɼ͜ΕΒʹ͍ͭͯઆ໌
  6. ಛ௃ྔͷ෼ྨ ߦಈಛ௃ྔ Ϣʔβʔಛ௃ྔ ಛ௃ྔ ߦಈಛ௃ྔ ʢintra-sessionʣ ߦಈಛ௃ྔ ʢinter-sessionʣ Ϣʔβʔߦಈ༧ଌͷಛ௃ྔ͸ʮߦಈಛ௃ྔʯʮϢʔβʔಛ௃ྔʯʮΞΠςϜಛ௃ྔʯʹେผ͞ΕΔ ຊௐࠪͰ͸ಛʹͲͷΑ͏ͳߦಈಛ௃ྔ͕༻͍ΒΕ͍ͯΔͷ͔ʹ஫໨ͯ͠ௐࠪ

    ΞΠςϜಛ௃ྔ ੑผɼ೥ྸɼɾɾ ঎඼໊ɼ஋ஈɼɾɾ ΫϦοΫɼɾɾ աڈͷ๚໰਺ɼɾɾ
  7. ໨࣍ 1. Session-aware recommendationͷ֓ཁ 2. Session-based/aware recommendationͷ༧ଌͰॏཁͳಛ௃ྔʹ͍ͭͯͷௐࠪ • SIGIR eCom

    21 Data Challenge • جຊߦಈಛ௃ྔͱಛ௃ྔબ୒ • ͍͔ͭ͘ͷྫ • Struggling search • Information Seeking Conversation • Churn Prediction • ͦͷଞ 3. ·ͱΊ
  8. ໨࣍ 1. Session-aware recommendationͷ֓ཁ 2. Session-based/aware recommendationͷ༧ଌͰॏཁͳಛ௃ྔʹ͍ͭͯͷௐࠪ • SIGIR eCom

    21 Data Challenge • جຊߦಈಛ௃ྔͱಛ௃ྔબ୒ • ͍͔ͭ͘ͷྫ • Struggling search • Information Seeking Conversation • Churn Prediction • ͦͷଞ 3. ·ͱΊ
  9. Session-awareͳਪનϞσϧͷ෼ྨͱಈ޲ɹ [Wang+ 2021] [Fang+ 2020] Session-based/aware recommendation Sequence-aware neural recommendation

    [Wang+ 2021] ఻౷తʹ͸kۙ๣๏΍Ϛϧίϑ࿈࠯ͳͲ͕༻͍ΒΕ͖͕ͯͨɼݚڀʹ͓͍ͯ͸ۙ೥͸χϡʔϥϧωο τϫʔΫΛ༻͍ͨ΋ͷ͕૿Ճ Session-based/aware recommendation
  10. Session-based/awareΞϧΰϦζϜͷpros/cons [Wang+ 2021] ݚڀͷத৺͸χϡʔϥϧωοτϫʔΫ ͔͠͠ɼݱঢ়ͦͷଞͷख๏͕χϡʔϥϧωοτʹྼޙ͍ͯ͠ΔΘ͚Ͱ͸ͳ͍ [Dacrema+ 2019] [Latifi+ 2021] Pros/consΛ͓͑ͯ͞͏·͘׆༻Ͱ͖Δͱྑ͍ؾ͕͢Δ

  11. ਪનΞϧΰϦζϜͷϦϙδτϦ/ϥΠϒϥϦ ۙ೥ͷਪનΞϧΰϦζϜ͕͙͢ʹ࢖͑ΔϥΠϒϥϦ͕͋ΔͷͰɼ͜ͷลͰ৭ʑࢼͯ͠ΈΔͱָ͍͔͠΋͠Εͳ͍ RecBole Sequential recommendation΋ؚΉ࣮ʹ70༨ΓͷϞσϧͱ28ͷσʔληοτ͕࢖༻ՄೳͳϥΠϒϥϦ SASRec΍GRU4Recͱ͍ͬͨϞσϧ͔ΒBERT4Rec·Ͱ https://github.com/RUCAIBox/RecBole Recommenders Microsoft͕ఏڙ͢ΔύοέʔδͰ30લޙͷϞσϧ͕࢖༻Մೳɽݹయతͳख๏͕ଟΊ https://github.com/microsoft/recommenders

    Session-rec Session-awareͳख๏΋ؚΊͨ25ͷϞσϧ͕࢖༻ՄೳͳϦϙδτϦ https://github.com/rn5l/session-rec Tutorial on Sequence-Aware Recommender Systems RecSys 2018ͰߦͳΘΕͨsequence-awareͳhands-on͕·ͱΊΒΕͨϦϙδτϦ https://github.com/mquad/sars_tutorial
  12. Session-based/awareͳ༧ଌʹ࢖͑Δσʔληοτ [Wang+ 2021] σʔληοτͰ͸click stream͔Βҙຯͷ͋Δجૅతͳߦಈσʔλʹม׵͞Ε͍ͯΔ ॳظతʹ͸͜ΕΒͷσʔληοτͰͲͷΑ͏ͳಛ௃ྔ͕࡞੒͞Ε͍ͯΔͷ͔ݟͯΈΔͱࢀߟʹͳΔ͔΋͠Εͳ͍ [Wang+ 2021]

  13. Session-aware RecommendationͰॏཁͳཁૉʢ1/3ʣ [Fang+ 2020] [Fang+ 2020] Fang+ 2020͸ɼෳ਺ͷsequential recommendationͷݚڀΛൺֱͨ݁͠Ռɼӈͷ දͷཁૉ͕ॏཁͰ͋ΔͱΘ͔ͬͨͱओு

    ͜Ε͸ͦΕͧΕԼਤͷsequential recommendationͷֶशͷϑϩʔʹରԠ
  14. Input Side Information ঎඼ಛ௃ྔɿ঎඼ͷΧςΰϦʔɼࣸਅɼઆ໌ͳͲ σʔλͷগͳ͞΍cold-startʹΑΔੑೳͷྼԽΛ؇࿨ [Wang+ 2016] [Zhang+ 2019] etc

