{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:21:10Z","timestamp":1750220470343,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":21,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T00:00:00Z","timestamp":1631491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,13]]},"DOI":"10.1145\/3460231.3478844","type":"proceedings-article","created":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T21:45:02Z","timestamp":1631569502000},"page":"674-679","source":"Crossref","is-referenced-by-count":2,"title":["Global-Local Item Embedding for Temporal Set Prediction"],"prefix":"10.1145","author":[{"given":"Seungjae","family":"Jung","sequence":"first","affiliation":[{"name":"Naver R&D Center and NAVER CLOVA, Korea, Republic of"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Young-Jin","family":"Park","sequence":"additional","affiliation":[{"name":"Naver R&D Center and NAVER CLOVA, Korea, Republic of"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jisu","family":"Jeong","sequence":"additional","affiliation":[{"name":"NAVER CLOVA and NAVER AI LAB, Korea, Republic of"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyung-Min","family":"Kim","sequence":"additional","affiliation":[{"name":"NAVER CLOVA and NAVER AI LAB, Korea, Republic of"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiun","family":"Kim","sequence":"additional","affiliation":[{"name":"NAVER CLOVA, Korea, Republic of"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minkyu","family":"Kim","sequence":"additional","affiliation":[{"name":"NAVER CLOVA, Korea, Republic of"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanock","family":"Kwak","sequence":"additional","affiliation":[{"name":"NAVER CLOVA, Korea, Republic of"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,9,13]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Advances in Neural Information Processing Systems, S.\u00a0Bengio, H.\u00a0Wallach, H.\u00a0Larochelle, K.\u00a0Grauman, N.\u00a0Cesa-Bianchi, and R.\u00a0Garnett (Eds.), Vol.\u00a031. Curran Associates","author":"Chen Ricky","year":"2018","unstructured":"Ricky T.\u00a0Q. Chen , Yulia Rubanova , Jesse Bettencourt , and David\u00a0 K Duvenaud . 2018. Neural Ordinary Differential Equations . In Advances in Neural Information Processing Systems, S.\u00a0Bengio, H.\u00a0Wallach, H.\u00a0Larochelle, K.\u00a0Grauman, N.\u00a0Cesa-Bianchi, and R.\u00a0Garnett (Eds.), Vol.\u00a031. Curran Associates , Inc .https:\/\/proceedings.neurips.cc\/paper\/ 2018 \/file\/69386f6bb1dfed68692a24c8686939b9-Paper.pdf Ricky T.\u00a0Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David\u00a0K Duvenaud. 2018. Neural Ordinary Differential Equations. In Advances in Neural Information Processing Systems, S.\u00a0Bengio, H.\u00a0Wallach, H.\u00a0Larochelle, K.\u00a0Grauman, N.\u00a0Cesa-Bianchi, and R.\u00a0Garnett (Eds.), Vol.\u00a031. Curran Associates, Inc.https:\/\/proceedings.neurips.cc\/paper\/2018\/file\/69386f6bb1dfed68692a24c8686939b9-Paper.pdf"},{"key":"e_1_3_2_2_2_1","unstructured":"Edward Choi Mohammad\u00a0Taha Bahadori Andy Schuetz Walter\u00a0F Stewart and Jimeng Sun. 2016. Doctor ai: Predicting clinical events via recurrent neural networks. In Machine learning for healthcare conference. PMLR 301\u2013318.  Edward Choi Mohammad\u00a0Taha Bahadori Andy Schuetz Walter\u00a0F Stewart and Jimeng Sun. 2016. Doctor ai: Predicting clinical events via recurrent neural networks. In Machine learning for healthcare conference. PMLR 301\u2013318."