{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:38:37Z","timestamp":1778258317489,"version":"3.51.4"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T00:00:00Z","timestamp":1657238400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T00:00:00Z","timestamp":1657238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Tianjin \u201dProject + Team\u201d Key Training Project","award":["Grant No. XC202022"],"award-info":[{"award-number":["Grant No. XC202022"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s10489-022-03748-1","type":"journal-article","created":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T04:02:39Z","timestamp":1657252959000},"page":"6432-6447","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["SIGA: social influence modeling integrating graph autoencoder for rating prediction"],"prefix":"10.1007","volume":"53","author":[{"given":"Jinxin","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5711-8638","authenticated-orcid":false,"given":"Yingyuan","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenguang","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ching-Hsien","family":"Hsu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,8]]},"reference":[{"key":"3748_CR1","unstructured":"Basu C, Hirsh H, Cohen W et al (1998) Recommendation as classification: Using social and content-based information in recommendation. In: Aaai\/iaai, pp 714\u2013720"},{"key":"3748_CR2","unstructured":"Bengio Y, Yao L, Alain G et al (2013) Generalized denoising auto-encoders as generative models. arXiv:13056663"},{"key":"3748_CR3","unstructured":"Berg R, Kipf TN, Welling M (2017) Graph convolutional matrix completion. arXiv:170602263"},{"key":"3748_CR4","doi-asserted-by":"crossref","unstructured":"Cao D, He X, Miao L et al (2018) Attentive group recommendation. In: The 41st international ACM SIGIR conference on research & development in information retrieval, pp 645\u2013654","DOI":"10.1145\/3209978.3209998"},{"issue":"3","key":"3748_CR5","doi-asserted-by":"publisher","first-page":"2082","DOI":"10.1016\/j.eswa.2007.02.008","volume":"34","author":"YL Chen","year":"2008","unstructured":"Chen Y L, Cheng L C, Chuang C N (2008) A group recommendation system with consideration of interactions among group members. Expert Syst Appl 34(3):2082\u20132090","journal-title":"Expert Syst Appl"},{"key":"3748_CR6","doi-asserted-by":"crossref","unstructured":"Fan W, Ma Y, Li Q et al (2019) Graph neural networks for social recommendation. In: The world wide web conference, pp 417\u2013426","DOI":"10.1145\/3308558.3313488"},{"key":"3748_CR7","doi-asserted-by":"crossref","unstructured":"Golbeck J, Hendler J et al (2006) Filmtrust: movie recommendations using trust in web-based social networks. In: Proceedings of the IEEE consumer communications and networking conference. Citeseer, pp 282\u2013286","DOI":"10.1109\/CCNC.2006.1593032"},{"key":"3748_CR8","doi-asserted-by":"crossref","unstructured":"Guo G, Zhang J, Yorke-Smith N (2015) Trustsvd: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In: Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v29i1.9153"},{"key":"3748_CR9","unstructured":"Hartford J, Graham D, Leyton-Brown K, et al (2018) Deep models of interactions across sets. In: International conference on machine learning. PMLR, pp 1909\u20131918"},{"key":"3748_CR10","doi-asserted-by":"crossref","unstructured":"He X, Liao L, Zhang H et al (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, pp 173\u2013182","DOI":"10.1145\/3038912.3052569"},{"key":"3748_CR11","doi-asserted-by":"crossref","unstructured":"He X, Deng K, Wang X et al (2020) 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","DOI":"10.1145\/3397271.3401063"},{"key":"3748_CR12","doi-asserted-by":"crossref","unstructured":"Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the fourth ACM conference on recommender systems, pp 135\u2013142","DOI":"10.1145\/1864708.1864736"},{"key":"3748_CR13","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv:160902907"},{"key":"3748_CR14","unstructured":"Kipf TN, Welling M (2016) Variational graph auto-encoders. arXiv:161107308"},{"key":"3748_CR15","unstructured":"Lin F, Cohen W (2010) Advances in social networks analysis and mining (asonam). In: 2010 International conference on, pp 192\u2013199"},{"key":"3748_CR16","doi-asserted-by":"crossref","unstructured":"Ling Z, Xiao Y, Wang H, et al (2019) Extracting implicit friends from heterogeneous information network for social recommendation. In: Pacific rim international conference on artificial intelligence. Springer, pp 607\u2013620","DOI":"10.1007\/978-3-030-29894-4_49"},{"key":"3748_CR17","doi-asserted-by":"crossref","unstructured":"Liu C Y, Zhou C, Wu J et al (2018) Social recommendation with an essential preference space. In: Thirty-second AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11245"},{"key":"3748_CR18","doi-asserted-by":"crossref","unstructured":"Liu F, Cheng Z, Zhu L, et al. (2021) Interest-aware message-passing gcn for recommendation. In: Proceedings of the web conference 2021, pp 1296-1305","DOI":"10.1145\/3442381.3449986"},{"key":"3748_CR19","unstructured":"Liu Y , Liang