    ߦಈಛ௃ྔɿdwell timeͳͲ Dwell timeͷ௥Ճ͸GRU4RecͷੑೳΛେ෯ʹվળ [Hidasi+ 2016] Behavior Type click, purchase, collectͳͲ Ϣʔβʔͷ௕ظͷᅂ޷ੑΛΑ͘ଊ͑Δͷ͸ߪങߦಈ͕ͩͦͷଞͷߦಈಛ௃΋୹ظͷᅂ޷ੑΛଊ͑Δͷʹ໾ཱͭ [Gao+ 2019] etc Repeated Consumption Ϣʔβʔͷ܁Γฦ͠ͷߪങߦಈΛߟྀͨ͠΄͏͕༧ଌʹΑ͍ [Ren+ 2019] recencyʢ࠷ऴߪങ͔Βͷ࣌ؒʣ͕repeated consumptionͷ༧ଌʹͱͬͯॏཁ [Anderson+ 2014] loyaltyΛද͢ಛ௃ྔΛ௥Ճ͢Δ͜ͱͰ༧ଌੑೳ͕޲্ [Wan+ 2018] RNN͕kNNʹਪનλεΫͰෛ͚Δͷ͸ɼrepeated consumptionΛ͏·͘ϞσϦϯάͰ͖ͳ͍͔Βʁ[Hu+ 2020] Session-aware RecommendationͰॏཁͳཁૉʢ2/3ʣ [Fang+ 2020]
  15. Data processing Embedding Design sessionͷຒΊࠐΈΛࣄલֶश͢ΔͳͲʢsession information embeddingʣ[Wu+ 2017] Data Augmentation

    NݸͷΠϕϯτ͔ΒͳΔηογϣϯ͔Βk(1~N)൪໨·ͰͷΠϕϯτ͔ΒͳΔNݸͷηογϣϯΛ࡞੒ [Tan+ 2016] Model Structure Incorporating Attention Mechanisms AttentionϞσϧ͸طଘϞσϧͷੑೳΛେ෯ʹ྇կ [Li+ 2017] [Kang+ 2018] [Zhang+ 2019] Adding Explicit User Representation User embedded models Ϣʔβʔ৘ใͷຒΊࠐΈΛՃ͑Δ͜ͱͰੑೳ͕޲্ [Tang+ 2018] ͨͩ͠୯ͳΔϢʔβʔ৘ใͷ׆༻͚ͩͰ͸cold-startͷ໰୊΍ɼϢʔβʔͷؔ৺ͷมԽ΁ͷରԠͷ໰୊͕ൃੜ User recurrent models Ϣʔβʔ৘ใΛ࣌ܥྻతʹϞσϧԽ͢Δ͜ͱͰGRU4RecΛ3.5%վળ [Quadrana+ 2018] Session-aware RecommendationͰॏཁͳཁૉʢ3/3ʣ [Fang+ 2020]
  16. Session Length Long sessionsʢ10 ~ʣɿҰ൪৘ใྔ͕ଟ͍͕ͦͷ෼ϊΠζ΋ଟ͘ɼΠϕϯτؒͷؔ܎΋ෳࡶ Medium sessionsʢ4 ~ 9ʣɿ࠷΋ଟ͍έʔεͰɼ͍͔ʹͯ͠ΑΓ๛͔ͳจ຺Λ׆༻Ͱ͖Δ͔͕େࣄ User

    Information Non-anonymous sessionsɿϢʔβʔͷաڈͷ৘ใΛར׆༻Ͱ͖ΔͷͰɼ͍͔ʹ௕ظͷᅂ޷ੑΛଊ͑Δ͔͕՝୊ Anonymous sessionsɿݱࡏηογϣϯͷ৘ใ͚͔ͩΒɼ͍͔ʹᅂ޷ੑΛଊ͑Δ͔͕՝୊ Session-data Structure Single-level session dataɿิॿ৘ใͷͳ͍anonymous sessionsͷ͜ͱͰɼcold-start΍data sparsityͷ໰୊΁ͷରॲ͕՝୊ Multi-level session dataɿ Adding Explicit User Representation User embedded models Ϣʔβʔ৘ใͷຒΊࠐΈΛՃ͑Δ͜ͱͰੑೳ͕޲্ [Tang+ 2018] ͨͩ͠୯ͳΔϢʔβʔ৘ใͷ׆༻͚ͩͰ͸cold-startͷ໰୊΍ɼϢʔβʔͷؔ৺ͷมԽ΁ͷରԠͷ໰୊͕ൃੜ User recurrent models Ϣʔβʔ৘ใΛ࣌ܥྻతʹϞσϧԽ͢Δ͜ͱͰGRU4RecΛ3.5%վળ [Quadrana+ 2018] Session-aware/based Recommendationʹ͓͚Δ՝୊ [Fang+ 2020]
  17. Session-aware recommendationͷݱঢ় session-awareͷݚڀࣗମ͕·ͩᴈ໌ظ session-awareͷݚڀ͸·ͩগͳ͘ɼͦͷେ൒͕2014೥Ҏ߱ʹ ग़൛͞Εͨ΋ͷʢACM Digital Library಺ʣ[Quadrana+ 2018] աڈͷཤྺ͕௕͍΄Ͳੑೳ޲্ʢw/๛෋ͳσʔλʣ [Pi+

    2019] AUC 0.02ͷվળ͕අ༻ʹݟ߹͏͔͸ཁݕ౼ ~ 1000ͷܥྻ௕͕༻͍ΒΕΔ latencyͱstorage cost͕Ϣʔβʔߦಈܥྻͷ௕͞ʹґଘͯ͠ઢ ܗʹ૿Ճ [Pi+ 2019] աڈ50ͷϢʔβʔߦಈΛอ࣋ [Zhout+ 2018] աڈ100/200/1000ͷϢʔβʔߦಈΛอ࣋ [Pi+ 2019] Session-based΍ݹయతख๏ʹྼޙ͢Δ͜ͱ͕͋Δ [Dacrema+ 2019] [Latifi+ 2020] Session-aware͸ΑΓ๛͔ͳ৘ใΛ༻͍ΔͷͰ࠷దԽ͕೉͍͠ ༧ଌର৅ʹ࣌ؒతʹۙ઀͢Δߦಈ͕Ұ൪ॏཁ [Chen+ 2019] [Quadrana+ 2018] [Pi+ 2019] [Zhout+ 2018] [Pi+ 2019]
  18. session-aware vs session-based session session session session session session 2021/11/2