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/3020847.3020882"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330979"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401066"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1987.tb01685.x"},{"volume-title":"The Theory of Dispersion Models","author":"J\u00f8rgensen Bent","key":"e_1_3_2_2_7_1","unstructured":"Bent J\u00f8rgensen . 1997. The Theory of Dispersion Models . Chapman and Hall\/CRC. Bent J\u00f8rgensen. 1997. The Theory of Dispersion Models. Chapman and Hall\/CRC."},{"key":"e_1_3_2_2_8_1","volume-title":"Proceedings of ACM RecSys 2019 Late-Breaking Results, Marko Tkalcic and Sole Pera (Eds.). http:\/\/ceur-ws.org\/Vol-2431\/paper12","author":"Kim Kyung-Min","year":"2019","unstructured":"Kyung-Min Kim , Dong-Hyun Kwak , Hanock Kwak , Young-Jin Park , Sangkwon Sim , Jae-Han Cho , Minkyu Kim , Jihun Kwon , Nako Sung , and Jung-Woo Ha . 2019 . Tripartite Heterogeneous Graph Propagation for Large-scale Social Recommendation . In Proceedings of ACM RecSys 2019 Late-Breaking Results, Marko Tkalcic and Sole Pera (Eds.). http:\/\/ceur-ws.org\/Vol-2431\/paper12 .pdf Kyung-Min Kim, Dong-Hyun Kwak, Hanock Kwak, Young-Jin Park, Sangkwon Sim, Jae-Han Cho, Minkyu Kim, Jihun Kwon, Nako Sung, and Jung-Woo Ha. 2019. Tripartite Heterogeneous Graph Propagation for Large-scale Social Recommendation. In Proceedings of ACM RecSys 2019 Late-Breaking Results, Marko Tkalcic and Sole Pera (Eds.). http:\/\/ceur-ws.org\/Vol-2431\/paper12.pdf"},{"volume-title":"Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations, Yoshua Bengio and Yann LeCun (Eds.). http:\/\/arxiv.org\/abs\/1412","author":"P.","key":"e_1_3_2_2_9_1","unstructured":"Diederik\u00a0 P. Kingma and Jimmy Ba. 2015 . Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations, Yoshua Bengio and Yann LeCun (Eds.). http:\/\/arxiv.org\/abs\/1412 .6980 Diederik\u00a0P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations, Yoshua Bengio and Yann LeCun (Eds.). http:\/\/arxiv.org\/abs\/1412.6980"},{"volume-title":"Auto-Encoding Variational Bayes. In International Conference on Learning Representations, Yoshua Bengio and Yann LeCun (Eds.). https:\/\/openreview.net\/forum?id=33X9fd2-9FyZd","author":"P.","key":"e_1_3_2_2_10_1","unstructured":"Diederik\u00a0 P. Kingma and Max Welling. 2014 . Auto-Encoding Variational Bayes. In International Conference on Learning Representations, Yoshua Bengio and Yann LeCun (Eds.). https:\/\/openreview.net\/forum?id=33X9fd2-9FyZd Diederik\u00a0P. Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. In International Conference on Learning Representations, Yoshua Bengio and Yann LeCun (Eds.). https:\/\/openreview.net\/forum?id=33X9fd2-9FyZd"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401944"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186150"},{"key":"e_1_3_2_2_13_1","volume-title":"International conference on machine learning. PMLR, 1278\u20131286","author":"Rezende Danilo\u00a0Jimenez","year":"2014","unstructured":"Danilo\u00a0Jimenez Rezende , Shakir Mohamed , and Daan Wierstra . 2014 . Stochastic backpropagation and approximate inference in deep generative models . In International conference on machine learning. PMLR, 1278\u20131286 . Danilo\u00a0Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014. Stochastic backpropagation and approximate inference in deep generative models. In International conference on machine learning. PMLR, 1278\u20131286."},{"key":"e_1_3_2_2_14_1","volume-title":"Advances in Neural Information Processing Systems, H.\u00a0Wallach, H.\u00a0Larochelle, A.\u00a0Beygelzimer, F.\u00a0d'Alch\u00e9-Buc, E.\u00a0Fox, and R.\u00a0Garnett (Eds.), Vol.