{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:31:21Z","timestamp":1772119881175,"version":"3.50.1"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T00:00:00Z","timestamp":1727827200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T00:00:00Z","timestamp":1727827200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72271024"],"award-info":[{"award-number":["72271024"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["World Wide Web"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s11280-024-01306-y","type":"journal-article","created":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T22:01:42Z","timestamp":1727820102000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SocialCU: integrating commonalities and uniqueness of users and items for social recommendation"],"prefix":"10.1007","volume":"27","author":[{"given":"Shuo","family":"Li","sequence":"first","affiliation":[]},{"given":"Mingxin","family":"Gan","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,2]]},"reference":[{"key":"1306_CR1","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1016\/j.future.2017.04.028","volume":"93","author":"F Amato","year":"2019","unstructured":"Amato, F., Moscato, V., Picariello, A., Piccialli, F.: Sos: a multimedia recommender system for online social networks. Futur. Gener. Comput. Syst. 93, 914\u2013923 (2019). https:\/\/doi.org\/10.1016\/j.future.2017.04.028","journal-title":"Futur. Gener. Comput. Syst."},{"key":"1306_CR2","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.future.2021.01.017","volume":"119","author":"J Li","year":"2021","unstructured":"Li, J., Yang, G.: Network embedding enhanced intelligent recommendation for online social networks. Futur. Gener. Comput. Syst. 119, 68\u201376 (2021). https:\/\/doi.org\/10.1016\/j.future.2021.01.017","journal-title":"Futur. Gener. Comput. Syst."},{"key":"1306_CR3","doi-asserted-by":"publisher","unstructured":"Long, X., Huang, C., Xu, Y., Xu, H., Dai, P., Xia, L., Bo, L.: Social recommendation with self-supervised metagraph informax network. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. CIKM \u201921, pp. 1160\u20131169. Association for Computing Machinery, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3459637.3482480","DOI":"10.1145\/3459637.3482480"},{"key":"1306_CR4","doi-asserted-by":"publisher","unstructured":"Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the Fourth ACM Conference on Recommender Systems. RecSys \u201910, pp. 135\u2013142. Association for Computing Machinery, New York, USA (2010). https:\/\/doi.org\/10.1145\/1864708.1864736","DOI":"10.1145\/1864708.1864736"},{"key":"1306_CR5","doi-asserted-by":"publisher","first-page":"106437","DOI":"10.1016\/j.engappai.2023.106437","volume":"123","author":"Z Hu","year":"2023","unstructured":"Hu, Z., Zhou, X., He, Z., Yang, Z., Chen, J., Huang, J.: Discrete limited attentional collaborative filtering for fast social recommendation. Eng. Appl. Artif. Intell. 123, 106437 (2023). https:\/\/doi.org\/10.1016\/j.engappai.2023.106437","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"8","key":"1306_CR6","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). https:\/\/doi.org\/10.1109\/MC.2009.263","journal-title":"Computer"},{"key":"1306_CR7","doi-asserted-by":"publisher","unstructured":"Zhang, C., Yu, L., Wang, Y., Shah, C., Zhang, X.: Collaborative User Network Embedding for Social Recommender Systems, pp. 381\u2013389. https:\/\/doi.org\/10.1137\/1.9781611974973.43","DOI":"10.1137\/1.9781611974973.43"},{"key":"1306_CR8","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.future.2023.04.029","volume":"147","author":"NR Kermany","year":"2023","unstructured":"Kermany, N.R., Zhao, W., Batsuuri, T., Yang, J., Wu, J.: Incorporating user rating credibility in recommender systems. Futur. Gener. Comput. Syst. 147, 30\u201343 (2023). https:\/\/doi.org\/10.1016\/j.future.2023.04.029","journal-title":"Futur. Gener. Comput. Syst."