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] SASRecɼBERT4RecɼKeBERT4RecͷΈ߹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] LSTMͱߦྻղ 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
ͷد༩ΛΈΔ͜ͱͰɼॏཁͳಛྔΛܦݧతʹධՁ͍ͯ͘͠ͷ͕ॏཁͩͱࢥΘΕΔ ॏཁͳಛྔͷԾઆ͕ੵΈ্͕ΕɼΑΓཉ͍͠ใͷղ૾্͕͕ΔͷͰɼઌߦݚڀɾࣄྫͷαʔϕΠޮ Խ͠ɼԾઆ͕ΑΓڧԽ͞Εɽɽͱ͍͏ਖ਼ͷϑΟʔυόοΫϧʔϓ͕ճΔ اۀͰϢʔβʔߦಈ༧ଌʹܞΘ͍ͬͯΔํʑ݁ہͦΕ͕େࣄͩͱ͍͍ͬͯΔ [Challenges, Best Practices and Pitfalls in Evaluating Results of Online Controlled Experiments, KDD 2019] ͕ͨͬͯ͠ɼॏཁͳಛྔʹ͍ͭͯͷԾઆݕূΛճ͢ΈࣗମΛͭ͘ΓͦΕΛճ͢͜ͱ͕ॏཁͰͳ͍͔ͱߟ͑Δ ͜͜·Ͱͷௐ͔ࠪΒͷࣔࠦ
page, leave page, swipe, tap͕جຊߦಈʢtapҐஔswipe࢝ऴҐஔܧଓ࣌ؒهʣ 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, purchaseͷ̐ͭͰɼ֤ߦಈʹରͯ࣍͠ͷ6ͭͷಛΛநग़ type index, top category index, leaf category index, page index, page stay time and timestamp ༧ଌ݁Ռʹج͍ͮͯɼϙοϓΞοϓࠂɼϓογϡ௨ʹ͍ͭͯෳͷઓུΛߟ͑ABςετ ఏҊख๏Ϣʔβʔͷreal-timeͳᅂੑΛ͏·͘ଊ͍͑ͯΔ͜ͱΛ࣮ূ EdgeDIPN: a Unified Deep Intent Prediction Network Deployed at the Edge [Guo+ VLDB 2020] (Alibaba, Peking University)
ग़ݱස͕গͳ͍ϥϕϧͷΈ߹ΘͤͦΕΛߏ͢Δ͍ͣΕ͔ͷϥϕϧʹϥϯμϜʹஔ͖͑ ϥϕϧͷΈ߹ΘͤͰग़ݱස͕େ͖͘ҟͳΔʢτοϓ32ͷϥϕϧͷΈ߹Θ͕ͤશମͷ90ˋΛΊΔʣ ྫɿCQ+FD+IR+RQ → CQ User Intent Prediction in Information-seeking Conversations [Qu+ CHIIR 2019] (U M Amherst, RMIT U, Alibaba) ࢀߟ
Similarity/DigSimʣ͕ॏཁ 15Ґ͔Β࠷ԼҐ·Ͱͯ͢ʮ͋ΔΩʔϫʔυΛؚΜͰ͍͔ͨʁʹؔ͢Δಛྔʯ User Intent Prediction in Information-seeking Conversations [Qu+ CHIIR 2019] (U M Amherst, RMIT U, Alibaba) ࢀߟ
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
[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