C, He X et al (2020) Modelling high-order social relations for item recommendation. IEEE Trans Knowl Data"},{"issue":"6","key":"3748_CR20","doi-asserted-by":"publisher","first-page":"e21","DOI":"10.1371\/journal.pone.0021202","volume":"6","author":"L L\u00fc","year":"2011","unstructured":"L\u00fc L, Zhang Y C, Yeung C H et al (2011) Leaders in social networks, the delicious case. PloS one 6(6):e21\u2013202","journal-title":"PloS one"},{"key":"3748_CR21","doi-asserted-by":"crossref","unstructured":"Ma C, Ma L, Zhang Y et al (2020) Memory augmented graph neural networks for sequential recommendation. In: Proceedings of the AAAI conference on artificial intelligence, pp 5045\u20135052","DOI":"10.1609\/aaai.v34i04.5945"},{"key":"3748_CR22","doi-asserted-by":"crossref","unstructured":"Ma H, Yang H, Lyu MR et al (2008) Sorec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM conference on information and knowledge management, pp 931\u2013940","DOI":"10.1145\/1458082.1458205"},{"key":"3748_CR23","unstructured":"Ma J, Zhou C, Cui P et al (2019) Learning disentangled representations for recommendation. arXiv:191014238"},{"key":"3748_CR24","doi-asserted-by":"crossref","unstructured":"Massam P, Avesani P (2007) Trust-aware recommender systems. In: Proceedings of the 2007 ACM conference on recommender systems, pp 17\u201324","DOI":"10.1145\/1297231.1297235"},{"key":"3748_CR25","unstructured":"Monti F, Bronstein MM, Bresson X (2017) Geometric matrix completion with recurrent multi-graph neural networks. arXiv:170406803"},{"key":"3748_CR26","doi-asserted-by":"crossref","unstructured":"Mu N, Zha D, He Y et al (2019) Graph attention networks for neural social recommendation. In: 2019 IEEE 31st international conference on tools with artificial intelligence ICTAI. IEEE, pp 1320\u20131327","DOI":"10.1109\/ICTAI.2019.00183"},{"issue":"2","key":"3748_CR27","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1002\/env.3170050203","volume":"5","author":"P Paatero","year":"1994","unstructured":"Paatero P, Tapper U (1994) Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5(2):111\u2013126","journal-title":"Environmetrics"},{"key":"3748_CR28","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":"3748_CR29","doi-asserted-by":"crossref","unstructured":"Sedhain S, Menon AK, Sanner S et al (2015) Autorec: autoencoders meet collaborative filtering. In: Proceedings of the 24th international conference on world wide web, pp 111\u2013112","DOI":"10.1145\/2740908.2742726"},{"key":"3748_CR30","doi-asserted-by":"crossref","unstructured":"Shenbin I, Alekseev A, Tutubalina E et al (2020) Recvae: A new variational autoencoder for top-n recommendations with implicit feedback. In: Proceedings of the 13th international conference on web search and data mining, pp 528\u2013536","DOI":"10.1145\/3336191.3371831"},{"key":"3748_CR31","doi-asserted-by":"crossref","unstructured":"Wu J, Wang X, Feng F et al (2021) 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","DOI":"10.1145\/3404835.3462862"},{"key":"3748_CR32","doi-asserted-by":"crossref","unstructured":"Wu L, Sun P, Fu Y et al (2019a) A neural influence diffusion model for social recommendation. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, pp 235\u2013244","DOI":"10.1145\/3331184.3331214"},{"key":"3748_CR33","doi-asserted-by":"crossref","unstructured":"Wu Q, Zhang H, Gao X et al (2019b) Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. In: The world wide web conference, pp 2091\u20132102","DOI":"10.1145\/3308558.3313442"},{"key":"3748_CR34","doi-asserted-by":"crossref","unstructured":"Yan S, Xiong Y, Lin D (2018) Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"3748_CR35","doi-asserted-by":"crossref","unstructured":"Ying R, He R, Chen K et al (2018) Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD International conference on knowledge discovery & data mining, pp 974\u2013983","DOI":"10.1145\/3219819.3219890"},{"key":"3748_CR36","doi-asserted-by":"crossref","unstructured":"Yu J, Yin H, Li J, et al. (2021) Self-supervised multi-channel hypergraph convolutional network for social recommendation. In: Proceedings of the web conference, pp 413\u2013424","DOI":"10.1145\/3442381.3449844"},{"key":"3748_CR37","doi-asserted-by":"crossref","unstructured":"Zhang C, Song D, Huang C et al (2019) Heterogeneous graph neural network. In: Proceedings of the 25th ACM SIGKDD International conference on knowledge discovery & data mining, pp 793\u2013803","DOI":"10.1145\/3292500.3330961"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03748-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03748-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03748-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T04:29:08Z","timestamp":1677472148000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03748-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,8]]},"references-count":37,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["3748"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03748-1","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,8]]},"assertion":[{"value":"7 May 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}