},{"key":"1306_CR9","doi-asserted-by":"publisher","first-page":"106409","DOI":"10.1016\/j.engappai.2023.106409","volume":"123","author":"L Guo","year":"2023","unstructured":"Guo, L., Luan, K., Sun, L., Luo, Y., Zheng, X.: Collaborative filtering recommendations based on multi-factor random walks. Eng. Appl. Artif. Intell. 123, 106409 (2023). https:\/\/doi.org\/10.1016\/j.engappai.2023.106409","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"1","key":"1306_CR10","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2009","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Networks 20(1), 61\u201380 (2009). https:\/\/doi.org\/10.1109\/TNN.2008.2005605","journal-title":"IEEE Trans. Neural Networks"},{"key":"1306_CR11","unstructured":"Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: Signal processing on graphs: extending high-dimensional data analysis to networks and other irregular data domains. CoRR. abs\/1211.0053 (2012)"},{"key":"1306_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2021.08.018","volume":"580","author":"Z Lin","year":"2021","unstructured":"Lin, Z., Feng, L., Yin, R., Xu, C., Kwoh, C.K.: Glimg: Global and local item graphs for top-n recommender systems. Inf. Sci. 580, 1\u201314 (2021). https:\/\/doi.org\/10.1016\/j.ins.2021.08.018","journal-title":"Inf. Sci."},{"key":"1306_CR13","doi-asserted-by":"publisher","unstructured":"Zhang, J., Ma, C., Zhong, C., Zhao, P., Mu, X.: Combining feature importance and neighbor node interactions for cold start recommendation. Eng. Appl. Artif. Intell. 112(C) (2022). https:\/\/doi.org\/10.1016\/j.engappai.2022.104864","DOI":"10.1016\/j.engappai.2022.104864"},{"key":"1306_CR14","doi-asserted-by":"publisher","unstructured":"Wu, L., Sun, P., Fu, Y., Hong, R., Wang, X., Wang, M.: A neural influence diffusion model for social recommendation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR\u201919, pp. 235\u2013244. Association for Computing Machinery, New York, NY, USA (2019). https:\/\/doi.org\/10.1145\/3331184.3331214","DOI":"10.1145\/3331184.3331214"},{"key":"1306_CR15","doi-asserted-by":"publisher","first-page":"102142","DOI":"10.1016\/j.datak.2023.102142","volume":"145","author":"X Ma","year":"2023","unstructured":"Ma, X., Dong, L., Wang, Y., Li, Y., Liu, Z., Zhang, H.: An enhanced attentive implicit relation embedding for social recommendation. Data Knowl. Eng. 145, 102142 (2023). https:\/\/doi.org\/10.1016\/j.datak.2023.102142","journal-title":"Data Knowl. Eng."},{"issue":"26","key":"1306_CR16","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1007\/s11280-022-01094-3","volume":"60","author":"M Gan","year":"2023","unstructured":"Gan, M., Tan, C.: Mining multiple sequential patterns through multi-graph representation for next point-of-interest recommendation. World Wide Web. 60(26), 1345 (2023). https:\/\/doi.org\/10.1007\/s11280-022-01094-3","journal-title":"World Wide Web."},{"key":"1306_CR17","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1016\/j.ins.2022.01.001","volume":"589","author":"J Liao","year":"2022","unstructured":"Liao, J., Zhou, W., Luo, F., Wen, J., Gao, M., Li, X., Zeng, J.: Sociallgn: Light graph convolution network for social recommendation. Inf. Sci. 589, 595\u2013607 (2022). https:\/\/doi.org\/10.1016\/j.ins.2022.01.001","journal-title":"Inf. Sci."