{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T16:15:40Z","timestamp":1783700140475,"version":"3.55.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T00:00:00Z","timestamp":1695600000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T00:00:00Z","timestamp":1695600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Open Projects of the Technology Innovation Center of Cultural Tourism Big Data of Hebei Province","award":["SG2019036-zd202205"],"award-info":[{"award-number":["SG2019036-zd202205"]}]},{"name":"Zhejiang Lab Open Research Project","award":["K2022KG0AB03"],"award-info":[{"award-number":["K2022KG0AB03"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61702043"],"award-info":[{"award-number":["61702043"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2018YFB1402600"],"award-info":[{"award-number":["2018YFB1402600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61972178"],"award-info":[{"award-number":["61972178"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s10115-023-01986-4","type":"journal-article","created":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T19:01:36Z","timestamp":1695668496000},"page":"1111-1134","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["SR-HetGNN: session-based recommendation with heterogeneous graph neural network"],"prefix":"10.1007","volume":"66","author":[{"given":"Jinpeng","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xudong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Senzhang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaimin","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,9,25]]},"reference":[{"key":"1986_CR1","unstructured":"Wang S, Cao L, Wang Y (2019) A survey on session-based recommender systems. arXiv preprint arXiv:1902.04864"},{"key":"1986_CR2","doi-asserted-by":"crossref","unstructured":"Wu S, Tang Y, Zhu Y, Wang L, Xie X, Tan T (2019) Session-based recommendation with graph neural networks. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 346\u2013353","DOI":"10.1609\/aaai.v33i01.3301346"},{"issue":"11","key":"1986_CR3","doi-asserted-by":"publisher","first-page":"992","DOI":"10.14778\/3402707.3402736","volume":"4","author":"Y Sun","year":"2011","unstructured":"Sun Y, Han J, Yan X, Yu PS, Wu T (2011) Pathsim: meta path-based top-k similarity search in heterogeneous information networks. Proc VLDB Endow 4(11):992\u20131003","journal-title":"Proc VLDB Endow"},{"key":"1986_CR4","doi-asserted-by":"crossref","unstructured":"Zhang C, Song D, Huang C, Swami A, Chawla NV (2019) Heterogeneous graph neural network. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, pp 793\u2013803","DOI":"10.1145\/3292500.3330961"},{"key":"1986_CR5","doi-asserted-by":"crossref","unstructured":"Barajas JM, Li X (2005) Collaborative filtering on data streams. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, pp 429\u2013436","DOI":"10.1007\/11564126_42"},{"key":"1986_CR6","doi-asserted-by":"crossref","unstructured":"Koren Y, Bell RM (2015) Advances in collaborative filtering. In: Recommender systems handbook, pp 77\u2013118","DOI":"10.1007\/978-1-4899-7637-6_3"},{"key":"1986_CR7","doi-asserted-by":"crossref","unstructured":"Rendle S, Freudenthaler C, Schmidt-Thieme L (2010) Factorizing personalized Markov chains for next-basket recommendation. In: Proceedings of the 19th international conference on World Wide Web, pp 811\u2013820","DOI":"10.1145\/1772690.1772773"},{"key":"1986_CR8","unstructured":"Rendle S, Freudenthaler C, Gantner Z, Schmidtthieme L (2009) Bpr: Bayesian personalized ranking from implicit feedback, pp 452\u2013461. arXiv preprint arXiv:1205.2618"},{"key":"1986_CR9","unstructured":"Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2015) Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939"},{"key":"1986_CR10","doi-asserted-by":"crossref","unstructured":"Li J, Ren P, Chen Z, Ren Z, Lian T, Ma J (2017) Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 1419\u20131428","DOI":"10.1145\/3132847.3132926"},{"key":"1986_CR11","doi-asserted-by":"crossref","unstructured":"Liu Q, Zeng Y, Mokhosi R, Zhang H (2018) STAMP: short-term attention\/memory priority model for session-based recommendation. