{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T12:38:39Z","timestamp":1766666319756,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,9,14]],"date-time":"2023-09-14T00:00:00Z","timestamp":1694649600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,9,14]]},"DOI":"10.1145\/3604915.3608810","type":"proceedings-article","created":{"date-parts":[[2023,9,14]],"date-time":"2023-09-14T22:40:23Z","timestamp":1694731223000},"page":"294-305","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Trending Now: Modeling Trend Recommendations"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9932-2971","authenticated-orcid":false,"given":"Hao","family":"Ding","sequence":"first","affiliation":[{"name":"AWS AI Labs, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3965-1367","authenticated-orcid":false,"given":"Branislav","family":"Kveton","sequence":"additional","affiliation":[{"name":"AWS AI Labs, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7643-458X","authenticated-orcid":false,"given":"Yifei","family":"Ma","sequence":"additional","affiliation":[{"name":"AWS AI Labs, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0970-9214","authenticated-orcid":false,"given":"Youngsuk","family":"Park","sequence":"additional","affiliation":[{"name":"AWS AI Labs, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9397-4180","authenticated-orcid":false,"given":"Venkataramana","family":"Kini","sequence":"additional","affiliation":[{"name":"Amazon, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3922-0948","authenticated-orcid":false,"given":"Yupeng","family":"Gu","sequence":"additional","affiliation":[{"name":"AWS AI Labs, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3140-0021","authenticated-orcid":false,"given":"Ravi","family":"Divvela","sequence":"additional","affiliation":[{"name":"Amazon, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8265-0602","authenticated-orcid":false,"given":"Fei","family":"Wang","sequence":"additional","affiliation":[{"name":"Amazon, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4566-8767","authenticated-orcid":false,"given":"Anoop","family":"Deoras","sequence":"additional","affiliation":[{"name":"AWS AI Labs, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7308-938X","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"AWS AI Labs, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,9,14]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109912"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2013.2265080"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.50"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.02.016"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331199"},{"key":"e_1_3_2_1_6_1","volume-title":"Proceedings of KDD cup and workshop, Vol.\u00a02007","author":"Bennett James","year":"2007","unstructured":"James Bennett, Stan Lanning, 2007. The netflix prize. In Proceedings of KDD cup and workshop, Vol.\u00a02007. New York, 35."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219891"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.2307\/2985674"},{"volume-title":"Time series analysis: forecasting and control","author":"Box EP","key":"e_1_3_2_1_9_1","unstructured":"George\u00a0EP Box, Gwilym\u00a0M Jenkins, Gregory\u00a0C Reinsel, and Greta\u00a0M Ljung. 2015. Time series analysis: forecasting and control. John Wiley & Sons."},{"key":"e_1_3_2_1_10_1","unstructured":"Yuri\u00a0M Brovman. 2019. Complementary Item Recommendations at eBay Scale."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271782"},{"key":"e_1_3_2_1_12_1","volume-title":"Zero-shot recommender systems. arXiv preprint arXiv:2105.08318","author":"Ding Hao","year":"2021","unstructured":"Hao Ding, Yifei Ma, Anoop Deoras, Yuyang Wang, and Hao Wang. 2021. Zero-shot recommender systems. arXiv preprint arXiv:2105.08318 (2021)."},{"key":"e_1_3_2_1_13_1","volume-title":"Proceedings of the UAI","author":"Fan Ziwei","year":"2023","unstructured":"Ziwei Fan, Hao Ding, Anoop Deoras, and Trong\u00a0Nghia Hoang. 2023. Personalized federated domain adaptation for item-to-item recommendation. In Proceedings of the UAI 2023. https:\/\/www.amazon.science\/publications\/personalized-federated-domain-adaptation-for-item-to-item-recommendation"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482242"},{"key":"e_1_3_2_1_15_1","volume-title":"Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939","author":"Hidasi Bal\u00e1zs","year":"2015","unstructured":"Bal\u00e1zs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)."},{"volume-title":"Forecasting with exponential smoothing: the state space approach","author":"Hyndman Rob","key":"e_1_3_2_1_16_1","unstructured":"Rob Hyndman, Anne\u00a0B Koehler, J\u00a0Keith Ord, and Ralph\u00a0D Snyder. 2008. Forecasting with exponential smoothing: the state space approach. Springer Science & Business Media."},{"key":"e_1_3_2_1_17_1","volume-title":"Deep Learning for Forecasting: Current Trends and Challenges.Foresight: The International Journal of Applied Forecasting51","author":"Januschowski Tim","year":"2018","unstructured":"Tim Januschowski, Jan Gasthaus, Yuyang Wang, Syama\u00a0Sundar Rangapuram, and Laurent Callot. 2018. Deep Learning for Forecasting: Current Trends and Challenges.Foresight: The International Journal of Applied Forecasting51 (2018)."