    2021/11/6 session-based ΄ͱΜͲͷ৔߹session-aware΋ಛ௃ྔͷҙຯͰ͸session-basedͱมΘΒͳ͍ ͲͪΒ΋session͕ೖྗͳͷ͸มΘΒͣɼͦͷ਺΍ͦΕΛѻ͏ΞϧΰϦζϜ͕ҧ͏͚ͩͳͨΊ →ɹsession-based/awareʹؔΘΒͣ༧ଌʹॏཁͦ͏ͳಛ௃ྔΛௐࠪ͢Δ session-aware [Ludwelg 2020] user 1
  19. ໨࣍ 1. Session-aware recommendationͷ֓ཁ 2. Session-based/aware recommendationͷ༧ଌͰॏཁͳಛ௃ྔʹ͍ͭͯͷௐࠪ • SIGIR eCom

    21 Data Challenge • جຊߦಈಛ௃ྔͱಛ௃ྔબ୒ • ͍͔ͭ͘ͷྫ • Struggling search • Information Seeking Conversation • Churn Prediction • ͦͷଞ 3. ·ͱΊ
  20. SIGIR eCom 21 Data Challenge SIGIR eCom 21 ͰCoveoʹΑͬͯ։࠵͞Εͨίϯϖ 1ͭͷηογϣϯͷ৘ใ͔ΒϢʔβʔߦಈΛ༧ଌ͢Δ

    ࢀՃऀ͸ҎԼͷ2ͭͷλεΫʹऔΓ૊Ή ਪનλεΫʢsession-based recommendationʣ ॳΊͷkݸͷΠϕϯτ͔Βಉηογϣϯ಺Ͱͷ঎඼ΛΊ͙ΔߦಈΛ༧ଌ ҙਤ༧ଌλεΫʢcart-abandonment predictionʣ Χʔτʹ঎඼͕ೖΕΒΕͨޙɼಉηογϣϯ಺Ͱ঎඼͕ߪೖ͞Ε͔ͨΛ༧ଌ
  21. σʔληοτ 36MΠϕϯτΛؚΉσʔλͰҎԼͷ3ͭͷcsvϑΝΠϧ͔ΒͳΔ browsing_train.csv ঎඼ϖʔδͰͷϢʔβʔΠϕϯτͷϩάͱඇ঎඼ϖʔδʢFAQͱ͔ʣͰͷӾཡߦಈ Ӿཡ͔Πϕϯτ͔ɼΧʔτ΁௥Ճ͔ߪೖ͔঎඼ͷৄࡉΛݟΔ͔ͳͲ search_train.csv ηογϣϯʹؔΘΔݕࡧΠϕϯτ ݕࡧ୯ޠͷΫΤϦϕΫτϧɼݕࡧ݁Ռͷ঎඼ͷϦετɼΫϦοΫ͞Εͨ঎඼ͳͲ sku_to_content.csv ঎඼ͷϝλσʔλ৘ใ

    Ձ֨ɼ঎඼ΧςΰϦʔɼ঎඼ը૾ͱ঎඼ͷઆ໌จΛදݱ͢ΔϕΫτϧͳͲ ৄࡉ͸ϦϙδτϦΛࢀর https://github.com/coveooss/SIGIR-ecom-data-challenge
  22. Accepted papers 1 . Transformers with Multi-modal Features and Post-fusion

    Context for E-commerce Session- based Recommendation [Gabriel Moreira, Sara Rabhi, Ronay Ak, Md Yasin Kabir and Even Oldridge] Transformer-XLͱXLNetͷΞϯαϯϒϧ 2 . Comparison of Transformer-Based Sequential Product Recommendation Models for the Coveo Data Challenge [Elisabeth Fischer, Daniel Zoller and Andreas Hotho] SASRecɼBERT4RecɼKeBERT4Recͷ૊Έ߹Θͤ 3 . Utilizing Graph Neural Network to Predict Next Items in Large-sized Session-based Recommendation Industry Data [Tianqi Wang, Zhongfen Deng, Houwei Chou, Lei Chen and Wei-Te Chen] KNN, s-KNN, SASRec, DynamicRec, SRGNNΛ࢖༻ 4 . Session-based Recommender System Using an Ensemble of Multiple NN Models with LSTM and Matrix Factorization [Yoshihiro Sakatani] LSTMͱߦྻ෼ղ 5 . Adversarial Validation to Select Validation Data for Evaluating Performance in E-commerce Purchase Intent Prediction [Shotaro Ishihara, Shuhei Goda and Hidehisa Arai] GBDT, NN, ϧʔϧϕʔεͷΞϯαϯϒϧ 6 . A Session-aware DeepWalk Model for Session-based Recommendation [Kaiyuan Li, Pengfei Wang and Long Xia] page-viewͱproduct interactionͦΕͧΕͰάϥϑΛ࡞ͬͯDeepWalkΛ࢖༻ͯ͠ϊʔυຒΊࠐΈΛͯ͠ɼGRU
  23. ৘ใ͕গͳ͍σʔλͷѻ͍ ग़ݱස౓/interaction͕গͳ͍঎඼ͷѻ͍ [1] Interaction͕5ճҎԼͷ঎඼Λಉ͡঎඼IDͱͯ͠·ͱΊΔ [2] Ұ౓͔͠interaction͕ͳ͍ΞΠςϜΛআڈ [4] ग़ݱස౓͕8ճҎԼͷ঎඼͸single eventͱͯ͠·ͱΊΔ page-viewͷΈͷURLͷѻ͍

    [1] page-viewͷURLΛԾ૝తͳ঎඼ͱͯ͠ѻ͏ ɹʢඇ঎඼ϖʔδӾཡʢ70%ʣ͸঎඼Ӿཡʢ28%ʣ΍ݕࡧΫϦοΫ( 2%)ΑΓଟ͍ʣ [3] ঎඼ͱͷinteractionͷͳ͍page-viewΠϕϯτͷআڈ ৘ใ͕গͳ͍sessionͷѻ͍ [2] Ұ౓͔͠interaction͕ͳ͍sessionΛআڈ [3] Ұͭͷ঎඼ؚ͔͠ΜͰ͍ͳ͍sessionΛςετηοτ͔Βআڈ ॏෳ͢ΔΠϕϯτͷѻ͍ [1] ঎඼ͱͷ࠷ॳͷinteractionͷΠϕϯτ͚ͩΛ࢒͢ʢશମͷ13%͕ॏෳʣ [4] ಉҰηογϣϯ಺ͰಉURLʹରͯ͠ى͖Δpage-viewΛ࡟আ [6] ॏෳͨ͠Ϣʔβʔinteraction͸࠷ޙͷ͚ͩ࢒͢
  24. ঎඼ಛ௃ྔ [1] όέοτ঎඼Ձ֨Λ঎඼ʢαϒʣΧςΰϦͷฏۉͰׂͬͨྔ ಛఆ঎඼΁ͷinteractionͷ਺ɼಛఆͷ঎඼ͷৄࡉΛݟ͔ͨɼಛఆͷ঎඼ΛΧʔτʹೖΕ͔ͨΛಛ௃ྔͱͯ͠௥Ճ [4] hashed product IDͷ਺ɼhashed categoriesɼ֤ηογϣϯͷ࠷ॳͷ঎඼ɼ࠷ॳͷ঎඼ͷΧςΰϦɼϝλσʔλͷ༗ແ Χ΢ϯτಛ௃ྔ