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1831\u20131839","DOI":"10.1145\/3219819.3219950"},{"key":"1986_CR12","unstructured":"Fang J (2021) Session-based recommendation with self-attention networks. arXiv preprint arXiv:2102.01922"},{"key":"1986_CR13","doi-asserted-by":"crossref","unstructured":"Guo C, Zhang M, Fang J, Jin J, Pan M (2020) Session-based recommendation with hierarchical leaping networks. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp 1705\u20131708","DOI":"10.1145\/3397271.3401217"},{"key":"1986_CR14","unstructured":"Zhou J, Cui G, Zhang Z, Yang C, Liu Z, Sun M (2018) Graph neural networks: a review of methods and applications. CoRR arXiv:1812.08434"},{"key":"1986_CR15","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2020) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32:4\u201324","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"1986_CR16","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1145\/3382764","volume":"38","author":"R Qiu","year":"2020","unstructured":"Qiu R, Huang Z, Li J, Yin H (2020) Exploiting cross-session information for session-based recommendation with graph neural networks. ACM Trans Inf Syst 38(3):22\u201312223","journal-title":"ACM Trans Inf Syst"},{"issue":"2","key":"1986_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3446342","volume":"12","author":"J Chen","year":"2021","unstructured":"Chen J, Li K, Li K, Yu PS, Zeng Z (2021) Dynamic planning of bicycle stations in dockless public bicycle-sharing system using gated graph neural network. ACM Trans Intell Syst Technol 12(2):1\u201322","journal-title":"ACM Trans Intell Syst Technol"},{"key":"1986_CR18","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.knosys.2018.03.022","volume":"151","author":"P Goyal","year":"2018","unstructured":"Goyal P, Ferrara E (2018) Graph embedding techniques, applications, and performance: a survey. Knowl Based Syst 151:78\u201394","journal-title":"Knowl Based Syst"},{"key":"1986_CR19","doi-asserted-by":"crossref","unstructured":"Wang Z, Wei W, Cong G, Li X, Mao X, Qiu M (2020) Global context enhanced graph neural networks for session-based recommendation. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp 169\u2013178","DOI":"10.1145\/3397271.3401142"},{"key":"1986_CR20","doi-asserted-by":"crossref","unstructured":"Yu F, Zhu Y, Liu Q, Wu S, Wang L, Tan T(2020) Tagnn: target attentive graph neural networks for session-based recommendation. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp 1921\u20131924","DOI":"10.1145\/3397271.3401319"},{"key":"1986_CR21","doi-asserted-by":"crossref","unstructured":"Pan Z, Cai F, Chen W, Chen H, De\u00a0Rijke M (2020) Star graph neural networks for session-based recommendation. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp 1195\u20131204","DOI":"10.1145\/3340531.3412014"},{"key":"1986_CR22","doi-asserted-by":"crossref","unstructured":"Wang J, Ding K, Zhu Z, Caverlee J (2021) Session-based recommendation with hypergraph attention networks. In: Proceedings of the 2021 SIAM international conference on data mining (SDM), pp 82\u201390. SIAM","DOI":"10.1137\/1.9781611976700.10"},{"key":"1986_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117114","volume":"202","author":"H Wang","year":"2022","unstructured":"Wang H, Zeng Y, Chen J, Zhao Z, Chen H (2022) A spatiotemporal graph neural network for session-based recommendation. Expert Syst Appl 202:117114. https:\/\/doi.org\/10.1016\/j.eswa.2022.117114","journal-title":"Expert Syst Appl"},{"key":"1986_CR24","doi-asserted-by":"crossref","unstructured":"Xia X, Yin H, Yu J, Wang Q, Cui L, Zhang X (2021) Self-supervised hypergraph convolutional networks for session-based recommendation. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 4503\u20134511","DOI":"10.1609\/aaai.v35i5.16578"},{"key":"1986_CR25","doi-asserted-by":"crossref","unstructured":"Guo J, Yang Y, Song X, Zhang Y, Wang Y, Bai J, Zhang Y (2022) Learning multi-granularity consecutive user intent unit for session-based recommendation. In: Proceedings of the fifteenth ACM international conference on web search and data mining, pp 343\u2013352","DOI":"10.1145\/3488560.3498524"},{"issue":"3","key":"1986_CR26","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1145\/2500492","volume":"7","author":"Y Sun","year":"2013","unstructured":"Sun Y, Norick B, Han J, Yan X, Yu PS, Yu X (2013) Pathselclus: integrating meta-path selection with user-guided object clustering in heterogeneous information networks. ACM Trans Knowl Discov Data 7(3):11\u201311123","journal-title":"ACM Trans Knowl Discov Data"},{"key":"1986_CR27","doi-asserted-by":"crossref","unstructured":"Yang D, Wang Z, Jiang J, Xiao Y (2019) Knowledge embedding towards the recommendation with sparse user-item interactions. In: ASONAM \u201919: international conference on advances in social networks analysis and mining, Vancouver, British Columbia, Canada, 27\u201330 August, 2019, pp 325\u2013332","DOI":"10.1145\/3341161.3342876"},{"issue":"5","key":"1986_CR28","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1109\/TKDE.2018.2849727","volume":"31","author":"P Cui","year":"2019","unstructured":"Cui P, Wang X, Pei J, Zhu W (2019) A survey on network embedding. IEEE Trans Knowl Data Eng 31(5):833\u2013852","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1986_CR29","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"key":"1986_CR30","doi-asserted-by":"crossref","unstructured":"Dong Y, Chawla NV, Swami A (2017) metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 135\u2013144","DOI":"10.1145\/3097983.3098036"},{"key":"1986_CR31","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"key":"1986_CR32","doi-asserted-by":"crossref","unstructured":"Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) LINE: large-scale information network embedding. In: Proceedings of the 24th international conference on World Wide Web, pp 1067\u20131077","DOI":"10.1145\/2736277.2741093"},{"key":"1986_CR33","doi-asserted-by":"crossref","unstructured":"Ren X, Liu J, Yu X, Khandelwal U, Gu Q, Wang L, Han J (2014) Cluscite: effective citation recommendation by information network-based clustering. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp 821\u2013830","DOI":"10.1145\/2623330.2623630"},{"key":"1986_CR34","doi-asserted-by":"crossref","unstructured":"Hu B, Shi C, Zhao WX, Yu PS (2018) Leveraging meta-path based context for top- N recommendation with A neural co-attention model. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1531\u20131540","DOI":"10.1145\/3219819.3219965"},{"key":"1986_CR35","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3101356","author":"Y Yang","year":"2021","unstructured":"Yang Y, Guan Z, Li J, Zhao W, Cui J, Wang Q (2021) Interpretable and efficient heterogeneous graph convolutional network. IEEE Trans Knowl Data Eng. https:\/\/doi.org\/10.1109\/TKDE.2021.3101356","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1986_CR36","doi-asserted-by":"crossref","unstructured":"Jin D, Huo C, Liang C, Yang L (2021) Heterogeneous graph neural network via attribute completion. In: Proceedings of the web conference, 2021, pp 391\u2013400","DOI":"10.1145\/3442381.3449914"},{"key":"1986_CR37","doi-asserted-by":"crossref","unstructured":"Wang Z, Wei W, Cong G, Li X-L, Mao X-L, Qiu, M (2020) Global context enhanced graph neural networks for session-based recommendation. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp 169\u2013178","DOI":"10.1145\/3397271.3401142"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-023-01986-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-023-01986-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-023-01986-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T14:08:18Z","timestamp":1705500498000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-023-01986-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,25]]},"references-count":37,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["1986"],"URL":"https:\/\/doi.org\/10.1007\/s10115-023-01986-4","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,25]]},"assertion":[{"value":"22 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 July 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 September 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}