{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T03:05:15Z","timestamp":1779419115587,"version":"3.53.1"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819500086","type":"print"},{"value":"9789819500093","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-95-0009-3_26","type":"book-chapter","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T13:23:55Z","timestamp":1753363435000},"page":"303-313","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LTL-GCL:A More Efficient Layer-to-Layer Graph Contrastive Learning Method for Recommender System"],"prefix":"10.1007","author":[{"given":"Haoyang","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaying","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanlong","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongrui","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,25]]},"reference":[{"key":"26_CR1","doi-asserted-by":"crossref","unstructured":"Smith, B., Linden, G.: Two decades of recommender systems at Amazon. com. IEEE Internet Comput. 21(3), 12\u201318 (2017)","DOI":"10.1109\/MIC.2017.72"},{"key":"26_CR2","unstructured":"Van den Oord, A., Dieleman, S., Schrauwen, B.: Deep content-based music recommendation. In: Advances in Neural Information Processing Systems, vol. 26 (2013)"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Linden, G., Smith, B., York, J.: Amazon.com recommendations: Item-to-item collab-orative filtering. IEEE Internet Comput. 7(1), 76\u201380 (2003)","DOI":"10.1109\/MIC.2003.1167344"},{"key":"26_CR4","unstructured":"Berg, R.V.D., Kipf, T.N., Welling, M.: Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 (2017)"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Wu, J., et al.: Self-supervised graph learning for recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 726\u2013735. ACM (2021)","DOI":"10.1145\/3404835.3462862"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Graph contrastive learning with adaptive augmentation. In: Proceedings of the Web Conference 2021, pp. 2069\u20132080. ACM (2021)","DOI":"10.1145\/3442381.3449802"},{"key":"26_CR7","doi-asserted-by":"crossref","unstructured":"Zhuo, J., et al.: Improving graph contrastive learning via adaptive positive sampling. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 23179\u201323187. IEEE (2024)","DOI":"10.1109\/CVPR52733.2024.02187"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 165\u2013174. ACM (2019)","DOI":"10.1145\/3331184.3331267"},{"key":"26_CR9","doi-asserted-by":"crossref","unstructured":"He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: LightGCN: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 639\u2013648. ACM (2020)","DOI":"10.1145\/3397271.3401063"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Mao, K., Zhu, J., Xiao, X., Lu, B., Wang, Z., He, X.: UltraGCN: ultra simplification of graph convolutional networks for recommendation. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 1253\u20131262. ACM (2021)","DOI":"10.1145\/3459637.3482291"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"Procopio, L., Tripodi, R., Navigli, R.: SGL: speaking the graph languages of semantic parsing via multilingual translation. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 325\u2013337. ACL (2021)","DOI":"10.18653\/v1\/2021.naacl-main.30"},{"issue":"3","key":"26_CR12","doi-asserted-by":"publisher","first-page":"2089","DOI":"10.1007\/s10115-023-02022-1","volume":"66","author":"C Liu","year":"2024","unstructured":"Liu, C., Yu, C., Gui, N., Yu, Z., Deng, S.: SimGCL: graph contrastive learning by finding homophily in heterophily. Knowl. Inf. Syst. 66(3), 2089\u20132114 (2024)","journal-title":"Knowl. Inf. Syst."},{"key":"26_CR13","unstructured":"Cai, X., Huang, C., Xia, L., Ren, X.: LightGCL: simple yet effective graph contrastive learning for recommendation. arXiv preprint arXiv:2302.08191 (2023)"},{"issue":"3","key":"26_CR14","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1145\/65943.65945","volume":"7","author":"V Raghavan","year":"1989","unstructured":"Raghavan, V., Bollmann, P., Jung, G.S.: A critical investigation of recall and precision as measures of retrieval system performance. ACM Trans. Inf. Syst. 7(3), 205\u2013229 (1989)","journal-title":"ACM Trans. Inf. Syst."},{"key":"26_CR15","unstructured":"Wang, Y., Wang, L., Li, Y., He, D., Liu, T.Y.: A theoretical analysis of NDCG type ranking measures. In: Conference on Learning Theory, pp. 25\u201354. PMLR (2013)"},{"key":"26_CR16","unstructured":"Diederik, K.: Adam: a method for stochastic optimization (2014)"},{"key":"26_CR17","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neu-ral networks. In: Proceedings of the 13th International Conference on Artificial Intelli-gence and Statistics, pp. 249\u2013256. JMLR (2010)"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Xia, L., Huang, C., Xu, Y., Zhao, J., Yin, D., Huang, J.: Hypergraph contrastive collaborative filtering. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 70\u201379. ACM (2022)","DOI":"10.1145\/3477495.3532058"},{"key":"26_CR19","doi-asserted-by":"crossref","unstructured":"Xia, L., Huang, C., Zhang, C.: Self-supervised hypergraph transformer for recommender systems. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2100\u20132109. ACM (2022)","DOI":"10.1145\/3534678.3539473"},{"key":"26_CR20","unstructured":"Oord, A.V.D., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-0009-3_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T02:22:24Z","timestamp":1779416544000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-0009-3_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819500086","9789819500093"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-0009-3_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"25 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}