},{"key":"1306_CR18","doi-asserted-by":"publisher","unstructured":"Wang, X., He, X., Nie, L., Chua, T.-S.: Item silk road: Recommending items from information domains to social users. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR \u201917, pp. 185\u2013194. Association for Computing Machinery, New York, NY, USA (2017). https:\/\/doi.org\/10.1145\/3077136.3080771","DOI":"10.1145\/3077136.3080771"},{"key":"1306_CR19","doi-asserted-by":"publisher","unstructured":"Wu, L., Sun, P., Fu, Y., Hong, R., Wang, X., Wang, M.: A neural influence diffusion model for social recommendation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR\u201919, pp. 235\u2013244. Association for Computing Machinery, New York, NY, USA (2019). https:\/\/doi.org\/10.1145\/3331184.3331214","DOI":"10.1145\/3331184.3331214"},{"key":"1306_CR20","doi-asserted-by":"publisher","unstructured":"Zhang, Q., Wang, M., Wang, H., Rao, X., Chen, L.: Ssar-gnn: Self-supervised artist recommendation from spatio-temporal perspectives in art history with graph neural networks. Future Gener. Comput. Syst. 144(C), 230\u2013241 (2023) https:\/\/doi.org\/10.1016\/j.future.2023.03.003","DOI":"10.1016\/j.future.2023.03.003"},{"key":"1306_CR21","unstructured":"Liu, X., Zhang, F., Hou, Z., Wang, Z., Mian, L., Zhang, J., Tang, J.: Self-supervised learning: Generative or contrastive. CoRR. abs\/2006.08218 (2020)"},{"key":"1306_CR22","doi-asserted-by":"publisher","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: Proceedings of the 37th International Conference on Machine Learning. ICML\u201920. JMLR.org, (2020). https:\/\/doi.org\/10.5555\/3524938.3525087","DOI":"10.5555\/3524938.3525087"},{"key":"1306_CR23","doi-asserted-by":"publisher","unstructured":"Yu, J., Yin, H., Gao, M., Xia, X., Zhang, X., Viet\u00a0Hung, N.Q.: Socially-aware self-supervised tri-training for recommendation. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. KDD \u201921, pp. 2084\u20132092. Association for Computing Machinery, New York, USA (2021). https:\/\/doi.org\/10.1145\/3447548.3467340","DOI":"10.1145\/3447548.3467340"},{"key":"1306_CR24","doi-asserted-by":"publisher","first-page":"117755","DOI":"10.1016\/j.eswa.2022.117755","volume":"206","author":"X Feng","year":"2022","unstructured":"Feng, X., Liu, Z., Wu, W., Zuo, W.: Social recommendation via deep neural network-based multi-task learning. Expert Syst. Appl. 206, 117755 (2022). https:\/\/doi.org\/10.1016\/j.eswa.2022.117755","journal-title":"Expert Syst. Appl."},{"key":"1306_CR25","doi-asserted-by":"publisher","unstructured":"Ma, Y., Gan, M.: Deepassociate: A deep learning model exploring sequential influence and history-candidate association for sequence recommendation. Expert Syst. Appl. 185(C) (2021). https:\/\/doi.org\/10.1016\/j.eswa.2021.115587","DOI":"10.1016\/j.eswa.2021.115587"},{"key":"1306_CR26","doi-asserted-by":"publisher","unstructured":"Yu, J., Yin, H., Li, J., Wang, Q., Hung, N.Q.V., Zhang, X.: Self-supervised multi-channel hypergraph convolutional network for social recommendation. In: Proceedings of the Web Conference 2021. WWW \u201921, pp. 413\u2013424. Association for Computing Machinery, New York, USA (2021). https:\/\/doi.org\/10.1145\/3442381.3449844","DOI":"10.1145\/3442381.3449844"},{"key":"1306_CR27","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1016\/j.ins.2023.02.011","volume":"629","author":"J Ji","year":"2023","unstructured":"Ji, J., Zhang, B., Yu, J., Zhang, X., Qiu, D., Zhang, B.: Relationship-aware contrastive learning for social recommendations. Inf. Sci. 629, 778\u2013797 (2023). https:\/\/doi.org\/10.1016\/j.ins.2023.02.011","journal-title":"Inf. Sci."