{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:33:46Z","timestamp":1763202826090,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981472"},{"type":"electronic","value":"9789819981489"}],"license":[{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8148-9_1","type":"book-chapter","created":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T10:02:23Z","timestamp":1700906543000},"page":"3-16","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["PBTR: Pre-training and\u00a0Bidirectional Semantic Enhanced Trajectory Recovery"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5597-7421","authenticated-orcid":false,"given":"Qiming","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9684-611X","authenticated-orcid":false,"given":"Tianxi","family":"Liao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8948-3103","authenticated-orcid":false,"given":"Tongyu","family":"Zhu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0157-1716","authenticated-orcid":false,"given":"Leilei","family":"Sun","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0227-0891","authenticated-orcid":false,"given":"Weifeng","family":"Lv","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,26]]},"reference":[{"key":"1_CR1","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"key":"1_CR2","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder XGBoostfor statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"1_CR3","unstructured":"Erhan, D., Courville, A., Bengio, Y., Vincent, P.: Why does unsupervised pre-training help deep learning? In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 201\u2013208. JMLR Workshop and Conference Proceedings (2010)"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Feng, J., et al.: DeepMove: predicting human mobility with attentional recurrent networks. In: Proceedings of the 2018 World Wide Web Conference, pp. 1459\u20131468 (2018)","DOI":"10.1145\/3178876.3186058"},{"issue":"8","key":"1_CR5","doi-asserted-by":"publisher","first-page":"13108","DOI":"10.1109\/TITS.2021.3119887","volume":"23","author":"L Han","year":"2021","unstructured":"Han, L., Du, B., Lin, J., Sun, L., Li, X., Peng, Y.: Multi-semantic path representation learning for travel time estimation. IEEE Trans. Intell. Transp. Syst. 23(8), 13108\u201313117 (2021)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1_CR6","unstructured":"Hendrycks, D., Lee, K., Mazeika, M.: Using pre-training can improve model robustness and uncertainty. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 97, pp. 2712\u20132721. PMLR (2019)"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Li, X., Zhao, K., Cong, G., Jensen, C.S., Wei, W.: Deep representation learning for trajectory similarity computation. In: 2018 IEEE 34th International Conference on Data Engineering (ICDE), pp. 617\u2013628. IEEE (2018)","DOI":"10.1109\/ICDE.2018.00062"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Lin, Y., Wan, H., Guo, S., Lin, Y.: Pre-training context and time aware location embeddings from spatial-temporal trajectories for user next location prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4241\u20134248 (2021)","DOI":"10.1609\/aaai.v35i5.16548"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhao, K., Cong, G., Bao, Z.: Online anomalous trajectory detection with deep generative sequence modeling. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp. 949\u2013960. IEEE (2020)","DOI":"10.1109\/ICDE48307.2020.00087"},{"key":"1_CR10","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Park, S.H., Kim, B., Kang, C.M., Chung, C.C., Choi, J.W.: Sequence-to-sequence prediction of vehicle trajectory via LSTM encoder-decoder architecture. In: 2018 IEEE Intelligent Vehicles Symposium (IV), pp. 1672\u20131678. IEEE (2018)","DOI":"10.1109\/IVS.2018.8500658"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Ren, H., Ruan, S., Li, Y., Bao, J., Meng, C., Li, R., Zheng, Y.: MTrajRec: map-constrained trajectory recovery via seq2seq multi-task learning. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 1410\u20131419 (2021)","DOI":"10.1145\/3447548.3467238"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Shimizu, T., Yabe, T., Tsubouchi, K.: Learning fine grained place embeddings with spatial hierarchy from human mobility trajectories. arXiv preprint arXiv:2002.02058 (2020)","DOI":"10.1145\/3397536.3422229"},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Sun, H., Yang, C., Deng, L., Zhou, F., Huang, F., Zheng, K.: PeriodicMove: shift-aware human mobility recovery with graph neural network. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 1734\u20131743 (2021)","DOI":"10.1145\/3459637.3482284"},{"key":"1_CR15","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"key":"1_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1007\/978-3-030-18590-9_26","volume-title":"Database Systems for Advanced Applications","author":"H Wan","year":"2019","unstructured":"Wan, H., Li, F., Guo, S., Cao, Z., Lin, Y.: Learning time-aware distributed representations of locations from spatio-temporal trajectories. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds.) DASFAA 2019. LNCS, vol. 11448, pp. 268\u2013272. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-18590-9_26"},{"issue":"3","key":"1_CR17","first-page":"921","volume":"33","author":"J Wang","year":"2019","unstructured":"Wang, J., Wu, N., Lu, X., Zhao, W.X., Feng, K.: Deep trajectory recovery with fine-grained calibration using Kalman filter. IEEE Trans. Knowl. Data Eng. 33(3), 921\u2013934 (2019)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"2","key":"1_CR18","doi-asserted-by":"publisher","first-page":"108","DOI":"10.26599\/BDMA.2018.9020010","volume":"1","author":"R Wu","year":"2018","unstructured":"Wu, R., Luo, G., Shao, J., Tian, L., Peng, C.: Location prediction on trajectory data: a review. Big Data Min. Anal. 1(2), 108\u2013127 (2018)","journal-title":"Big Data Min. Anal."},{"key":"1_CR19","doi-asserted-by":"crossref","unstructured":"Xia, T., et al.: AttnMove: history enhanced trajectory recovery via attentional network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4494\u20134502 (2021)","DOI":"10.1609\/aaai.v35i5.16577"},{"key":"1_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/978-3-031-10989-8_2","volume-title":"Knowledge Science, Engineering and Management","author":"Y Xu","year":"2022","unstructured":"Xu, Y., Sun, L., Du, B., Han, L.: Spatial semantic learning for travel time estimation. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds.) KSEM 2022, Part III. LNCS, vol. 13370, pp. 15\u201326. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-10989-8_2"},{"issue":"1","key":"1_CR21","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1109\/TKDE.2011.200","volume":"25","author":"J Yuan","year":"2011","unstructured":"Yuan, J., Zheng, Y., Xie, X., Sun, G.: T-drive: enhancing driving directions with taxi drivers\u2019 intelligence. IEEE Trans. Knowl. Data Eng. 25(1), 220\u2013232 (2011)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Zhao, S., Zhao, T., King, I., Lyu, M.R.: Geo-teaser: geo-temporal sequential embedding rank for point-of-interest recommendation. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 153\u2013162 (2017)","DOI":"10.1145\/3041021.3054138"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8148-9_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:33:35Z","timestamp":1710268415000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8148-9_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,26]]},"ISBN":["9789819981472","9789819981489"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8148-9_1","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,26]]},"assertion":[{"value":"26 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"650","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.14","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.46","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}