    [4] Πϕϯτͷྦྷੵ࿨ɼ֤঎඼Πϕϯτؒͱ঎඼ΠϕϯτޙͷϖʔδϏϡʔͱݕࡧΠϕϯτͷճ਺΋ಛ௃ྔͱͯ͠࢖༻ ࣌ؒಛ௃ྔ [1] ঎඼͕ॳΊͯݟΒΕ͔ͯΒͷ࣌ؒɽϩάʢ೔ʣɼ࣌ؒɼ༵೔ [4] िɼ೔ɼ࣌ؒͱɼि຤͔൱͔Λಛ௃ྔͱͯ͠࢖༻ ͦͷଞಛ௃ྔ [1] ݕࡧςʔϒϧͰར༻ՄೳͳݕࡧΫϦοΫΠϕϯτ΋Πϕϯτʹ௥Ճ ΠϕϯτλΠϓɼ঎඼ʢαϒʣΧςΰϦɼόέοτՁ֨ɼ঎඼IDΛྡ઀IDͷͱͯ͠·ͱΊΔ browsing interaction, search query information, categorical meta dataΛͻͱͭͷΠϕϯτͱͯ͠·ͱΊΔ [4] Πϕϯτͷ௕͞΋ಛ௃ྔͱͯ͠࢖༻ ৽نಛ௃ྔͷ࡞੒
  25. GBDTͷ༧ଌʹޮ͍͍ͯͨಛ௃ྔ [5] ঎඼ΛΧʔτʹೖΕ͔ͨ൱͔ɼ͍࣍Ͱ঎඼ͷৄࡉΛݟ͔͕ͨ༧ଌʹޮ͍͍ͯͨ ॏཁͳಛ௃ྔ

  26. ଟ͘ͷάϧʔϓ͕ɼॏෳ͢Δಛ௃ྔͷू໿΍ɼ਺ͷগͳ͍ಛ௃ͷ࡟আɾม׵Λ͓͜ͳ͍ͬͯͨ ʮ๲Ε্͕Δσʔλʹ೗Կʹରॲ͢Δ͔ʁʯ͸Ϗδωε্Ұͭͷ՝୊Ͱ͋Δͱೝࣝ Ծʹ۩ମతͳ༧ଌର৅΍Ϣʔεέʔε͕ఆ·Γ͖͍ͬͯͳ͍৔߹ɼ༧ଌೳྗͷߴ͔ͭ͘େ͖͘ॖ໿͞Εͨಛ௃Λ ଈ࠲ʹ࡞Δͷ͸೉͍͠ͱࢥΘΕΔ େ఍ͷ৔߹શମͷ1~2ׂͷಛ௃Λอ͍࣋ͯ͠Ε͹શମͷ8~9ׂΛઆ໌Ͱ͖Δʢ͜ͱ͕ظ଴͞ΕΔʣ ·ͣ͸ɼجຊతͳಛ௃ྔͷ͏ͪ৘ใͷ৑௕ੑ͕͋Δ΋ͷΛ࡟ݮ͢Δ͜ͱͰɼσʔλอ࣋ͷίετΛݮΒ͢͜ͱ͕ Մೳ͔΋͠Εͳ͍ SIGIR e-commerce Data

    challengeʹ͍ͭͯͷௐ͔ࠪΒͷࣔࠦ
  27. ͋Δ༗໊ͳECαΠτͷ2018೥ͷ2ϲ݄෼ͷϒϥ΢δϯάσʔλ͔Β৽͍͠σʔληοτΛ࡞੒ʢData Challengeͷσʔληοτʣ ϢʔβʔߦಈΛpage view, detail ,add, remove, purchase, clickͷ6ͭʹ෼ྨ ʮߪೖʯؚ͕·Ε͍ͯΔܥྻ͸ɼͦͷ௚લ·Ͱͷ෦෼ܥྻͷΈΛ࢖༻

    ͜ͷσʔληοτͰϢʔβʔ͕঎඼Λߪೖ͔ͨ͠൱͔Λ༧ଌ͠ɼ͔ͳΓجຊతͳಛ௃ྔ͚ͩͰ͋Δఔ౓༧ଌՄೳͰ͋Δ͜ͱΛओு લॲཧͳͲ 5 clickҎ্155 clickҎԼͷσʔλͷΈ࢖༻ ʮߪೖʯ͕͓͜Δܥྻͱى͜Βͳ͍ܥྻͷ਺͕ۉߧ͕औΕΔΑ͏ʹαϯϓϦϯά ಛ௃ྔΛ࡞੒ͯ͠XGBoostͰ༧ଌ͢Δ৔߹ʢhand-craft feature engineeringʣͱneural networkͰ༧ଌ͢Δ৔߹ʢneural networkʣͦΕͧΕݕ౼ʢຊൃදͰ͸ޙऀͷΈઆ໌ʣ Shopper Intent Prediction from Clickstream E-commerce Data with Minimal Browsing Information [Requena+ SciRepo 2020] (Coveo)
  28. hand-craft feature engineering k-gram ϢʔβʔߦಈΛฒ΂ͨ΋ͷͷk-gram ྫɿ2-gramʮ34ʯʮ12ʯ K-gram୯ମ͚͔ͩΒ͸ϢʔβʔߦಈΛ༧ଌ͢Δͷ͸೉ͦ͠͏ horizontal visibility graph

    motifs (HVGM) ϊʔυΛҰྻʹฒ΂ͨແ޲άϥϑͰɼk-gramΑΓߴ࣍ͷύλʔϯʢZ1 ~ Z6ʣ͕ܭࢉՄೳ ͜ΕΒͷ֬཰ʹ͍ͭͯͷΤϯτϩϐʔʢhzʣ΋ܭࢉ ͜ΕΒͷಛ௃ྔΛ࢖ͬͯXGboostͰߪೖ͔ͨ͠൱͔Λ༧ଌ Shopper Intent Prediction from Clickstream E-commerce Data with Minimal Browsing Information [Requena+ SciRepo 2020] (Coveo)
  29. ॏཁͳಛ௃ྔ/GBDT add (3) ͚ͩͰͳ͘add-view (31) ͕େࣄ ͨͩͷӾཡߦಈͰ͋Δview-detail (12) ͕େࣄ ϖʔδؒભҠview-view