},{"key":"1306_CR28","doi-asserted-by":"publisher","unstructured":"Xiao, X., Wen, J., Zhou, W., Luo, F., Gao, M., Zeng, J.: Multi-interaction fusion collaborative filtering for social recommendation. Expert Syst. Appl. 205(C) (2022). https:\/\/doi.org\/10.1016\/j.eswa.2022.117610","DOI":"10.1016\/j.eswa.2022.117610"},{"key":"1306_CR29","doi-asserted-by":"publisher","unstructured":"Golbeck, J.: Generating predictive movie recommendations from trust in social networks. In: Proceedings of the 4th International Conference on Trust Management. iTrust\u201906, pp. 93\u2013104. Springer, Berlin, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11755593_8","DOI":"10.1007\/11755593_8"},{"key":"1306_CR30","doi-asserted-by":"publisher","unstructured":"Massa, P., Avesani, P.: Trust-aware recommender systems. In: Proceedings of the 2007 ACM Conference on Recommender Systems. RecSys \u201907, pp. 17\u201324. Association for Computing Machinery, New York, USA (2007). https:\/\/doi.org\/10.1145\/1297231.1297235","DOI":"10.1145\/1297231.1297235"},{"issue":"8","key":"1306_CR31","doi-asserted-by":"publisher","first-page":"1633","DOI":"10.1109\/TPAMI.2016.2605085","volume":"39","author":"B Yang","year":"2017","unstructured":"Yang, B., Lei, Y., Liu, J., Li, W.: Social collaborative filtering by trust. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1633\u20131647 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2605085","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1306_CR32","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. CoRR. abs\/1609.02907 (2016)"},{"key":"1306_CR33","doi-asserted-by":"publisher","first-page":"105981","DOI":"10.1016\/j.engappai.2023.105981","volume":"121","author":"L Cai","year":"2023","unstructured":"Cai, L., Lai, T., Wang, L., Zhou, Y., Xiong, Y.: Graph convolutional network combining node similarity association and layer attention for personalized recommendation. Eng. Appl. Artif. Intell. 121, 105981 (2023). https:\/\/doi.org\/10.1016\/j.engappai.2023.105981","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"21","key":"1306_CR34","doi-asserted-by":"publisher","first-page":"25511","DOI":"10.1007\/s10489-023-04860-6","volume":"53","author":"J Zhao","year":"2023","unstructured":"Zhao, J., Huang, K., Li, P.: Dual channel group-aware graph convolutional networks for collaborative filtering. Appl. Intell. 53(21), 25511\u201325524 (2023). https:\/\/doi.org\/10.1007\/s10489-023-04860-6","journal-title":"Appl. Intell."},{"key":"1306_CR35","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903. (2017)"},{"key":"1306_CR36","doi-asserted-by":"publisher","unstructured":"Dong, Y., Ding, K., Jalaian, B., Ji, S., Li, J.: Adagnn: Graph neural networks with adaptive frequency response filter. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. CIKM \u201921, pp. 392\u2013401. Association for Computing Machinery, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3459637.3482226","DOI":"10.1145\/3459637.3482226"},{"issue":"5","key":"1306_CR37","doi-asserted-by":"publisher","first-page":"4115","DOI":"10.1609\/aaai.v35i5.16533","volume":"35","author":"C Huang","year":"2021","unstructured":"Huang, C., Xu, H., Xu, Y., Dai, P., Xia, L., Lu, M., Bo, L., Xing, H., Lai, X., Ye, Y.: Knowledge-aware coupled graph neural network for social recommendation. Proceedings of the AAAI Conference on Artificial Intelligence. 