{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T15:59:51Z","timestamp":1774367991552,"version":"3.50.1"},"reference-count":59,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2023,7,8]],"date-time":"2023-07-08T00:00:00Z","timestamp":1688774400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100012456","name":"national social science fund of china","doi-asserted-by":"publisher","award":["21ATQ006"],"award-info":[{"award-number":["21ATQ006"]}],"id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"fundamental research funds for the central universities","doi-asserted-by":"publisher","award":["CCNU22QN017"],"award-info":[{"award-number":["CCNU22QN017"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Information Science"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:p>Cross-domain recommendation models are proposed to enrich the knowledge in the target domain by taking advantage of the data in the auxiliary domain to mitigate sparsity and cold-start user problems. However, most of the existing cross-domain recommendation models are dependent on rating information of items, ignoring high-order information contained in the graph data structure. In this study, we develop a novel cross-domain recommendation model by unified modelling high-order information and rating information to tackle the research gaps. Different from previous research work, we apply heterogeneous graph neural network to extract high-order information among users, items and features; obtain high-order information embeddings of users and items; and then use neural network to extract rating information and obtain user rating information embeddings by a non-linear mapping function MLP (Multilayer Perceptron). Moreover, high-order information embeddings and rating information embeddings are fused in a unified way to complete the final rating prediction, and the gradient descent method is adopted to learn the parameters of the model based on the loss function. Experiments conducted on two real-world data sets including 3,032,642 ratings from two experimental scenarios demonstrate that our model can effectively alleviate the problems of sparsity and cold-start users simultaneously, and significantly outperforms the baseline models using a variety of recommendation accuracy metrics.<\/jats:p>","DOI":"10.1177\/01655515231182068","type":"journal-article","created":{"date-parts":[[2023,7,8]],"date-time":"2023-07-08T08:11:15Z","timestamp":1688803875000},"page":"124-146","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["A cross-domain recommendation model by unified modelling high-order information and rating information"],"prefix":"10.1177","volume":"52","author":[{"given":"Ming","family":"Yi","sequence":"first","affiliation":[{"name":"School of Information Management, Central China Normal University, China"}]},{"given":"Ming","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Management, Central China Normal University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9753-0328","authenticated-orcid":false,"given":"Cuicui","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Information Management, Central China Normal University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6641-9355","authenticated-orcid":false,"given":"Weihua","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Public Administration, Huazhong Agricultural University, China"}]}],"member":"179","published-online":{"date-parts":[[2023,7,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2013.04.012"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2013.03.012"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2016.04.004"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2013.01.003"},{"issue":"1","key":"e_1_3_2_7_2","first-page":"1","article-title":"Hotel recommendation system by bipartite networks and link prediction","volume":"79","author":"Kaya B.","year":"2020","unstructured":"Kaya B. Hotel recommendation system by bipartite networks and link prediction. J Inf Sci 2020; 79(1): 1\u201314.","journal-title":"J Inf Sci"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102666"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2013.05.025"},{"issue":"5","key":"e_1_3_2_10_2","first-page":"1","article-title":"A hybrid recommender system for the mining of consumer preferences from their reviews","volume":"46","author":"Cheng LC","year":"2019","unstructured":"Cheng LC, Lin MC. A hybrid recommender system for the mining of consumer preferences from their reviews. J Inf Sci 2019; 46(5): 1\u201319.","journal-title":"J Inf Sci"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/2009916.2009961"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11257-018-9217-6"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/2976737"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3073565"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40991-2_11"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/587"},{"key":"e_1_3_2_17_2","volume-title":"RecSys\u201916 proceedings of the 10th ACM conference on recommender systems","author":"Kazama M","unstructured":"Kazama M, Varga I. Cross domain recommendation using vector space transfer learning. In: RecSys\u201916 proceedings of the 10th ACM conference on recommender systems, Boston, MA, 17 September 2016, pp. 1601397. New York: ACM."