    (1)΍঎඼ؒભҠdetail-detail (2) ͸͋Δ͕ݕࡧ search (6) ͸গͳ͍ ॏཁͳಛ௃ྔ/SHAP view-detail (12) ΍ detail (2)΍search (6)͕ߴ͍ͱߪങʹࢸΒͳ͍ (SHAP஋͕negative) ͜ͷͱ͖Z1͕ߴͯ͘hz͕௿͍ˠ͜ͷΑ͏ͳϢʔβʔߦಈ͸୯ௐ ྫɿview-view-view-detail-view-detail (111212)ͷ৔߹ʢϢʔβʔ͕঎඼Λݟ͚ͭΔ໨ తͰϖʔδΛ୳ͯ͠໭ͬͨΓΛ͍ͯ͠Δʣ view-view (11) ΍detail (2) ͕ߴ͘ͳΔ Z1 ͕2ͭͰ Z4͕1ͭͳͷͰhz͸த͘Β͍ʹͳΔ ঎඼ϖʔδΛ๚Εͣଟ͘ͷϖʔδΛ๚ΕΔ৔߹Ͱ (high view P(1))ɼ༷ʑͳߦಈ͔ΒӾཡʹ ࢸΔ৔߹ (low view-view P(1, 1) and high detail-view P(2, 1), add-view P(3, 1))ɼ঎඼ߪೖ ଞͷ঎඼ϖʔδ͔Β௚઀ඈͿ৔߹ (high detail-detail P(2, 2))΋ߪങʹͭͳ͕Δ Τϯτϩϐʔhz͕ߴ͍ύλʔϯͱͯ࣍͠ͷΑ͏ͳ΋ͷ͕͋Δ view-add-detail-view-add-view (132131) ͕ͩ detail P(2) ͱ view-view P(1, 1)͕௿͘ɼ view P(1) ɼadd P(3), ɼadd-view P(3, 1) ɼdetail-view P(2, 1)͕ߴ͍ ༧ΊԿ͕΄͍͔ܾ͠·͍ͬͯΔΑ͏ͳڍಈͰ͋Γɼ͜Ε͕ߪೖʹͭͳ͕Δͷ͸ଥ౰ Shopper Intent Prediction from Clickstream E-commerce Data with Minimal Browsing Information [Requena+ SciRepo 2020] (Coveo)
  30. ͋Δe-commerceͷσʔληοτΛ༻͍ͯsequence-aware recommendationʹ͓͍ͯԿ͕ॏཁ͔Λௐࠪ ໿95ͷಛ௃ྔΛ࡞੒͠ɼGain ratioɼChi-squaredɼInformation gain ratioɼGini indexʹΑͬͯॏཁ౓ΛධՁ શಛ௃ྔ͸HPࢀর https://rn5l.github.io/session-rec/umuai/ Exploring

    Session-Awareness in E-Commerce [Ludewig 2020] [Jannach+ 2017]
  31. SIGIR e-commerce Data challengeɼ[Requena+ 2020]ɼ [Ludewig 2020]ͰࢀߟʹͳΔͷ͸ɼૉ๿ͳಛ௃ྔ࡞੒ → ಛ௃ྔબ୒ͱ͍͏ϓϩηε Ϣʔβʔߦಈ෼ੳʹ͓͍ͯ͸ɼ·ͣ͸͋Δಛఆͷ໰୊ʹରͯ͠ૉ๿ͳಛ௃ྔΛ࡞੒͠ɼ࣮ࡍʹ༧ଌ໰୊Λͱ͍ͯͦ

    ͷد༩ΛΈΔ͜ͱͰɼॏཁͳಛ௃ྔΛܦݧతʹධՁ͍ͯ͘͠ͷ͕ॏཁͩͱࢥΘΕΔ ॏཁͳಛ௃ྔͷԾઆ͕ੵΈ্͕Ε͹ɼΑΓཉ͍͠৘ใͷղ૾౓্͕͕ΔͷͰɼઌߦݚڀɾࣄྫͷαʔϕΠ΋ޮ཰ Խ͠ɼԾઆ͕ΑΓڧԽ͞Εɽɽͱ͍͏ਖ਼ͷϑΟʔυόοΫϧʔϓ͕ճΔ اۀͰϢʔβʔߦಈ༧ଌʹܞΘ͍ͬͯΔํʑ΋݁ہͦΕ͕େࣄͩͱ͍͍ͬͯΔ [Challenges, Best Practices and Pitfalls in Evaluating Results of Online Controlled Experiments, KDD 2019] ͕ͨͬͯ͠ɼॏཁͳಛ௃ྔʹ͍ͭͯͷԾઆݕূΛճ͢࢓૊ΈࣗମΛͭ͘ΓͦΕΛճ͢͜ͱ͕ॏཁͰ͸ͳ͍͔ͱߟ͑Δ ͜͜·Ͱͷௐ͔ࠪΒͷࣔࠦ
  32. GRUΛ࣋ͭDNNʹΑΔϚϧνλεΫֶशͰuser intent prediction ๲େͳσʔλʹର͢Δ౷ܭతͳ෼ੳͷ݁ՌɼҎԼͷಛ௃ྔ͕Ϣʔβʔͷߪങ༧ଌʹॏཁͰ͋Δ͜ͱΛൃݟ Ϣʔβʔͷجຊతͳ৘ใ ੑผɼ೥ྸɼաڈҰ೥ͷߪೖϨϕϧɼॅΜͰ͍Δ஍ҬͳͲ Ϣʔβʔߦಈʢ೔୯Ґɼ݄୯Ґʣ աڈ਺ճͷߪങߦಈɼొ࿥͔Βͷ೔਺ɼ࠷େ࿈ଓΞΫςΟϒ೔਺ɼաڈ਺ճͷ׆ಈͷมԽɼηʔϧεϓϩϞʔγϣϯ࣌ͷߪങߦಈ ਺஋ಛ௃ྔΛ͢΂ͯΧςΰϦΧϧಛ௃ྔʹม׵ One-hotͩͱεύʔεʹͳΔͷͰɼຒΊࠐΈ