\u00a032. Curran Associates","author":"Rubanova Yulia","year":"2019","unstructured":"Yulia Rubanova , Ricky T.\u00a0Q. Chen , and David\u00a0 K Duvenaud . 2019. Latent Ordinary Differential Equations for Irregularly-Sampled Time Series . In Advances in Neural Information Processing Systems, H.\u00a0Wallach, H.\u00a0Larochelle, A.\u00a0Beygelzimer, F.\u00a0d'Alch\u00e9-Buc, E.\u00a0Fox, and R.\u00a0Garnett (Eds.), Vol.\u00a032. Curran Associates , Inc .https:\/\/proceedings.neurips.cc\/paper\/ 2019 \/file\/42a6845a557bef704ad8ac9cb4461d43-Paper.pdf Yulia Rubanova, Ricky T.\u00a0Q. Chen, and David\u00a0K Duvenaud. 2019. Latent Ordinary Differential Equations for Irregularly-Sampled Time Series. In Advances in Neural Information Processing Systems, H.\u00a0Wallach, H.\u00a0Larochelle, A.\u00a0Beygelzimer, F.\u00a0d'Alch\u00e9-Buc, E.\u00a0Fox, and R.\u00a0Garnett (Eds.), Vol.\u00a032. Curran Associates, Inc.https:\/\/proceedings.neurips.cc\/paper\/2019\/file\/42a6845a557bef704ad8ac9cb4461d43-Paper.pdf"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.5555\/2981562.2981720"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/371920.372071"},{"key":"e_1_3_2_2_17_1","volume-title":"Advances in Neural Information Processing Systems, L.\u00a0Saul, Y.\u00a0Weiss, and L.\u00a0Bottou (Eds.), Vol.\u00a017","author":"Srebro Nathan","year":"2004","unstructured":"Nathan Srebro , Jason Rennie , and Tommi Jaakkola . 2005. Maximum-Margin Matrix Factorization . In Advances in Neural Information Processing Systems, L.\u00a0Saul, Y.\u00a0Weiss, and L.\u00a0Bottou (Eds.), Vol.\u00a017 . MIT Press . https:\/\/proceedings.neurips.cc\/paper\/ 2004 \/file\/e0688d13958a19e087e123148555e4b4-Paper.pdf Nathan Srebro, Jason Rennie, and Tommi Jaakkola. 2005. Maximum-Margin Matrix Factorization. In Advances in Neural Information Processing Systems, L.\u00a0Saul, Y.\u00a0Weiss, and L.\u00a0Bottou (Eds.), Vol.\u00a017. MIT Press. https:\/\/proceedings.neurips.cc\/paper\/2004\/file\/e0688d13958a19e087e123148555e4b4-Paper.pdf"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441759"},{"key":"e_1_3_2_2_19_1","unstructured":"Yi Yang Wei Qian and Hui Zou. 2016. Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models. arxiv:1508.06378\u00a0[stat.ME]  Yi Yang Wei Qian and Hui Zou. 2016. Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models. arxiv:1508.06378\u00a0[stat.ME]"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/2911451.2914683"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403152"}],"event":{"name":"RecSys '21: Fifteenth ACM Conference on Recommender Systems","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGAI ACM Special Interest Group on Artificial Intelligence","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval","SIGCHI ACM Special Interest Group on Computer-Human Interaction","SIGecom Special Interest Group on Economics and Computation"],"location":"Amsterdam Netherlands","acronym":"RecSys '21"},"container-title":["Fifteenth ACM Conference on Recommender Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3460231.3478844","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3460231.3478844","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:48:30Z","timestamp":1750193310000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3460231.3478844"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,13]]},"references-count":21,"alternative-id":["10.1145\/3460231.3478844","10.1145\/3460231"],"URL":"https:\/\/doi.org\/10.1145\/3460231.3478844","relation":{},"subject":[],"published":{"date-parts":[[2021,9,13]]}}}