},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/343"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/516"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3231601"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-019-01396-5"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.21236\/ADA486804"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020423"},{"key":"e_1_3_2_24_2","doi-asserted-by":"crossref","unstructured":"Hu L Cao J Xu GD et al. Personalized recommendation via cross-domain triadic factorization. In: WWW\u201913 proceedings of the 22nd international conference on world wide web Rio de Janeiro Brazil 13\u201317 May 2013 pp. 595\u2013606. New York: ACM.","DOI":"10.1145\/2488388.2488441"},{"issue":"5","key":"e_1_3_2_25_2","first-page":"1054","article-title":"Rating knowledge sharing in cross-domain collaborative filtering","volume":"45","author":"Li B","year":"2015","unstructured":"Li B, Zhu X, Li R, et al. Rating knowledge sharing in cross-domain collaborative filtering. IEEE Trans. Cybern 2015; 45(5): 1054\u20131068.","journal-title":"IEEE Trans. Cybern"},{"key":"e_1_3_2_26_2","first-page":"2052","volume-title":"IJCAI\u201909 proceedings of the 21st international joint conference on artificial intelligence","author":"Li B","unstructured":"Li B, Yang Q, Xue XY. Can movies and books collaborate? Cross-domain collaborative filtering for sparsity reduction. In: IJCAI\u201909 proceedings of the 21st international joint conference on artificial intelligence, Pasadena, CA, 11\u201316 July 2009, pp. 2052\u20132057. San Francisco, CA: Morgan Kaufmann."},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.09.042"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2896881"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271684"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.07.091"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371793"},{"key":"e_1_3_2_32_2","doi-asserted-by":"crossref","unstructured":"Gao C Zhao K Chen XN et al. Cross-domain recommendation without sharing user-relevant data. In: WWW\u201919 proceedings of the 28th international conference on world wide web San Francisco CA 13\u201317 May 2019 pp. 491\u2013502. New York: ACM.","DOI":"10.1145\/3308558.3313538"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_35_2","doi-asserted-by":"crossref","unstructured":"Tang J Qu M Wang MZ et al. LINE: large-scale information network embedding. In: WWW\u201915 proceedings of the 24th international conference on world wide web Florence Italy 18\u201322 May 2015 pp. 1067\u20131077. New York: ACM.","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-01587-8"},{"key":"e_1_3_2_38_2","first-page":"1","volume-title":"ICLR\u201917 proceedings of the 5th international conference on learning representations","author":"Kipf TN","unstructured":"Kipf TN, Welling M. Semi-supervised classification with graph convolutional network. In: ICLR\u201917 proceedings of the 5th international conference on learning representations, Toulon, France, 24\u201326 April 2017, pp. 1\u201314."},{"key":"e_1_3_2_39_2","first-page":"1","volume-title":"ICLR\u201919 proceedings of the 7th international conference on learning representations","author":"Xu BB","unstructured":"Xu BB, Shen HW, Cao Q, et al. Graph wavelet neural networ. In: ICLR\u201919 proceedings of the 7th international conference on learning representations, New Orleans, LA, 6\u20139 May 2019, pp. 1\u201313."},{"key":"e_1_3_2_40_2","first-page":"1025","volume-title":"NIPS\u201917 proceedings of the 31st annual conference on neural information processing systems","author":"Hamilton WL","unstructured":"Hamilton WL, Ying R, Leskovec J. Inductive representation learning on large graphs. In: NIPS\u201917 proceedings of the 31st annual conference on neural information processing systems, Long Beach, CA, 4\u20139 December 2017, pp. 1025\u20131035. Cambridge, MA: MIT Press."},{"key":"e_1_3_2_41_2","first-page":"1","volume-title":"ICLR\u201918 proceedings of the 6th international conference on learning representations","author":"Velickovic P","unstructured":"Velickovic P, Cucurull G, Casanova A, et al. Graph attention networks. In: ICLR\u201918 proceedings of the 6th international conference on learning representations, Vancouver, BC, Canada, 30 April - 3 May 2018, pp. 1\u201312."