{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T14:01:16Z","timestamp":1742997676004,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819944019"},{"type":"electronic","value":"9789819944026"}],"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\/978-981-99-4402-6_28","type":"book-chapter","created":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T23:02:45Z","timestamp":1690412565000},"page":"383-398","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Proactive Perception of\u00a0Preferences Evolution Based on\u00a0Graph Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3034-8936","authenticated-orcid":false,"given":"Lixin","family":"Pang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6913-0514","authenticated-orcid":false,"given":"Zhizhong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0850-5789","authenticated-orcid":false,"given":"Lingqiang","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4051-5490","authenticated-orcid":false,"given":"Linxia","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5536-1979","authenticated-orcid":false,"given":"Xiaoyu","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,27]]},"reference":[{"key":"28_CR1","doi-asserted-by":"publisher","first-page":"102784","DOI":"10.1016\/j.jretconser.2021.102784","volume":"64","author":"L Yang","year":"2022","unstructured":"Yang, L., Xu, M., Xing, L.: Exploring the core factors of online purchase decisions by building an e-commerce network evolution model. J. Retail. Consum. Serv. 64, 102784 (2022)","journal-title":"J. Retail. Consum. Serv."},{"key":"28_CR2","doi-asserted-by":"publisher","first-page":"103421","DOI":"10.1016\/j.compind.2021.103421","volume":"128","author":"MC Chiu","year":"2021","unstructured":"Chiu, M.C., Huang, J.H., Gupta, S., Akman, G.: Developing a personalized recommendation system in a smart product service system based on unsupervised learning model. Comput. Ind. 128, 103421 (2021)","journal-title":"Comput. Ind."},{"issue":"2","key":"28_CR3","doi-asserted-by":"publisher","first-page":"449","DOI":"10.5267\/j.ijdns.2021.12.009","volume":"6","author":"H Alzoubi","year":"2022","unstructured":"Alzoubi, H., Alshurideh, M., Kurdi, B., Akour, I., Aziz, R.: Does BLE technology contribute towards improving marketing strategies, customers\u2019 satisfaction and loyalty? the role of open innovation. Int. J. Data Netw. Sci. 6(2), 449\u2013460 (2022)","journal-title":"Int. J. Data Netw. Sci."},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Yang, M., Zhou, M., Liu, J., Lian, D., King, I.: HRCF: enhancing collaborative filtering via hyperbolic geometric regularization. In: Proceedings of the ACM Web Conference 2022, pp. 2462\u20132471 (2022)","DOI":"10.1145\/3485447.3512118"},{"key":"28_CR5","unstructured":"Li, Y., Liu, J., Jin, Y., Fan, X., Wang, B.: Crowdfunding platform recommendation algorithm based on collaborative filtering. J. Eng. Res. (2022)"},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Xu, K., Yang, J., Xu, J., Gao, S., Guo, J., Wen, J.R.: Adapting user preference to online feedback in multi-round conversational recommendation. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 364\u2013372 (2021)","DOI":"10.1145\/3437963.3441791"},{"issue":"2","key":"28_CR7","doi-asserted-by":"publisher","first-page":"60","DOI":"10.3390\/info14020060","volume":"14","author":"H Zhang","year":"2023","unstructured":"Zhang, H., Shen, Z.: News recommendation based on user topic and entity preferences in historical behavior. Information 14(2), 60 (2023)","journal-title":"Information"},{"issue":"3","key":"28_CR8","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1007\/s10791-022-09410-1","volume":"25","author":"N Godavarthy","year":"2022","unstructured":"Godavarthy, N., Wang, Y., Ebesu, T., Suthee, U., Xie, M., Fang, Y.: Learning user preferences through online conversations via personalized memory transfer. Inf. Retrieval J. 25(3), 306\u2013328 (2022)","journal-title":"Inf. Retrieval J."},{"issue":"1","key":"28_CR9","first-page":"15","volume":"43","author":"H Yao","year":"2023","unstructured":"Yao, H., Ye, D., Chen, Z.: Multi-round conversational reinforcement learning recommendation algorithm via multi-granularity feedback. J. Comput. Appl. 43(1), 15 (2023)","journal-title":"J. Comput. Appl."},{"key":"28_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/8882813","volume":"2020","author":"Z Li","year":"2020","unstructured":"Li, Z., Zhang, L., Lei, C., Chen, X., Gao, J., Gao, J.: Attention with long-term interval-based deep sequential learning for recommendation. Complexity 2020, 1\u201313 (2020)","journal-title":"Complexity"},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"Ying, H., et al.: Sequential recommender system based on hierarchical attention network. In: IJCAI International Joint Conference on Artificial Intelligence (2018)","DOI":"10.24963\/ijcai.2018\/546"},{"key":"28_CR12","doi-asserted-by":"crossref","unstructured":"Luo, M., Zhang, X., Li, J., Duan, P., Lu, S.: User dynamic preference construction method based on behavior sequence. Sci. Program. 2022 (2022)","DOI":"10.1155\/2022\/6101045"},{"key":"28_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10844-022-00723-7","volume":"59","author":"J Lei","year":"2022","unstructured":"Lei, J., Li, Y., Yang, S., Shi, W., Wu, Y.: Two-stage sequential recommendation for side information fusion and long-term and short-term preferences modeling. J. Intell. Inf. Syst. 59, 1\u201321 (2022)","journal-title":"J. Intell. Inf. Syst."