{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T19:29:30Z","timestamp":1773775770547,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":25,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467060","type":"proceedings-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T18:21:39Z","timestamp":1628878899000},"page":"2840-2848","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":34,"title":["SSML: Self-Supervised Meta-Learner for En Route Travel Time Estimation at Baidu Maps"],"prefix":"10.1145","author":[{"given":"Xiaomin","family":"Fang","sequence":"first","affiliation":[{"name":"Baidu Inc., Shenzhen, China"}]},{"given":"Jizhou","family":"Huang","sequence":"additional","affiliation":[{"name":"Baidu Inc., Beijing, China"}]},{"given":"Fan","family":"Wang","sequence":"additional","affiliation":[{"name":"Baidu Inc., Shenzhen, China"}]},{"given":"Lihang","family":"Liu","sequence":"additional","affiliation":[{"name":"Baidu Inc., Shenzhen, China"}]},{"given":"Yibo","family":"Sun","sequence":"additional","affiliation":[{"name":"Baidu Inc., Beijing, China"}]},{"given":"Haifeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Baidu Inc., Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"crossref","unstructured":"Pouria Amirian Anahid Basiri and Jeremy Morley. 2016. Predictive Analytics for Enhancing Travel Time Estimation in Navigation Apps of Apple Google and Microsoft. In SIGSPATIAL. 31--36.  Pouria Amirian Anahid Basiri and Jeremy Morley. 2016. Predictive Analytics for Enhancing Travel Time Estimation in Navigation Apps of Apple Google and Microsoft. In SIGSPATIAL. 31--36.","DOI":"10.1145\/3003965.3003976"},{"key":"e_1_3_2_2_2_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL. 4171--4186.","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2019 . BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL. 4171--4186. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL. 4171--4186."},{"key":"e_1_3_2_2_3_1","unstructured":"Zhengxiao Du Xiaowei Wang Hongxia Yang Jingren Zhou and Jie Tang. 2019. Sequential Scenario-Specific Meta Learner for Online Recommendation. In KDD. 2895--2904.  Zhengxiao Du Xiaowei Wang Hongxia Yang Jingren Zhou and Jie Tang. 2019. Sequential Scenario-Specific Meta Learner for Online Recommendation. In KDD. 2895--2904."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"crossref","unstructured":"Xiaomin Fang Jizhou Huang FanWang Lingke Zeng Haijin Liang and Haifeng Wang. 2020. ConSTGAT: Contextual Spatial-Temporal Graph Attention Network for Travel Time Estimation at Baidu Maps. In KDD. 2697--2705.  Xiaomin Fang Jizhou Huang FanWang Lingke Zeng Haijin Liang and Haifeng Wang. 2020. ConSTGAT: Contextual Spatial-Temporal Graph Attention Network for Travel Time Estimation at Baidu Maps. In KDD. 2697--2705.","DOI":"10.1145\/3394486.3403320"},{"key":"e_1_3_2_2_5_1","volume-title":"Model-agnostic metalearning for fast adaptation of deep networks. arXiv preprint:1703.03400","author":"Finn Chelsea","year":"2017","unstructured":"Chelsea Finn , Pieter Abbeel , and Sergey Levine . 2017. Model-agnostic metalearning for fast adaptation of deep networks. arXiv preprint:1703.03400 ( 2017 ). Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic metalearning for fast adaptation of deep networks. arXiv preprint:1703.03400 (2017)."},{"key":"e_1_3_2_2_6_1","volume-title":"Long short-term memory. Neural computation 9, 8","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber . 1997. Long short-term memory. Neural computation 9, 8 ( 1997 ), 1735--1780. Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780."},{"key":"e_1_3_2_2_7_1","volume-title":"Meta-learning in neural networks: A survey. arXiv preprint:2004.05439","author":"Hospedales Timothy","year":"2020","unstructured":"Timothy Hospedales , Antreas Antoniou , Paul Micaelli , and Amos Storkey . 