},{"key":"e_1_3_2_42_2","doi-asserted-by":"crossref","unstructured":"Zhang YZ Xiong Y Kong XN et al. Deep collective classification in heterogeneous information networks. In: WWW\u201918 proceedings of the 27th international conference on world wide web Lyon France 23\u201327 April 2018 pp. 399\u2013408. New York: ACM.","DOI":"10.1145\/3178876.3186106"},{"key":"e_1_3_2_43_2","doi-asserted-by":"crossref","unstructured":"Wang X Ji HY Cui P et al. Heterogeneous graph attention network. In: WWW\u201919 proceedings of the 28th international conference on world wide web San Francisco CA 13\u201317 May 2019 pp. 2022\u20132032. New York: ACM.","DOI":"10.1145\/3308558.3313562"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330961"},{"key":"e_1_3_2_45_2","doi-asserted-by":"crossref","unstructured":"Fu XY Zhang JN Meng ZQ et al. Magnn: metapath aggregated graph neural network for heterogeneous graph embedding. In: WWW\u201920 proceedings of the 29th international conference on world wide web Taipei Taiwan China 20\u201324 April 2020 pp. 2331\u20132341. New York: ACM.","DOI":"10.1145\/3366423.3380297"},{"issue":"1","key":"e_1_3_2_46_2","first-page":"62","article-title":"Using unsupervised graphs of neural networks for constructing learning representations of academic papers","volume":"41","author":"Din g H","year":"2022","unstructured":"Din g H, Ren W, Cao G. Using unsupervised graphs of neural networks for constructing learning representations of academic papers. J China Soc Sci Tech Inf 2022; 41(1): 62\u201372.","journal-title":"J China Soc Sci Tech Inf"},{"issue":"11","key":"e_1_3_2_47_2","first-page":"1209","article-title":"Detection of scientific knowledge structure based on graph representation learning","volume":"40","author":"Liu F","year":"2021","unstructured":"Liu F, Zhang S, Luo S, et al. Detection of scientific knowledge structure based on graph representation learning. J China Soc Sci Tech Inf 2021; 40(11): 1209\u20131220.","journal-title":"J China Soc Sci Tech Inf"},{"issue":"12","key":"e_1_3_2_48_2","first-page":"3617","article-title":"Item recommendation algorithm based on GNN and deep learning","volume":"38","author":"Feng X","year":"2021","unstructured":"Feng X, Sheng XY. Item recommendation algorithm based on GNN and deep learning. Appl Res Comp 2021; 38(12): 3617\u20133622.","journal-title":"Appl Res Comp"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412012"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_51_2","author":"Berg R","year":"1706","unstructured":"Berg R, Kipf TN, Welling M. Graph convolutional matrix completion. arXiv: 1706.02263[stat.ML], 2017.","journal-title":"Graph convolutional matrix completion"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2598561"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102384"},{"key":"e_1_3_2_54_2","first-page":"1257","article-title":"Probabilistic matrix factorization","volume":"20","author":"Mnih A","year":"2007","unstructured":"Mnih A, Salakhutdinov RR. Probabilistic matrix factorization. Adv Neural Inf Process Syst 2007; 20: 1257\u20131264.","journal-title":"Adv Neural Inf Process Syst"},{"issue":"11","key":"e_1_3_2_55_2","first-page":"94","article-title":"Research on the intelligent recommendation method of digital resources of library collection based on probability matrix factorization","volume":"37","author":"Wu X.","year":"2014","unstructured":"Wu X. Research on the intelligent recommendation method of digital resources of library collection based on probability matrix factorization. Inf Stud Theory Appl 2014; 37(11): 94\u201397.","journal-title":"Inf Stud Theory Appl"},{"key":"e_1_3_2_56_2","doi-asserted-by":"crossref","unstructured":"He XN Liao LZ Zhang HW et al. Neural collaborative filtering. In: WWW\u201917 proceedings of the 26th international conference on world wide web Perth WA Australia 3\u20137 April 2017 pp. 173\u2013182. New York: ACM.","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE55515.2023.00167"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1177\/0165551518808191"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102531"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109160"}],"container-title":["Journal of Information Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/01655515231182068","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/01655515231182068","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/01655515231182068","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T05:49:47Z","timestamp":1769579387000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/01655515231182068"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,8]]},"references-count":59,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["10.1177\/01655515231182068"],"URL":"https:\/\/doi.org\/10.1177\/01655515231182068","relation":{},"ISSN":["0165-5515","1741-6485"],"issn-type":[{"value":"0165-5515","type":"print"},{"value":"1741-6485","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,8]]}}}