{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:07:16Z","timestamp":1775815636741,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":41,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819681792","type":"print"},{"value":"9789819681808","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\/978-981-96-8180-8_23","type":"book-chapter","created":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T09:15:50Z","timestamp":1750324550000},"page":"291-303","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["ReducedGCN: Learning to\u00a0Adapt Graph Convolution for\u00a0Top-N Recommendation"],"prefix":"10.1007","author":[{"given":"Eungi","family":"Kim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chanwoo","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kwangeun","family":"Yeo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinri","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yujin","family":"Jeon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sewon","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0786-8086","authenticated-orcid":false,"given":"Joonseok","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,20]]},"reference":[{"key":"23_CR1","unstructured":"Berg, R., et\u00a0al.: Graph convolutional matrix completion. arXiv:1706.02263 (2017)"},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Chen, D., et\u00a0al.: Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. In: AAAI (2020)","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Chen, J., et\u00a0al.: Attentive collaborative filtering: multimedia recommendation with item-and component-level attention. In: SIGIR (2017)","DOI":"10.1145\/3077136.3080797"},{"key":"23_CR4","unstructured":"Chen, J., Ma, T., Xiao, C.: FastGCN: fast learning with graph convolutional networks via importance sampling. arXiv:1801.10247 (2018)"},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Chen, L., et\u00a0al.: Revisiting graph based collaborative filtering: a linear residual graph convolutional network approach. In: AAAI (2020)","DOI":"10.1609\/aaai.v34i01.5330"},{"key":"23_CR6","doi-asserted-by":"crossref","unstructured":"Chiang, W., Liu, X., Si, S., Li, Y., Bengio, S., Hsieh, C.: Cluster-GCN: an efficient algorithm for training deep and large graph convolutional networks. In: KDD (2019)","DOI":"10.1145\/3292500.3330925"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Choi, M., et\u00a0al.: Local collaborative autoencoders. In: WSDM (2021)","DOI":"10.1145\/3437963.3441808"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Choi, M., Kim, J., Lee, J., Shim, H., Lee, J.:. S-walk: accurate and scalable session-based recommendation with random walks. In: WSDM (2022)","DOI":"10.1145\/3488560.3498464"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Christakopoulou, E., Karypis, G.: Local item-item models for top-N recommendation. In: RecSys (2016)","DOI":"10.1145\/2959100.2959185"},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"Fan, Z., Xu, K., Dong, Z., Peng, H., Zhang, J., Yu, P.S.: Graph collaborative signals denoising and augmentation for recommendation. In: SIGIR (2023)","DOI":"10.1145\/3539618.3591994"},{"key":"23_CR11","unstructured":"Gori, M., Pucci, A., Roma, V., Siena, I.: ItemRank: a random-walk based scoring algorithm for recommender engines. In: IJCAI (2007)"},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"Guo, H., Tang, R.,Ye, Y., Li, Z., He, X.: DeepFM: a factorization-machine based neural network for CTR prediction. arXiv:1703.04247 (2017)","DOI":"10.24963\/ijcai.2017\/239"},{"key":"23_CR13","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: NIPS (2017)"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4) (2015)","DOI":"10.1145\/2827872"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"He, R., McAuley, J.: VBPR: visual Bayesian personalized ranking from implicit feedback. In: AAAI (2016)","DOI":"10.1609\/aaai.v30i1.9973"},{"issue":"1","key":"23_CR16","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1109\/TKDE.2016.2611584","volume":"29","author":"X He","year":"2016","unstructured":"He, X., et al.: BiRank: towards ranking on bipartite graphs. IEEE Trans. Knowl. Data Eng. 29(1), 57\u201371 (2016)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"23_CR17","doi-asserted-by":"crossref","unstructured":"He, X., et\u00a0al.: Neural collaborative filtering. In: WWW (2017)","DOI":"10.1145\/3038912.3052569"},{"key":"23_CR18","doi-asserted-by":"crossref","unstructured":"He, X., et\u00a0al.: LightGCN: simplifying and powering graph convolution network for recommendation. In: SIGIR (2020)","DOI":"10.1145\/3397271.3401063"},{"key":"23_CR19","doi-asserted-by":"crossref","unstructured":"Kim, J., et