    TPG-DNN: A Method for User Intent Prediction Based on Total Probability Formula and GRU Loss with Multi-task Learning [Jiang+ ACM 2017] (Alibaba)
  33. ΤοδσόΠεʹ͓͚ΔDNNʹΑΔuser intent prediction swipe-interactive behaviorɼtap-interactive behaviorɼbrowse-interactive behaviorͷߦಈάϧʔϓΛ࡞੒ swipe-interactive behavior open

    page, leave page, swipe, tap͕جຊߦಈʢtapҐஔ΍swipe࢝ऴҐஔ΍ܧଓ࣌ؒ΋ه࿥ʣ time duration of a swipe, time gap between two actions, positional coordinates of actions (tap position X/Y, swipe start position X/Y, swipe end position X/Y and swipe length on X/Y) page indices, action indices and swipe directions tap-interactive behavior timestampɼpage indexɼevent indexɼbutton index browse-interactive behavior browse, search, collect, add to cart, purchaseͷ̐ͭͰɼ֤ߦಈʹରͯ࣍͠ͷ6ͭͷಛ௃Λநग़ type index, top category index, leaf category index, page index, page stay time and timestamp ༧ଌ݁Ռʹج͍ͮͯɼϙοϓΞοϓ޿ࠂɼϓογϡ௨஌ʹ͍ͭͯෳ਺ͷઓུΛߟ͑ABςετ ఏҊख๏͸Ϣʔβʔͷreal-timeͳᅂ޷ੑΛ͏·͘ଊ͍͑ͯΔ͜ͱΛ࣮ূ EdgeDIPN: a Unified Deep Intent Prediction Network Deployed at the Edge [Guo+ VLDB 2020] (Alibaba, Peking University)
  34. جຊతͳߦಈಛ௃ྔΛ༻͍ͨɼݕࡧʹ͓͚Δຬ଍౓ͷ༧ଌ Deep Sequential Models for Task Satisfaction Prediction / User

    Interaction Sequences for Search Satisfaction Prediction [Mehrotra+ CIKM/SIGIR 2017] (UCL, Microsoft)
  35. ୳ࡧతݕࡧߦಈͱۤઓ͍ͯ͠Δ࣌ͷݕࡧߦಈͷफ़ผ ಛ௃ྔ Struggling or Exploring? Disambiguating Long Search Sessions [Hassan+

    WSDM 2014] (Microsoft) ۤઓ͍ͯ͠Δ͍Δͱ͖ͷݕࡧߦಈ ۤઓ͍ͯ͠Δͱ͖͸࠷ॳͷΫΤϦͱͷྨࣅ౓͕ߴ͍͕୳ࡧͷ৔߹͸গͣͭ͠ྨࣅ౓͕Լ͕Δ ୳ࡧͷ৔߹͸ɼޙ൒ͷηογϣϯʹͳΔ΄ͲΫϦοΫ਺͕૿͑Δ
  36. ۤઓ͍ͯ͠Δ͕࠷ऴతʹॴ๬ͷ݁ՌΛಘΒΕͨ࣌ͷݕࡧߦಈ [Odijk+ 2015] ΫϦοΫ͕গͳ͍ɼΫΤϦΛΑΓ۩ମతͳ΋ͷʹมߋ͢ΔͳͲ ݕࡧ݁Ռʹຬ଍͢Δ͔൱͔ͷ༧ଌʹ͸࣍ͷΫΤϦͱͷྨࣅ౓ͳͲͷಛ௃ྔ͕େࣄ [Diriye+ 2012] ΫΤϦͷมߋ΍ݕࡧ݁ՌͷΫϦοΫ͸ݕࡧͷࠔ೉͞΍ݕࡧʹର͢ΔϑϥετϨʔγϣϯΛද͢ [Aula+ 2010]

    [Allan+ 2010] ΫϦοΫΛڬ·ͣΫΤϦ͕Կ౓΋มߋ͞Ε͍ͯΔͱ͖͸ݕࡧ݁ՌʹෆຬΛ͍͍ͩͯΔ [White+ 2009] E-commerceͷݕࡧʹ͓͍ͯɼΫΤϦ͸มߋ͞ΕΔͨͼʹ௕͘ͳΓɼΫΤϦมߋ͸ΫϦοΫ਺ͷ૿Ճͱ࠷ऴతͳߪങ΁ͱͭͳ ͕ͬͨ ʢҎԼͷಛ௃ྔͰΫΤϦมߋͷ༧ଌΛߦͬͨʣ[Hirsch+ 2020] ݕࡧߦಈͱຬ଍/ෆຬ଍/ۤઓ
  37. conversational assistantͱͷϚϧνλʔϯͷձ࿩ʹ͓͚Δuser intent prediction લॲཧ Ϣʔβʔͷ࣭໰ҙਤΛͦͷ಺༰͔Β12ͷΫϥεʹϥϕϧ෼͚ Greetings/Gratitude, Junk, Others͸༧ଌʹߩݙ͠ͳ͍ͷͰআ֎ ͜ͷϥϕϧͷ૊Έ߹Θͤʹରͯ͠Ϣʔβʔߦಈ͔Β༧ଌΛߦ͏