35(5), 4115\u20134122 (2021). https:\/\/doi.org\/10.1609\/aaai.v35i5.16533","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"1306_CR38","doi-asserted-by":"publisher","unstructured":"Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., Yin, D.: Graph neural networks for social recommendation. In: The World Wide Web Conference. WWW \u201919, pp. 417\u2013426. Association for Computing Machinery, New York, NY, USA (2019). https:\/\/doi.org\/10.1145\/3308558.3313488","DOI":"10.1145\/3308558.3313488"},{"key":"1306_CR39","doi-asserted-by":"publisher","unstructured":"Bai, T., Zhang, Y., Wu, B., Nie, J.-Y.: Temporal graph neural networks for social recommendation. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 898\u2013903 (2020). https:\/\/doi.org\/10.1109\/BigData50022.2020.9378444","DOI":"10.1109\/BigData50022.2020.9378444"},{"key":"1306_CR40","doi-asserted-by":"publisher","unstructured":"Chen, J., Xin, X., Liang, X., He, X., Liu, J.: Gdsrec: Graph-based decentralized collaborative filtering for social recommendation. IEEE Transactions on Knowledge & amp; Data Engineering. 35(05), 4813\u20134824 (2023) https:\/\/doi.org\/10.1109\/TKDE.2022.3153284","DOI":"10.1109\/TKDE.2022.3153284"},{"key":"1306_CR41","doi-asserted-by":"publisher","unstructured":"Liu, Y., Chen, L., He, X., Peng, J., Zheng, Z., Tang, J.: Modelling high-order social relations for item recommendation (extended abstract). In: 2023 IEEE 39th International Conference on Data Engineering (ICDE), pp. 3821\u20133822. IEEE Computer Society, Los Alamitos, CA, USA (2023). https:\/\/doi.org\/10.1109\/ICDE55515.2023.00337","DOI":"10.1109\/ICDE55515.2023.00337"},{"key":"1306_CR42","doi-asserted-by":"publisher","unstructured":"Wu, Q., Zhang, H., Gao, X., He, P., Weng, P., Gao, H., Chen, G.: Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. In: The World Wide Web Conference. WWW \u201919, pp. 2091\u20132102. Association for Computing Machinery, New York, USA (2019). https:\/\/doi.org\/10.1145\/3308558.3313442","DOI":"10.1145\/3308558.3313442"},{"key":"1306_CR43","doi-asserted-by":"publisher","unstructured":"Wu, L., Li, J., Sun, P., Hong, R., Ge, Y., Wang, M.: Diffnet++: a neural influence and interest diffusion network for social recommendation. IEEE Trans. on Knowl. and Data Eng. 34(10), 4753\u20134766 (2022). https:\/\/doi.org\/10.1109\/TKDE.2020.3048414","DOI":"10.1109\/TKDE.2020.3048414"},{"key":"1306_CR44","doi-asserted-by":"publisher","unstructured":"Xu, F., Lian, J., Han, Z., Li, Y., Xu, Y., Xie, X.: Relation-aware graph convolutional networks for agent-initiated social e-commerce recommendation. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. CIKM \u201919, pp. 529\u2013538. Association for Computing Machinery, New York, NY, USA (2019). https:\/\/doi.org\/10.1145\/3357384.3357924","DOI":"10.1145\/3357384.3357924"},{"key":"1306_CR45","doi-asserted-by":"publisher","unstructured":"Zhang, J., Gao, C., Jin, D., Li, Y.: Group-buying recommendation for social e-commerce. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 1536\u20131547. IEEE Computer Society, Los Alamitos, CA, USA (2021). https:\/\/doi.org\/10.1109\/ICDE51399.2021.00136","DOI":"10.1109\/ICDE51399.2021.00136"},{"key":"1306_CR46","doi-asserted-by":"publisher","unstructured":"Miao, H., Li, A., Yang, B.: Meta-path enhanced lightweight graph neural network for &nbsp;social recommendation. In: Database Systems for Advanced Applications: 27th International Conference, DASFAA 2022, Virtual Event, April 11\u201314, 2022, Proceedings, Part II, pp. 134\u2013149. Springer, Berlin, Heidelberg (2022). https:\/\/doi.org\/10.1007\/978-3-031-00126-0_9","DOI":"10.1007\/978-3-031-00126-0_9"},{"key":"1306_CR47","unstructured":"Rusch, T.K., Bronstein, M.M., Mishra, S.: A survey on oversmoothing in graph neural networks. arXiv preprint arXiv:2303.10993. (2023)"},{"key":"1306_CR48","doi-asserted-by":"publisher","unstructured":"Zhao, Q., Dong, J.: Self-supervised representation learning by predicting visual permutations. Knowl.