2020. Meta-learning in neural networks: A survey. arXiv preprint:2004.05439 ( 2020 ). Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey. 2020. Meta-learning in neural networks: A survey. arXiv preprint:2004.05439 (2020)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177703732"},{"key":"e_1_3_2_2_9_1","unstructured":"Hoyeop Lee Jinbae Im Seongwon Jang Hyunsouk Cho and Sehee Chung. 2019. MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation. In KDD. 1073--1082.  Hoyeop Lee Jinbae Im Seongwon Jang Hyunsouk Cho and Sehee Chung. 2019. MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation. In KDD. 1073--1082."},{"key":"e_1_3_2_2_10_1","unstructured":"Yaguang Li Kun Fu Zheng Wang Cyrus Shahabi Jieping Ye and Yan Liu. 2018. Multi-task representation learning for travel time estimation. In KDD. 1695--1704.  Yaguang Li Kun Fu Zheng Wang Cyrus Shahabi Jieping Ye and Yan Liu. 2018. Multi-task representation learning for travel time estimation. In KDD. 1695--1704."},{"key":"e_1_3_2_2_11_1","volume-title":"A simple neural attentive meta-learner. arXiv preprint:1707.03141","author":"Mishra Nikhil","year":"2017","unstructured":"Nikhil Mishra , Mostafa Rohaninejad , Xi Chen , and Pieter Abbeel . 2017. A simple neural attentive meta-learner. arXiv preprint:1707.03141 ( 2017 ). Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel. 2017. A simple neural attentive meta-learner. arXiv preprint:1707.03141 (2017)."},{"key":"e_1_3_2_2_12_1","volume-title":"Proceedings of machine learning research 70","author":"Munkhdalai Tsendsuren","year":"2017","unstructured":"Tsendsuren Munkhdalai and Hong Yu . 2017 . Meta networks . Proceedings of machine learning research 70 (2017), 2554. Tsendsuren Munkhdalai and Hong Yu. 2017. Meta networks. Proceedings of machine learning research 70 (2017), 2554."},{"key":"e_1_3_2_2_13_1","unstructured":"Vinod Nair and Geoffrey E Hinton. 2010. Rectified linear units improve restricted boltzmann machines. In ICML. 807--814.  Vinod Nair and Geoffrey E Hinton. 2010. Rectified linear units improve restricted boltzmann machines. In ICML. 807--814."},{"key":"e_1_3_2_2_14_1","volume-title":"Reptile: a scalable metalearning algorithm. arXiv preprint:1803.02999 2, 3","author":"Nichol Alex","year":"2018","unstructured":"Alex Nichol and John Schulman . 2018. Reptile: a scalable metalearning algorithm. arXiv preprint:1803.02999 2, 3 ( 2018 ), 4. Alex Nichol and John Schulman. 2018. Reptile: a scalable metalearning algorithm. arXiv preprint:1803.02999 2, 3 (2018), 4."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"crossref","unstructured":"Zheyi Pan Yuxuan Liang Weifeng Wang Yong Yu Yu Zheng and Junbo Zhang. 2019. Urban traffic prediction from spatio-temporal data using deep meta learning. In KDD. 1720--1730.  Zheyi Pan Yuxuan Liang Weifeng Wang Yong Yu Yu Zheng and Junbo Zhang. 2019. Urban traffic prediction from spatio-temporal data using deep meta learning. In KDD. 1720--1730.","DOI":"10.1145\/3292500.3330884"},{"key":"e_1_3_2_2_16_1","unstructured":"Adam Santoro Sergey Bartunov MatthewBotvinick Daan Wierstra and Timothy Lillicrap. 2016. Meta-learning with memory-augmented neural networks. In ICML. 1842--1850.  Adam Santoro Sergey Bartunov MatthewBotvinick Daan Wierstra and Timothy Lillicrap. 2016. Meta-learning with memory-augmented neural networks. In ICML. 1842--1850."},{"key":"e_1_3_2_2_17_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez Lukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In NIPS. 5998--6008.  Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez Lukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In NIPS. 5998--6008."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"crossref","unstructured":"Dong Wang Junbo Zhang Wei Cao Jian Li and Yu Zheng. 