al.: Content-based graph reconstruction for cold-start item recommendation. In: SIGIR (2024)","DOI":"10.1145\/3626772.3657801"},{"key":"23_CR20","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016)"},{"issue":"8","key":"23_CR21","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30\u201337 (2009)","journal-title":"Computer"},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Lee, J., Bengio, S., Kim, S., Lebanon, G., Singer, Y.: Local collaborative ranking. In: WWW (2014)","DOI":"10.1145\/2566486.2567970"},{"key":"23_CR23","unstructured":"Lee, J., Kim, S., Lebanon, G., Singer, Y.: Local low-rank matrix approximation. In: ICML (2013)"},{"key":"23_CR24","unstructured":"Lee, J., Kim, S., Lebanon, G., Singer, Y., Bengio, S.: LLORMA: local low-rank matrix approximation. In: JMLR (2016)"},{"key":"23_CR25","doi-asserted-by":"crossref","unstructured":"Li, Q., Han, Z., Wu, X.-M.: Deeper insights into graph convolutional networks for semi-supervised learning. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"23_CR26","doi-asserted-by":"crossref","unstructured":"Liang, D., et\u00a0al.: Variational autoencoders for collaborative filter. In: WWW (2018)","DOI":"10.1145\/3178876.3186150"},{"key":"23_CR27","doi-asserted-by":"crossref","unstructured":"Lin, Z., Tian, C., Hou, Y., Zhao, W.X.: Improving graph collaborative filtering with neighborhood-enriched contrastive learning. In: WWW (2022)","DOI":"10.1145\/3485447.3512104"},{"key":"23_CR28","doi-asserted-by":"crossref","unstructured":"Liu, F., et\u00a0al.: Interest-aware message-passing GCN for recommend. In: WWW (2021)","DOI":"10.1145\/3442381.3449986"},{"key":"23_CR29","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: UAI (2009)"},{"key":"23_CR30","unstructured":"Rong, Y., Huang, W., Xu, T., Huang, J.: DropEdge: towards deep graph convolutional networks on node classification. arXiv:1907.10903 (2019)"},{"key":"23_CR31","doi-asserted-by":"crossref","unstructured":"Sedhain, S., et\u00a0al.: AutoRec: autoencoders meet collaborative filter. In: WWW (2015)","DOI":"10.1145\/2740908.2742726"},{"key":"23_CR32","doi-asserted-by":"crossref","unstructured":"Shenbin, I., et\u00a0al.: RecVAE: a new variational autoencoder for top-N recommendations with implicit feedback. In: WSDM (2020)","DOI":"10.1145\/3336191.3371831"},{"key":"23_CR33","doi-asserted-by":"crossref","unstructured":"Tian, C., Xie, Y., Li, Y., Yang, N., Zhao, W.X.: Learning to denoise unreliable interactions for graph collaborative filtering. In: SIGIR (2022)","DOI":"10.1145\/3477495.3531889"},{"key":"23_CR34","unstructured":"Veli\u010dkovi\u0107, P., et\u00a0al.: Graph attention networks. arXiv:1710.10903 (2017)"},{"key":"23_CR35","doi-asserted-by":"crossref","unstructured":"Wang, X., et\u00a0al.: Neural graph collaborative filtering. In: SIGIR (2019)","DOI":"10.1145\/3331184.3331267"},{"key":"23_CR36","doi-asserted-by":"crossref","unstructured":"Wang, X., et\u00a0al.: Disentangled graph collaborative filtering. In: SIGIR (2020)","DOI":"10.1145\/3397271.3401137"},{"key":"23_CR37","doi-asserted-by":"crossref","unstructured":"Wu, J., et\u00a0al.: Self-supervised graph learning for recommendation. In: SIGIR (2021)","DOI":"10.1145\/3404835.3462862"},{"key":"23_CR38","doi-asserted-by":"crossref","unstructured":"Wu, W., et\u00a0al.: AFDGCF: adaptive feature de-correlation graph collaborative filtering for recommendations. In: SIGIR (2024)","DOI":"10.1145\/3626772.3657724"},{"key":"23_CR39","doi-asserted-by":"crossref","unstructured":"Xia, L., et\u00a0al.: Hypergraph contrastive collaborative filtering. In: SIGIR (2022)","DOI":"10.1145\/3477495.3532058"},{"key":"23_CR40","doi-asserted-by":"crossref","unstructured":"Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: KDD (2018)","DOI":"10.1145\/3219819.3219890"},{"key":"23_CR41","unstructured":"Zhou, K., Huang, X., Li, Y., Zha, D., Chen, R., Hu, X.: Towards deeper graph neural networks with differentiable group normalization. In: NeurIPS (2020)"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-8180-8_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T20:32:55Z","timestamp":1757190775000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-8180-8_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819681792","9789819681808"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-8180-8_23","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":"20 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","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":"10 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2025.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}