{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:47:36Z","timestamp":1742996856673,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030821357"},{"type":"electronic","value":"9783030821364"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-82136-4_19","type":"book-chapter","created":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T23:26:36Z","timestamp":1628292396000},"page":"229-241","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SEGAR: Knowledge Graph Augmented Session-Based Recommendation"],"prefix":"10.1007","author":[{"given":"Xinyi","family":"Xu","sequence":"first","affiliation":[]},{"given":"Yan","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Zhuoming","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,7]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","first-page":"50306","DOI":"10.1109\/ACCESS.2018.2868516","volume":"6","author":"D Chen","year":"2018","unstructured":"Chen, D., Zhao, H.: Research on the method of extracting domain knowledge from the freebase RDF dumps. IEEE Access 6, 50306\u201350322 (2018)","journal-title":"IEEE Access"},{"issue":"3","key":"19_CR2","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1109\/MCOM.2018.1700274","volume":"56","author":"M Chen","year":"2018","unstructured":"Chen, M., Zhang, Y., Qiu, M., Guizani, N., Hao, Y.: SPHA: smart personal health advisor based on deep analytics. IEEE Commun. Mag. 56(3), 164\u2013169 (2018)","journal-title":"IEEE Commun. Mag."},{"key":"19_CR3","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.neucom.2020.08.023","volume":"419","author":"P Gu","year":"2021","unstructured":"Gu, P., Han, Y., Gao, W., Xu, G., Wu, J.: Enhancing session-based social recommendation through item graph embedding and contextual friendship modeling. Neurocomputing 419, 190\u2013202 (2021)","journal-title":"Neurocomputing"},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Huang, J., Ren, Z., Zhao, W.X., He, G., Wen, J.R., Dong, D.: Taxonomy-aware multi-hop reasoning networks for sequential recommendation. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 573\u2013581 (2019)","DOI":"10.1145\/3289600.3290972"},{"key":"19_CR5","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7\u20139 May 2015, Conference Track Proceedings (2015)"},{"key":"19_CR6","doi-asserted-by":"crossref","unstructured":"Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1419\u20131428 (2017)","DOI":"10.1145\/3132847.3132926"},{"key":"19_CR7","unstructured":"Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 25\u201330 January 2015, pp. 2181\u20132187 (2015)"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Liu, Q., Zeng, Y., Mokhosi, R., Zhang, H.: Stamp: short-term attention\/memory priority model for session-based recommendation. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1831\u20131839 (2018)","DOI":"10.1145\/3219819.3219950"},{"key":"19_CR9","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":"19_CR10","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 417\u2013426 (2018)","DOI":"10.1145\/3269206.3271739"},{"key":"19_CR11","unstructured":"Wang, H., Zhao, M., et al.: Knowledge graph convolutional networks for recommender systems. In: The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, 13\u201317 May 2019, pp. 3307\u20133313. ACM (2019)"},{"key":"19_CR12","doi-asserted-by":"crossref","unstructured":"Wang, X., He, X., Cao, Y., Liu, M., Chua, T.: KGAT: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, 4\u20138 August 2019, pp. 950\u2013958. ACM (2019)","DOI":"10.1145\/3292500.3330989"},{"key":"19_CR13","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.knosys.2016.06.028","volume":"109","author":"C Wu","year":"2016","unstructured":"Wu, C., Wang, J., Liu, J., Liu, W.: Recurrent neural network based recommendation for time heterogeneous feedback. Knowl.-Based Syst. 109, 90\u2013103 (2016)","journal-title":"Knowl.-Based Syst."},{"key":"19_CR14","first-page":"346","volume":"33","author":"S Wu","year":"2019","unstructured":"Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. Proc. AAAI Conf. Artif. Intell. 33, 346\u2013353 (2019)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"19_CR15","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/978-3-030-55393-7_13","volume-title":"Knowledge Science, Engineering and Management","author":"C Zhang","year":"2020","unstructured":"Zhang, C., Nie, J.: Spatio-temporal attentive network for session-based recommendation. In: Li, G., Shen, H.T., Yuan, Y., Wang, X., Liu, H., Zhao, X. (eds.) KSEM 2020. LNCS (LNAI), vol. 12275, pp. 131\u2013139. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-55393-7_13"}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-82136-4_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T23:28:01Z","timestamp":1628292481000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-82136-4_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030821357","9783030821364"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-82136-4_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tokyo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 August 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ksem2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.cloud-conf.net\/ksem21\/index.html","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":"492","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":"164","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":"33% - 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":"10","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)"}}]}}