{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T20:46:19Z","timestamp":1743021979185,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031466731"},{"type":"electronic","value":"9783031466748"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-46674-8_45","type":"book-chapter","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:02:29Z","timestamp":1699102949000},"page":"648-664","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["HA-CMNet: A Driver CTR Model for\u00a0Vehicle-Cargo Matching in\u00a0O2O Platform"],"prefix":"10.1007","author":[{"given":"Zilong","family":"Jiang","sequence":"first","affiliation":[]},{"given":"Xiang","family":"Zuo","sequence":"additional","affiliation":[]},{"given":"Kaifu","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Lin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Dali","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiaohui","family":"Tao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"key":"45_CR1","doi-asserted-by":"crossref","unstructured":"Li, J., Zheng, Y., Dai, B., Yu, J.: Implications of matching and pricing strategies for multiple delivery-points service in a freight O2O platform. Transp. Res. Part E Logistics Transp. Rev. 136, 101871 (2020)","DOI":"10.1016\/j.tre.2020.101871"},{"key":"45_CR2","unstructured":"Li, J., Zhou, T., Xu, L., Dai, B.: Matching optimization decision of city LTL carpool based on time windows on the freight O2O platform. Syst. Eng. \u2013 Theor. Pract. 40(4), 979\u2013988 (2020)"},{"key":"45_CR3","unstructured":"Gu, J.: Vehicle-Cargo Matching System Based on Semantic Web Technology. Tsinghua University, Beijing (2013)"},{"key":"45_CR4","volume-title":"Research on the Optimization of the Matching of Truck and Cargo in the Highway Port of Transfar","author":"X Liu","year":"2013","unstructured":"Liu, X.: Research on the Optimization of the Matching of Truck and Cargo in the Highway Port of Transfar. Tsinghua University, Beijing (2013)"},{"issue":"4","key":"45_CR5","first-page":"903","volume":"23","author":"X Jia","year":"2017","unstructured":"Jia, X., Hai, F., Dong, R.: Control design for promoting single-homing user ratio of vehicles and cargos matching two-sided platform. Comput. Integr. Manuf. Syst. 23(4), 903\u2013912 (2017)","journal-title":"Comput. Integr. Manuf. Syst."},{"key":"45_CR6","doi-asserted-by":"crossref","unstructured":"Shao, Y., Yang, M., Wang, Y.-J., Wei, Y.-Z.: Research on competitiveness of vehicle-freight matching platform for carrier. In: 15th International Conference on Service Systems and Service Management. Hangzhou, China (2018)","DOI":"10.1109\/ICSSSM.2018.8464974"},{"key":"45_CR7","volume-title":"Research on Vehicle-Cargo Matching in Highway Trunk Freight Transportation - Taking T Platform as an Example","author":"C Bing","year":"2018","unstructured":"Bing, C.: Research on Vehicle-Cargo Matching in Highway Trunk Freight Transportation - Taking T Platform as an Example. Zhejiang Sci-Tech University, Hangzhou (2018)"},{"key":"45_CR8","doi-asserted-by":"crossref","unstructured":"Wang, R., Fu, B., Fu, G., Wang, M.: Deep & Cross network for ad click predictions. In: 23th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Workshop-ADKDD &TargetAD. Halifax, NS, Canada (2017)","DOI":"10.1145\/3124749.3124754"},{"key":"45_CR9","doi-asserted-by":"crossref","unstructured":"Sun, J., Zhu, M., Jiang, Y., Liu, Y.-Z., Wu, L.: Hierarchical attention model for personalized tag recommendation. J. Am. Soc. Inf. Sci. 72(2), 173\u2013189 (2021)","DOI":"10.1002\/asi.24400"},{"key":"45_CR10","unstructured":"YMM-TECH algorithm contest dataset. https:\/\/ymmtech.ymm56.com. Accessed 6 Feb 2020"},{"key":"45_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-1967-3","volume-title":"Machine Learning","author":"Z-H Zhou","year":"2021","unstructured":"Zhou, Z.-H.: Machine Learning, 2nd edn. Tsinghua University Press, Beijing (2021)","edition":"2"},{"key":"45_CR12","unstructured":"Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: The Thirty-First Annual Conference on Neural Information Processing Systems. Long Beach, CA, USA, pp. 3146\u20133154 (2017)"},{"key":"45_CR13","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, CA, USA, pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"45_CR14","unstructured":"DNN based competition plan. http:\/\/api.ymm56.com\/article4153.html. Accessed 12 Oct 2022"},{"key":"45_CR15","first-page":"91","volume":"2","author":"F Fang","year":"2022","unstructured":"Fang, F., Wang, C.: Driver click-trough rate prediction model based on SENet two-tower model considering attention mechanism in vehicle-cargo matching. Logistics Sci. Technol. 2, 91\u201397 (2022)","journal-title":"Logistics Sci. Technol."},{"key":"45_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108274","volume":"241","author":"X Song","year":"2022","unstructured":"Song, X., Li, J., Lei, Q., Zhao, W., Chen, Y., Mian, A.: Bi-CLKT: Bi-graph contrastive learning based knowledge tracing. Knowl.-Based Syst. 241, 108274 (2022)","journal-title":"Knowl.-Based Syst."},{"key":"45_CR17","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1016\/j.ins.2021.08.100","volume":"580","author":"X Song","year":"2021","unstructured":"Song, X., Li, J., Tang, Y., Zhao, T., Chen, Y., Guan, Z.: A joint graph convolutional network based deep knowledge tracing. Inf. Sci. 580, 510\u2013523 (2021)","journal-title":"Inf. Sci."},{"key":"45_CR18","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.neucom.2021.03.138","volume":"472","author":"G Xue","year":"2022","unstructured":"Xue, G., Zhong, M., Li, J., Chen, J., Zhai, C., Kong, R.: Dynamic network embedding survey. Neurocomputing 472, 212\u2013223 (2022)","journal-title":"Neurocomputing"},{"key":"45_CR19","unstructured":"Cai, T., Li, J., Mian, A., Li, R.-H., Sellis, T., Yu, J.X.: Target-aware holistic influence maximization in spatial social networks. IEEE Trans. Knowl. Data Eng. 34(4), 1993\u20132007 (2022)"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46674-8_45","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:21:17Z","timestamp":1699104077000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46674-8_45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031466731","9783031466748"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46674-8_45","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","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":"27 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2023.uqcloud.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes. Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"503","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":"216","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":"43% - 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":"2.97","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":"3.77","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}