-Based Syst. 210, 106534 (2020). https:\/\/doi.org\/10.1016\/j.knosys.2020.106534","DOI":"10.1016\/j.knosys.2020.106534"},{"key":"1306_CR49","doi-asserted-by":"publisher","unstructured":"Aberdam, A., Litman, R., Tsiper, S., Anschel, O., Slossberg, R., Mazor, S., Manmatha, R., Perona, P.: Sequence-to-sequence contrastive learning for text recognition. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15297\u201315307 (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.01505","DOI":"10.1109\/CVPR46437.2021.01505"},{"key":"1306_CR50","doi-asserted-by":"publisher","unstructured":"Jing, M., Zhu, Y., Zang, T., Wang, K.: Contrastive self-supervised learning in recommender systems: a survey. ACM Trans. Inf. Syst. 42(2) (2023). https:\/\/doi.org\/10.1145\/3627158","DOI":"10.1145\/3627158"},{"issue":"21","key":"1306_CR51","doi-asserted-by":"publisher","first-page":"25836","DOI":"10.1007\/s10489-023-04787-y","volume":"53","author":"D Lin","year":"2023","unstructured":"Lin, D., Ding, X., Hu, D., Jiang, Y.: Community-aware graph contrastive learning for collaborative filtering. Appl. Intell. 53(21), 25836\u201325849 (2023). https:\/\/doi.org\/10.1007\/s10489-023-04787-y","journal-title":"Appl. Intell."},{"issue":"22","key":"1306_CR52","doi-asserted-by":"publisher","first-page":"27624","DOI":"10.1007\/s10489-023-04995-6","volume":"53","author":"Y Liang","year":"2023","unstructured":"Liang, Y., Wan, Y.: Learning on heterogeneous graph neural networks with consistency-based augmentation. Appl. Intell. 53(22), 27624\u201327636 (2023). https:\/\/doi.org\/10.1007\/s10489-023-04995-6","journal-title":"Appl. Intell."},{"key":"1306_CR53","doi-asserted-by":"publisher","unstructured":"Lee, D., Kang, S., Ju, H., Park, C., Yu, H.: Bootstrapping user and item representations for one-class collaborative filtering. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR \u201921, pp. 317\u2013326. Association for Computing Machinery, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3404835.3462935","DOI":"10.1145\/3404835.3462935"},{"key":"1306_CR54","doi-asserted-by":"publisher","unstructured":"Yang, Y., Wu, L., Hong, R., Zhang, K., Wang, M.: Enhanced graph learning for collaborative filtering via mutual information maximization. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR \u201921, pp. 71\u201380. Association for Computing Machinery, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3404835.3462928","DOI":"10.1145\/3404835.3462928"},{"key":"1306_CR55","doi-asserted-by":"publisher","unstructured":"Yang, H., Chen, H., Li, L., Yu, P.S., Xu, G.: Hyper meta-path contrastive learning for multi-behavior recommendation. In: 2021 IEEE International Conference on Data Mining (ICDM), pp. 787\u2013796. IEEE Computer Society, Los Alamitos, CA, USA (2021). https:\/\/doi.org\/10.1109\/ICDM51629.2021.00090","DOI":"10.1109\/ICDM51629.2021.00090"},{"key":"1306_CR56","unstructured":"Liu, Z., Ma, Y., Ouyang, Y., Xiong, Z.: Contrastive learning for recommender system. CoRR. abs\/2101.01317 (2021)"},{"key":"1306_CR57","doi-asserted-by":"publisher","unstructured":"Hassani, K., Khasahmadi, A.H.: Contrastive multi-view representation learning on graphs. In: Proceedings of the 37th International Conference on Machine Learning. ICML\u201920. JMLR.org, (2020). https:\/\/doi.org\/10.5555\/3524938.3525323","DOI":"10.5555\/3524938.3525323"},{"issue":"5","key":"1306_CR58","doi-asserted-by":"publisher","first-page":"4503","DOI":"10.1609\/aaai.v35i5.16578","volume":"35","author":"X Xia","year":"2021","unstructured":"Xia, X., Yin, H., Yu, J., Wang, Q., Cui, L., Zhang, X.: Self-supervised hypergraph convolutional networks for session-based recommendation. Proceedings of the AAAI Conference on Artificial Intelligence. 