{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T06:34:49Z","timestamp":1747809289363,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031001253"},{"type":"electronic","value":"9783031001260"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-00126-0_3","type":"book-chapter","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T18:07:55Z","timestamp":1650996475000},"page":"36-52","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Fully Utilizing Neighbors for Session-Based Recommendation with Graph Neural Networks"],"prefix":"10.1007","author":[{"given":"Xingyu","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Chaofeng","family":"Sha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,8]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Chen, T., Wong, R.C.: Handling information loss of graph neural networks for session-based recommendation. In: KDD, pp. 1172\u20131180. ACM (2020)","DOI":"10.1145\/3394486.3403170"},{"key":"3_CR2","unstructured":"Gupta, P., Garg, D., Malhotra, P., Vig, L., Shroff, G.: NISER: normalized item and session representations with graph neural networks. CoRR abs\/1909.04276 (2019)"},{"key":"3_CR3","unstructured":"Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: ICLR (Poster) (2016)"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Jannach, D., Ludewig, M.: When recurrent neural networks meet the neighborhood for session-based recommendation. In: RecSys, pp. 306\u2013310. ACM (2017)","DOI":"10.1145\/3109859.3109872"},{"key":"3_CR5","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (Poster) (2015)"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Klicpera, J., Bojchevski, A., G\u00fcnnemann, S.: Predict then propagate: graph neural networks meet personalized PageRank. In: ICLR (Poster) (2019). OpenReview.net","DOI":"10.1145\/3394486.3403296"},{"key":"3_CR7","unstructured":"Klicpera, J., Wei\u00dfenberger, S., G\u00fcnnemann, S.: Diffusion improves graph learning. In: NeurIPS, pp. 13333\u201313345 (2019)"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: CIKM, pp. 1419\u20131428. ACM (2017)","DOI":"10.1145\/3132847.3132926"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Li, Q., Han, Z., Wu, X.: Deeper insights into graph convolutional networks for semi-supervised learning. In: AAAI, pp. 3538\u20133545. AAAI Press (2018)","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"3_CR10","unstructured":"Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.S.: Gated graph sequence neural networks. In: ICLR (Poster) (2016)"},{"key":"3_CR11","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: KDD, pp. 1831\u20131839. ACM (2018)","DOI":"10.1145\/3219819.3219950"},{"key":"3_CR12","unstructured":"Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Technical report, Stanford InfoLab (1999)"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Pan, Z., Cai, F., Chen, W., Chen, H., de Rijke, M.: Star graph neural networks for session-based recommendation. In: CIKM, pp. 1195\u20131204. ACM (2020)","DOI":"10.1145\/3340531.3412014"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Qiu, R., Li, J., Huang, Z., Yin, H.: Rethinking the item order in session-based recommendation with graph neural networks. In: CIKM, pp. 579\u2013588. ACM (2019)","DOI":"10.1145\/3357384.3358010"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized Markov chains for next-basket recommendation. In: WWW, pp. 811\u2013820. ACM (2010)","DOI":"10.1145\/1772690.1772773"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285\u2013295. ACM (2001)","DOI":"10.1145\/371920.372071"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Tan, Y.K., Xu, X., Liu, Y.: Improved recurrent neural networks for session-based recommendations. In: DLRS@RecSys, pp. 17\u201322. ACM (2016)","DOI":"10.1145\/2988450.2988452"},{"key":"3_CR18","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: ICLR (Poster) (2018). OpenReview.net"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Wang, G., Ying, R., Huang, J., Leskovec, J.: Multi-hop attention graph neural networks. In: IJCAI, pp. 3089\u20133096 (2021). ijcai.org","DOI":"10.24963\/ijcai.2021\/425"},{"key":"3_CR20","unstructured":"Wang, M., et al.: Deep graph library: a graph-centric, highly-performant package for graph neural networks. arXiv preprint arXiv:1909.01315 (2019)"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wei, W., Cong, G., Li, X., Mao, X., Qiu, M.: Global context enhanced graph neural networks for session-based recommendation. In: SIGIR, pp. 169\u2013178. ACM (2020)","DOI":"10.1145\/3397271.3401142"},{"key":"3_CR22","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: AAAI, pp. 346\u2013353. AAAI Press (2019)","DOI":"10.1609\/aaai.v33i01.3301346"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Xu, C., et al.: Graph contextualized self-attention network for session-based recommendation. In: IJCAI, pp. 3940\u20133946 (2019)","DOI":"10.24963\/ijcai.2019\/547"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Yu, F., Zhu, Y., Liu, Q., Wu, S., Wang, L., Tan, T.: TAGNN: target attentive graph neural networks for session-based recommendation. In: SIGIR, pp. 1921\u20131924. ACM (2020)","DOI":"10.1145\/3397271.3401319"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-00126-0_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T16:02:35Z","timestamp":1675440155000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-00126-0_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031001253","9783031001260"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-00126-0_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"8 April 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 April 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dasfaa2022.org\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"543","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":"72","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":"76","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":"13% - 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":"6","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)"}},{"value":"Conference was originally planned to take place in Hyberabad, India. 24 other papers are included in the volume.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}