},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"Wu, B., He, X., Zhang, Q., Wang, M., Ye, Y.: GCRec: graph-augmented capsule network for next-item recommendation. IEEE Trans. Neural Netw. Learn. Syst. (2022)","DOI":"10.1109\/TNNLS.2022.3164982"},{"key":"28_CR15","doi-asserted-by":"crossref","unstructured":"Chang, J., et al.: Sequential recommendation with graph neural networks. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 378\u2013387 (2021)","DOI":"10.1145\/3404835.3462968"},{"issue":"1","key":"28_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3568022","volume":"1","author":"C Gao","year":"2023","unstructured":"Gao, C., et al.: A survey of graph neural networks for recommender systems: challenges, methods, and directions. ACM Trans. Recommender Syst. 1(1), 1\u201351 (2023)","journal-title":"ACM Trans. Recommender Syst."},{"key":"28_CR17","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1007\/978-981-16-6054-2_21","volume-title":"Graph Neural Networks: Foundations, Frontiers, and Applications","author":"B Liu","year":"2022","unstructured":"Liu, B., Wu, L.: Graph neural networks in natural language processing. In: Graph Neural Networks: Foundations, Frontiers, and Applications, pp. 463\u2013481. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-16-6054-2_21"},{"key":"28_CR18","doi-asserted-by":"crossref","unstructured":"Ghosh, S., Maji, S., Desarkar, M.S.: Graph neural network enhanced language models for efficient multilingual text classification. arXiv preprint arXiv:2203.02912 (2022)","DOI":"10.1145\/3501247.3531561"},{"key":"28_CR19","doi-asserted-by":"crossref","unstructured":"Xue, F.: Refining latent multi-view graph for relation extraction (2021)","DOI":"10.1609\/aaai.v35i16.17670"},{"key":"28_CR20","doi-asserted-by":"crossref","unstructured":"Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 346\u2013353 (2019)","DOI":"10.1609\/aaai.v33i01.3301346"},{"issue":"6","key":"28_CR21","doi-asserted-by":"publisher","first-page":"2161","DOI":"10.1007\/s11280-021-00961-9","volume":"24","author":"D Wang","year":"2021","unstructured":"Wang, D., Wang, X., Xiang, Z., Yu, D., Deng, S., Xu, G.: Attentive sequential model based on graph neural network for next poi recommendation. World Wide Web 24(6), 2161\u20132184 (2021). https:\/\/doi.org\/10.1007\/s11280-021-00961-9","journal-title":"World Wide Web"},{"key":"28_CR22","doi-asserted-by":"publisher","first-page":"26471","DOI":"10.1109\/ACCESS.2022.3156618","volume":"10","author":"Q Li","year":"2022","unstructured":"Li, Q., Xu, X., Liu, X., Chen, Q.: An attention-based spatiotemporal GGNN for next POI recommendation. IEEE Access 10, 26471\u201326480 (2022)","journal-title":"IEEE Access"},{"key":"28_CR23","doi-asserted-by":"publisher","first-page":"106511","DOI":"10.1016\/j.knosys.2020.106511","volume":"211","author":"G Zhu","year":"2021","unstructured":"Zhu, G., et al.: Neural attentive travel package recommendation via exploiting long-term and short-term behaviors. Knowl.-Based Syst. 211, 106511 (2021)","journal-title":"Knowl.-Based Syst."},{"key":"28_CR24","doi-asserted-by":"crossref","unstructured":"Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, pp. 811\u2013820 (2010)","DOI":"10.1145\/1772690.1772773"},{"key":"28_CR25","doi-asserted-by":"crossref","unstructured":"Wang, P., Guo, J., Lan, Y., Xu, J., Wan, S., Cheng, X.: Learning hierarchical representation model for nextbasket recommendation. In: Proceedings of the 38th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 403\u2013412 (2015)","DOI":"10.1145\/2766462.2767694"},{"key":"28_CR26","doi-asserted-by":"publisher","first-page":"1487","DOI":"10.1007\/s11042-020-09746-0","volume":"80","author":"D Yu","year":"2021","unstructured":"Yu, D., Wanyan, W., Wang, D.: Leveraging contextual influence and user preferences for point-of-interest recommendation. Multimedia Tools Appl. 80, 1487\u20131501 (2021)","journal-title":"Multimedia Tools Appl."}],"container-title":["Communications in Computer and Information Science","Service Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-4402-6_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T23:06:14Z","timestamp":1690412774000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-4402-6_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819944019","9789819944026"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-4402-6_28","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"27 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Service Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Harbin","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":"13 May 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 May 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icss22023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conf.ccf.org.cn\/ICSS2023","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":"71","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":"36","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":"2","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":"3","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","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)"}}]}}