35(5), 4503\u20134511 (2021). https:\/\/doi.org\/10.1609\/aaai.v35i5.16578","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"1306_CR59","doi-asserted-by":"publisher","first-page":"6547","DOI":"10.1007\/s00500-022-07178-6","volume":"26","author":"M Gan","year":"2022","unstructured":"Gan, M., Ma, Y.: Knowledge transfer learning from multiple user activities to improve personalized recommendation. Soft. Comput. 26, 6547\u20136566 (2022). https:\/\/doi.org\/10.1007\/s00500-022-07178-6","journal-title":"Soft. Comput."},{"key":"1306_CR60","doi-asserted-by":"publisher","unstructured":"Gan, M., Kwon, O.-C.: A knowledge-enhanced contextual bandit approach for personalized recommendation in dynamic domains. Knowl.-Based Syst. 251, 109158 (2022). https:\/\/doi.org\/10.1016\/j.knosys.2022.109158","DOI":"10.1016\/j.knosys.2022.109158"},{"key":"1306_CR61","doi-asserted-by":"publisher","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. WWW \u201921, pp. 2069\u20132080. Association for Computing Machinery, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3442381.3449802","DOI":"10.1145\/3442381.3449802"},{"issue":"5594","key":"1306_CR62","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1126\/science.298.5594.824","volume":"298","author":"R Milo","year":"2002","unstructured":"Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298(5594), 824\u2013827 (2002). https:\/\/doi.org\/10.1126\/science.298.5594.824","journal-title":"Science"},{"key":"1306_CR63","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1016\/j.future.2017.02.027","volume":"78","author":"M Gan","year":"2018","unstructured":"Gan, M., Jiang, R.: Flower: fusing global and local associations towards personalized social recommendation. Futur. Gener. Comput. Syst. 78, 462\u2013473 (2018). https:\/\/doi.org\/10.1016\/j.future.2017.02.027","journal-title":"Futur. Gener. Comput. Syst."},{"key":"1306_CR64","unstructured":"Gutmann, M.U., Hyv\u00e4rinen, A.: Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics. J. Mach. Learn. Res. 13(null), 307\u2013361 (2012)"},{"key":"1306_CR65","doi-asserted-by":"publisher","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. SIGIR \u201920, pp. 639\u2013648. Association for Computing Machinery, New York, USA (2020). https:\/\/doi.org\/10.1145\/3397271.3401063","DOI":"10.1145\/3397271.3401063"},{"issue":"8","key":"1306_CR66","doi-asserted-by":"publisher","first-page":"3727","DOI":"10.1109\/TKDE.2020.3033673","volume":"34","author":"J Yu","year":"2022","unstructured":"Yu, J., Yin, H., Li, J., Gao, M., Huang, Z., Cui, L.: Enhancing social recommendation with adversarial graph convolutional networks. IEEE Trans. Knowl. Data Eng. 34(8), 3727\u20133739 (2022). https:\/\/doi.org\/10.1109\/TKDE.2020.3033673","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1306_CR67","doi-asserted-by":"publisher","unstructured":"Yu, J., Yin, H., Xia, X., Chen, T., Cui, L., Nguyen, Q.V.H.: Are graph augmentations necessary? simple graph contrastive &nbsp;learning for recommendation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR \u201922, pp. 1294\u20131303. Association for Computing Machinery, New York, NY, USA (2022). https:\/\/doi.org\/10.1145\/3477495.3531937","DOI":"10.1145\/3477495.3531937"},{"key":"1306_CR68","doi-asserted-by":"publisher","unstructured":"He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web. WWW \u201917, pp. 173\u2013182. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE (2017). https:\/\/doi.org\/10.1145\/3038912.3052569","DOI":"10.1145\/3038912.3052569"},{"key":"1306_CR69","doi-asserted-by":"publisher","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. SIGIR\u201919, pp. 165\u2013174. Association for Computing Machinery, New York, NY, USA (2019). https:\/\/doi.org\/10.1145\/3331184.3331267","DOI":"10.1145\/3331184.3331267"},{"key":"1306_CR70","unstructured":"Zhu, Z., Gao, C., Chen, X., Li, N., Jin, D., Li, Y.: Inhomogeneous social recommendation with hypergraph convolutional networks. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE) (2022)"},{"key":"1306_CR71","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1016\/j.ins.2023.02.011","volume":"629","author":"J Ji","year":"2023","unstructured":"Ji, J., Zhang, B., Yu, J., Zhang, X., Qiu, D., Zhang, B.: Relationship-aware contrastive learning for social recommendations. Inf. Sci. 629, 778\u2013797 (2023). https:\/\/doi.org\/10.1016\/j.ins.2023.02.011","journal-title":"Inf. Sci."},{"key":"1306_CR72","doi-asserted-by":"publisher","unstructured":"Wang, C., Li, L., Zhang, H., Li, D.: Quaternion-based knowledge graph neural network for social recommendation. Knowl.-Based Syst. 257, 109940 (2022). https:\/\/doi.org\/10.1016\/j.knosys.2022.109940","DOI":"10.1016\/j.knosys.2022.109940"},{"issue":"8","key":"1306_CR73","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798\u20131828 (2013). https:\/\/doi.org\/10.1109\/TPAMI.2013.50","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"1306_CR74","doi-asserted-by":"publisher","first-page":"103169","DOI":"10.1016\/j.ipm.2022.103169","volume":"60","author":"M Gan","year":"2023","unstructured":"Gan, M., Ma, Y.: Mapping user interest into hyper-spherical space: a novel poi recommendation method. Inform. Process. Manag. 60(2), 103169 (2023). https:\/\/doi.org\/10.1016\/j.ipm.2022.103169","journal-title":"Inform. Process. Manag."},{"key":"1306_CR75","first-page":"207","volume":"10","author":"KQ Weinberger","year":"2009","unstructured":"Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10, 207\u2013244 (2009)","journal-title":"J. Mach. Learn. Res."},{"key":"1306_CR76","doi-asserted-by":"publisher","unstructured":"din, A.M., Qureshi, S.: Limits of depth: over-smoothing and over-squashing in gnns. Big Data Mining and Analytics. 7(1), 205\u2013216 (2024). https:\/\/doi.org\/10.26599\/BDMA.2023.9020019","DOI":"10.26599\/BDMA.2023.9020019"}],"container-title":["World Wide Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-024-01306-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11280-024-01306-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-024-01306-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T03:50:46Z","timestamp":1732074646000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11280-024-01306-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,2]]},"references-count":76,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["1306"],"URL":"https:\/\/doi.org\/10.1007\/s11280-024-01306-y","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-3889697\/v1","asserted-by":"object"}]},"ISSN":["1386-145X","1573-1413"],"issn-type":[{"value":"1386-145X","type":"print"},{"value":"1573-1413","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,2]]},"assertion":[{"value":"23 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 September 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 October 2024","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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"71"}}