    ग़ݱස౓͕গͳ͍ϥϕϧͷ૊Έ߹Θͤ͸ͦΕΛߏ੒͢Δ͍ͣΕ͔ͷϥϕϧʹϥϯμϜʹஔ͖׵͑ ϥϕϧͷ૊Έ߹ΘͤͰग़ݱස౓͕େ͖͘ҟͳΔʢτοϓ32ͷϥϕϧͷ૊Έ߹Θ͕ͤશମͷ90ˋΛ઎ΊΔʣ ྫɿCQ+FD+IR+RQ → CQ User Intent Prediction in Information-seeking Conversations [Qu+ CHIIR 2019] (U M Amherst, RMIT U, Alibaba) ࢀߟ
  38. ಛ௃ྔ Ϣʔβʔͷൃ࿩ʹ͍ͭͯɼ಺༰ʹؔ͢Δ΋ͷʢContent/Conʣɼର࿩ͷߏ଄ʹؔ͢Δ΋ͷʢStructure/Strʣɼײ৘ʹؔ͢Δ΋ͷ ʢSentiment/senʣͦΕͧΕʹରͯ͠ಛ௃ྔΛ࡞੒ ߏ଄ʹؔ͢Δಛ௃ྔ͕Ϣʔβʔҙਤͷ༧ଌʹॏཁ ಛʹɼൃݴ͕Ͳ͜Ͱൃੜ͔ͨ͠ʢAbsolute Position/AbsPosɼNormalized Position/NormPosʣͳͲ͕ॏཁͩͬͨ ಺༰ʹؔ͢Δ΋ͷͰ͸࠷ॳͷൃ࿩ͷྨࣅ౓ʢInitial Utterance Similarity/InitSimʣ΍ର࿩ͷྨࣅ౓ʢDialog

    Similarity/DigSimʣ͕ॏཁ 15Ґ͔Β࠷ԼҐ·Ͱ͸͢΂ͯʮ͋ΔΩʔϫʔυΛؚΜͰ͍͔ͨʁʹؔ͢Δಛ௃ྔʯ User Intent Prediction in Information-seeking Conversations [Qu+ CHIIR 2019] (U M Amherst, RMIT U, Alibaba) ࢀߟ
  39. Frequency Ϣʔβʔ͕ͲΕ͚ͩසൟʹαʔϏεΛར༻͢Δ͔ Recency Ϣʔβʔ͕࠷ޙʹαʔϏεΛར༻͔ͯ͠ΒͲΕ͚ͩܦ͔ͭ Increasing/Decreasing Ϣʔβʔͷར༻ස౓͸૿͍͑ͯΔ͔ɾݮ͍ͬͯΔ͔ Churn Prediction (1/3) https://www.iguazio.com/sessions/product-madness-an-aristocrat-co-on-predicting-1st-day-churn-in-real-time/

    ར༻ස౓ ࣌ؒ
  40. ήʔϜʹ͓͚ΔϢʔβʔ཭୤༧ଌ Churn Prediction (2/3) [Xi+ 2019 A Latent Feelings-aware RNN

    Model for User Churn Prediction with Behavioral Data]
  41. Telco Customer ChurnΛ༻͍ͨɼ௨৴αʔϏεղ໿༧ଌ Churn Prediction (3/3) EDA ಛ௃ྔͷ෼ੳ [AI ʹΑΔୀձ཈ࢭ

    γϦʔζ ʮࣄۀܦӦ΁ͷ AI ׆༻ʯᶃ ࡾඛUFJϦαʔν&ίϯαϧςΟϯά]
  42. Ϣʔβʔͱ঎඼ͷinteractionͷಛ௃ྔΛೖΕΔ͜ͱͰ༧ଌਫ਼౓͕͕͋ͬͨ [Zhang+ 2020] ಛఆͷ঎඼ΛΫϦοΫͨ͠ճ਺ɼಛఆͷ঎඼ͷuser collectionͷ਺ɼಛఆͷ঎඼ΛΧʔτʹೖΕͨ਺ ͦͷଞ

  43. ໨࣍ 1. Session-aware recommendationͷ֓ཁ 2. Session-based/aware recommendationͷ༧ଌͰॏཁͳಛ௃ྔʹ͍ͭͯͷௐࠪ • SIGIR eCom

    21 Data Challenge • جຊߦಈಛ௃ྔͱಛ௃ྔબ୒ • ͍͔ͭ͘ͷྫ • Struggling search • Information Seeking Conversation • Churn Prediction • ͦͷଞ 3. ·ͱΊ
  44. ݁࿦ ༧ଌʹͱͬͯԿ͕ॏཁͳಛ௃ྔ͕Կ͔͸༧ଌର৅/υϝΠϯʹґଘ͢Δ ߪങߦಈͷ༧ଌʹ͸ߪങʹؔ࿈͢Δߦಈಛ௃ྔ͕ޮ͍ͯ͘Δ͠ɼ཭୤཰ͷ༧ଌʹ͸recency͕ޮ͘ ۜߦआೖΕͷ༧ଌͳΒआ͕ۚ͋Δ͔͕ޮ͍ͯ͘Δ͠ɼήʔϜͷ཭୤༧ଌʹ͸ͲΜͳήʔϜΛ΍͔͕ͬͨޮ͘ Session-awareʹϢχʔΫͳಛ௃ྔͷٞ࿦͸͋·Γͳ͞Ε͍ͯͳ͍ҹ৅ Session-awareͳख๏͸݁ہsession͕جຊ୯ҐͳͷͰɼsession-basedͱൺ΂ͯѻ͏sessionͷ਺͕૿͚͑ͨͩ Session-awareͷݱࡏͷख๏ͷओྲྀ͸neural network͕ͩneural networkͰ͸ಛ௃ྔΛ࡞Γࠐ·ͳ͍ ίϯϖͰ͸৘ใ͕গͳ͍sessionͷ৘ใΛ͍͔ʹѻ͏͔͕ҰͭϙΠϯτͩͬͨҹ৅

    ొ৔ස౓͕গͳ͍΋ͷΛ࡟আͨ͠Γ৑௕ͳ৘ใΛ΋ͭ΋ͷΛ·ͱΊͨΓ ૉ๿ʹEDAΛ΍ͬͨ݁ՌΘ͔ͬͨ͜ͱͩͱ͍͏ͷ͕େࣄ
  45. ܥྻ৘ใͷอ࣋ͱτϨʔυΦϑ ৘ใྔ ѹॖ཰ ߦಈಛ௃ྔ ൚༻ੑ ੜσʔλ ྫɿclick جຊಛ௃ྔ ྫɿclick਺ υϝΠϯಛԽಛ௃ྔ