},{"key":"e_1_3_2_1_18_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 10603\u201310621","author":"Kan Kelvin","year":"2022","unstructured":"Kelvin Kan, Franccois-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, and Jan Gasthaus. 2022. Multivariate quantile function forecaster. In International Conference on Artificial Intelligence and Statistics. PMLR, 10603\u201310621."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00035"},{"key":"e_1_3_2_1_20_1","volume-title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Advances in neural information processing systems 32","author":"Li Shiyang","year":"2019","unstructured":"Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, and Xifeng Yan. 2019. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482257"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2021.03.012"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449791"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403278"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00050"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/1807167.1807306"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1002\/asi.21489"},{"key":"e_1_3_2_1_28_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 8127\u20138150","author":"Park Youngsuk","year":"2022","unstructured":"Youngsuk Park, Danielle Maddix, Franccois-Xavier Aubet, Kelvin Kan, Jan Gasthaus, and Yuyang Wang. 2022. Learning quantile functions without quantile crossing for distribution-free time series forecasting. In International Conference on Artificial Intelligence and Statistics. PMLR, 8127\u20138150."},{"key":"e_1_3_2_1_29_1","volume-title":"Deep state space models for time series forecasting. Advances in neural information processing systems 31","author":"Rangapuram Syama\u00a0Sundar","year":"2018","unstructured":"Syama\u00a0Sundar Rangapuram, Matthias\u00a0W Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski. 2018. Deep state space models for time series forecasting. Advances in neural information processing systems 31 (2018)."},{"key":"e_1_3_2_1_30_1","volume-title":"High-dimensional multivariate forecasting with low-rank gaussian copula processes. Advances in neural information processing systems 32","author":"Salinas David","year":"2019","unstructured":"David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, and Jan Gasthaus. 2019. High-dimensional multivariate forecasting with low-rank gaussian copula processes. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2019.07.001"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357895"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783273"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467289"},{"key":"e_1_3_2_1_35_1","volume-title":"A multi-horizon quantile recurrent forecaster. arXiv preprint arXiv:1711.11053","author":"Wen Ruofeng","year":"2017","unstructured":"Ruofeng Wen, Kari Torkkola, Balakrishnan Narayanaswamy, and Dhruv Madeka. 2017. A multi-horizon quantile recurrent forecaster. arXiv preprint arXiv:1711.11053 (2017)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.331"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3487553.3524222"},{"key":"e_1_3_2_1_38_1","volume-title":"What trends in Chinese social media. arXiv preprint arXiv:1107.3522","author":"Yu Louis","year":"2011","unstructured":"Louis Yu, Sitaram Asur, and Bernardo\u00a0A Huberman. 2011. What trends in Chinese social media. arXiv preprint arXiv:1107.3522 (2011)."},{"key":"e_1_3_2_1_39_1","volume-title":"First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting. arXiv preprint arXiv:2212.08151","author":"Zhang Xiyuan","year":"2022","unstructured":"Xiyuan Zhang, Xiaoyong Jin, Karthick Gopalswamy, Gaurav Gupta, Youngsuk Park, Xingjian Shi, Hao Wang, Danielle\u00a0C Maddix, and Yuyang Wang. 2022. First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting. arXiv preprint arXiv:2212.08151 (2022)."},{"key":"e_1_3_2_1_40_1","unstructured":"Yuhui Zhang Hao Ding Zeren Shui Yifei Ma James Zou Anoop Deoras and Hao Wang. 2021. Language models as recommender systems: Evaluations and limitations. (2021)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462875"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741637"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449788"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219826"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"crossref","unstructured":"Yu Zhu Hao Li Yikang Liao Beidou Wang Ziyu Guan Haifeng Liu and Deng Cai. 2017. What to Do Next: Modeling User Behaviors by Time-LSTM.. In IJCAI Vol.\u00a017. 3602\u20133608.","DOI":"10.24963\/ijcai.2017\/504"}],"event":{"name":"RecSys '23: Seventeenth 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":"Singapore Singapore","acronym":"RecSys '23"},"container-title":["Proceedings of the 17th ACM Conference on Recommender Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3604915.3608810","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3604915.3608810","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:46:06Z","timestamp":1750178766000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3604915.3608810"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,14]]},"references-count":45,"alternative-id":["10.1145\/3604915.3608810","10.1145\/3604915"],"URL":"https:\/\/doi.org\/10.1145\/3604915.3608810","relation":{},"subject":[],"published":{"date-parts":[[2023,9,14]]},"assertion":[{"value":"2023-09-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}