2018. When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks. In AAAI. 2500--2507.  Dong Wang Junbo Zhang Wei Cao Jian Li and Yu Zheng. 2018. When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks. In AAAI. 2500--2507.","DOI":"10.1609\/aaai.v32i1.11877"},{"key":"e_1_3_2_2_19_1","volume-title":"Article 19","author":"Wang Hongjian","year":"2019","unstructured":"Hongjian Wang , Xianfeng Tang , Yu-Hsuan Kuo , Daniel Kifer , and Zhenhui Li. 2019. A Simple Baseline for Travel Time Estimation Using Large-scale Trip Data. TIST 10, 2 , Article 19 ( 2019 ), 22 pages. Hongjian Wang, Xianfeng Tang, Yu-Hsuan Kuo, Daniel Kifer, and Zhenhui Li. 2019. A Simple Baseline for Travel Time Estimation Using Large-scale Trip Data. TIST 10, 2, Article 19 (2019), 22 pages."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Yilun Wang Yu Zheng and Yexiang Xue. 2014. Travel time estimation of a path using sparse trajectories. In KDD. 25--34.  Yilun Wang Yu Zheng and Yexiang Xue. 2014. Travel time estimation of a path using sparse trajectories. In KDD. 25--34.","DOI":"10.1145\/2623330.2623656"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"crossref","unstructured":"Zheng Wang Kun Fu and Jieping Ye. 2018. Learning to estimate the travel time. In KDD. 858--866.  Zheng Wang Kun Fu and Jieping Ye. 2018. Learning to estimate the travel time. In KDD. 858--866.","DOI":"10.1145\/3219819.3219900"},{"key":"e_1_3_2_2_22_1","unstructured":"Huaxiu Yao Yiding Liu YingWei Xianfeng Tang and Zhenhui Li. 2019. Learning from multiple cities: A meta-learning approach for spatial-temporal prediction. In WWW. 2181--2191.  Huaxiu Yao Yiding Liu YingWei Xianfeng Tang and Zhenhui Li. 2019. Learning from multiple cities: A meta-learning approach for spatial-temporal prediction. In WWW. 2181--2191."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"crossref","unstructured":"Xinshi Zang Huaxiu Yao Guanjie Zheng Nan Xu Kai Xu and Zhenhui Li. 2020. MetaLight: Value-Based Meta-Reinforcement Learning for Traffic Signal Control. In AAAI. 1153--1160.  Xinshi Zang Huaxiu Yao Guanjie Zheng Nan Xu Kai Xu and Zhenhui Li. 2020. MetaLight: Value-Based Meta-Reinforcement Learning for Traffic Signal Control. In AAAI. 1153--1160.","DOI":"10.1609\/aaai.v34i01.5467"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"crossref","unstructured":"Hanyuan Zhang Hao Wu Weiwei Sun and Baihua Zheng. 2018. DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision. In IJCAI. 3655--3661.  Hanyuan Zhang Hao Wu Weiwei Sun and Baihua Zheng. 2018. DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision. In IJCAI. 3655--3661.","DOI":"10.24963\/ijcai.2018\/508"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"crossref","unstructured":"Yang Zhang Fuli Feng Chenxu Wang Xiangnan He Meng Wang Yan Li and Yongdong Zhang. 2020. How to Retrain Recommender System? A Sequential Meta-Learning Method. In SIGIR. 1479--1488.  Yang Zhang Fuli Feng Chenxu Wang Xiangnan He Meng Wang Yan Li and Yongdong Zhang. 2020. How to Retrain Recommender System? A Sequential Meta-Learning Method. In SIGIR. 1479--1488.","DOI":"10.1145\/3397271.3401167"}],"event":{"name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event Singapore","acronym":"KDD '21","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467060","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467060","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:25:10Z","timestamp":1750195510000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467060"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":25,"alternative-id":["10.1145\/3447548.3467060","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467060","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}