    ྫɿsnap_view ߴ ௿ গ ଟ ௿ ߴ ɾɾɾ ҰൠʹͲΕ͘Β͍ಛ௃ྔΛੜσʔλʹ͍ۙܗͰอ࣋͢Δ͔͸ଞͷ༷ʑͳཁૉͱτϨʔυΦϑͷؔ܎ʹ͋Δ͸ͣ Ϗδωε্ಛʹॏཁͳ෼ੳ࣠ຖʹͲ͜·ͰଥڠͰ͖Δ͔Λߟ͑Δ͜ͱͰɼͲͷཻ౓ͰσʔλΛอ࣋͢Δͷ͔͕ܾ·Γͦ͏
  46. େࣄͩͱࢥͬͨ͜ͱ ར༻໨తͷ໌֬Խ Կʹ༻͍Δ͔͕ఆ·Γ͖͍ͬͯͳ͍ͳΒੜσʔλɾجૅతͳߦಈಛ௃ྔΛ࣋ͭ֎ͳ͍ ఆ·͍ͬͯͳ͍৔߹ɼͱΓ͋͑ͣجૅతͳಛ௃ྔΛ࡞੒্ͨ͠Ͱ৑௕ͳಛ௃ྔΛ࡟আ͢Δ͘Β͍͸Ͱ͖ͦ͏ ग़ݱස౓ͷগͳ͍/ॏෳ͢Δaction/session/itemͷ࡟আ΍ू໿Ͱσʔλอ࣋ͷίετΛҰ࣍తʹԼ͛ΔͳͲ ಛ௃ྔબ୒ͷԾઆݕূαΠΫϧ Ͳͷಛ௃ྔ͕ޮ͔͘ɾ൚༻త͔͸݁ہ΍ͬͯΈͳ͍ͱΘ͔Βͳ͍ͷͰԾઆݕূΛճ͍ͯ͘͠࢓૊ΈࣗମΛ࡞Δͷ͕େࣄͦ͏ ·ͣ͸ԾઃΛཱͯͯૉ๿ͳಛ௃ྔΛ୔ࢁ࡞ΓɼGBDT΍random forestͳͲͰɼෳ਺ͷҟͳΔυϝΠϯͰਪ࿦͢ΔͳͲ ͦͷޙɼ૬ؔ΍SHAP஋ͳͲΛௐ΂ͯ൚༻తʹ༧ଌʹޮ͍͍ͯͦ͏ͳಛ௃ྔΛ୳͍ͯ͘͠ͳͲ

    Session-basedͰ༗ྗͳಛ௃ྔͷར༻ Session-awareͷಛ௃ྔͱsession-basedͷಛ௃ྔͱ͸େ͖͘มΘΒͳ͍ʢҧ͍͸ओʹΞϧΰϦζϜͷ෦෼ʣ ݁ہ༧ଌΠϕϯτʹ࣌ؒతʹ͍ۙߦಈಛ௃ྔ͕ॏཁͰ͋Δ͜ͱ͕ࢦఠ͞Ε͍ͯΔ ϢʔβʔID͕खʹೖΒͳ͍ͱsession-awareͳख๏͸࢖͑ͳ͍ Session-awareʹϢχʔΫͳ࣌ؒํ޲ʹ௵ͨ͠ಛ௃ྔΛ࡞Δ৔߹΋ɼ·ͣ͸ૉ๿/Ұൠతͳ΋ͷΛ࡞੒͠ɼԾઆݕূΛճ͢ RecencyɼϘοτͷ࢖༻ճ਺ɼొ࿥͔Βͷ೔਺ͳͲ
  47. [Ludewig 2020] Advances in Session-based and Session-aware Recommendation [Fang+ 2020]

    Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations [Wang+ 2021] A Survey on Session-based Recommender Systems [Wang+ 2016] Collaborative Recurrent Autoencoder: Recommend While Learning to Fill in the Blank [Zhang+ 2019] Deep Learning Based Recommender System: A Survey and New Perspectives [Hidashi+ 2016] Session-based Recommendations with Recurrent Neural Networks [Gao+ 2019] Learning to Recommend with Multiple Cascading Behaviors [Ren+ 2019] RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation [Anderson+ 2014] The Dynamics of Repeat Consumption [Wan+ 2018] Representing and Recommending Shopping Baskets with Complementarity, Compatibility and Loyalty [Hu+ 2020] Modeling Personalized Item Frequency Information for Next-basket Recommendation [Wu+ 2017] Session-aware Information Embedding for e-commerce Product Recommendation [Tan+ 2016] Improved Recurrent Neural Networks for Session-based Recommendations [Li+ 2017] Neural Attentive Session-based Recommendation [Kang+ 2018] Translation-based Recommendation [Tang+ 2018] Personalized top-n Sequential Recommendation via Convolutional Sequence Embedding [Quadrana+ 2018] Sequence-aware Recommender Systems ࢀߟจݙ
  48. [Zhou+ 2018] Deep Interest Evolution Network for Click-Through Rate Prediction

    [Pi+ 2019] Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction [Dacrema+ 2019] Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches [Laitifi+ 2020] Session-aware Recommendation: A Surprising Quest for the State-of-the-art [Chen+ 2019] AntProphet: an Intention Mining System behind Alipay’s Intelligent Customer Service Bot [Jannach+ 2017] Determining Characteristics of Successful Recommendations from Log Data—A Case Study [Odijk+ 2015] Struggling and Success in Web Search [Diriye+ 2012] Leaving so soon? Understanding and predicting web search abandonment rationales [Aula+ 2010] How Does Search Behavior Change as Search Becomes More Difficult? [Feild+ 2010] Predicting Searcher Frustration [Hirsch+ 2020] Query Reformulation in E-Commerce Search [White+ 2009] Characterizing and Predicting Search Engine Switching Behavior [Zhang+ 2020] An Improved Deep Forest Model for Prediction of E-commerce Consumers’ Repurchase Behavior ࢀߟจݙ
  49. ΧϥΫϦͰ͸αΠϨϯτΧελϚʔʢ*1ʣΛٹࡁ͢ΔͨΊͷαʔϏε ։ൃ΍σʔλ෼ੳʹऔΓ૊ΜͰ͓ΓɺػցֶशΤϯδχΞ΍WebΤϯ δχΞΛืू͓ͯ͠Γ·͢ɻ Ұॹʹಇ͘͜ͱʹڵຯͷ͋Δํ͸ੋඇ͝࿈བྷ͍ͩ͘͞ʂ Twitter: @not_oohikata Mail: k.obinata@karakuri.ai *1ɿαʔϏεӡӦऀଆʹٙ໰΍ෆ҆Λ໰͍߹ΘͤͣɺαΠτ͔Β཭୤ ͯ͠͠·͏ސ٬

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