{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T20:18:52Z","timestamp":1771273132108,"version":"3.50.1"},"reference-count":75,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2020,10,30]],"date-time":"2020-10-30T00:00:00Z","timestamp":1604016000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,10,30]],"date-time":"2020-10-30T00:00:00Z","timestamp":1604016000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1007\/s11042-020-09949-5","type":"journal-article","created":{"date-parts":[[2020,10,30]],"date-time":"2020-10-30T14:02:45Z","timestamp":1604066565000},"page":"7805-7832","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":85,"title":["Presentation of a recommender system with ensemble learning and graph embedding: a case on MovieLens"],"prefix":"10.1007","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5952-156X","authenticated-orcid":false,"given":"Saman","family":"Forouzandeh","sequence":"first","affiliation":[]},{"given":"Kamal","family":"Berahmand","sequence":"additional","affiliation":[]},{"given":"Mehrdad","family":"Rostami","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,30]]},"reference":[{"key":"9949_CR1","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.is.2018.11.008","volume":"81","author":"J Bai","year":"2019","unstructured":"Bai J, Li L, Zeng D (2019) HiWalk: learning node embeddings from heterogeneous networks. Inf Syst 81:82\u201391","journal-title":"Inf Syst"},{"key":"9949_CR2","doi-asserted-by":"crossref","unstructured":"Barbin JP, Yousefi S, Masoumi B (2020) Efficient service recommendation using ensemble learning in the internet of things (IoT). J Ambient Intell Humaniz Comput 11(3):1339\u20131350","DOI":"10.1007\/s12652-019-01451-7"},{"key":"9949_CR3","doi-asserted-by":"crossref","unstructured":"Barkan O, Koenigstein N (2016) Item2vec: neural item embedding for collaborative filtering. In: 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, Vietri sul Mare, pp 1\u20136","DOI":"10.1109\/MLSP.2016.7738886"},{"key":"9949_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.is.2019.07.001","volume":"86","author":"P Basile","year":"2019","unstructured":"Basile P, Greco C, Suglia A, Semeraro G (2019) Bridging the gap between linked open data-based recommender systems and distributed representations. Inf Syst 86:1\u20138","journal-title":"Inf Syst"},{"key":"9949_CR5","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.procs.2018.01.092","volume":"127","author":"N Ben-Lhachemi","year":"2018","unstructured":"Ben-Lhachemi N (2018) Using tweets embeddings for hashtag recommendation in twitter. Procedia Comput Sci 127:7\u201315","journal-title":"Procedia Comput Sci"},{"key":"9949_CR6","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.chaos.2018.03.014","volume":"110","author":"K Berahmand","year":"2018","unstructured":"Berahmand K, Bouyer A, Samadi N (2018) A new centrality measure based on the negative and positive effects of clustering coefficient for identifying influential spreaders in complex networks. Chaos, Solitons Fractals 110:41\u201354","journal-title":"Chaos, Solitons Fractals"},{"issue":"11","key":"9949_CR7","doi-asserted-by":"publisher","first-page":"1711","DOI":"10.1007\/s00607-018-0684-8","volume":"101","author":"K Berahmand","year":"2019","unstructured":"Berahmand K, Bouyer A, Samadi N (2019) A new local and multidimensional ranking measure to detect spreaders in social networks. Computing 101(11):1711\u20131733","journal-title":"Computing"},{"key":"9949_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cosrev.2018.01.003","volume":"28","author":"T Boongoen","year":"2018","unstructured":"Boongoen T, Iam-On N (2018) Cluster ensembles: a survey of approaches with recent extensions and applications. Comput Sci Rev 28:1\u201325","journal-title":"Comput Sci Rev"},{"issue":"16","key":"9949_CR9","doi-asserted-by":"publisher","first-page":"7370","DOI":"10.1016\/j.eswa.2014.06.007","volume":"41","author":"J Borr\u00e0s","year":"2014","unstructured":"Borr\u00e0s J, Moreno A, Valls A (2014) Intelligent tourism recommender systems: a survey. Expert Syst Appl 41(16):7370\u20137389","journal-title":"Expert Syst Appl"},{"issue":"9","key":"9949_CR10","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1109\/TKDE.2018.2807452","volume":"30","author":"H Cai","year":"2018","unstructured":"Cai H, Zheng VW, Chang KC-C (2018) A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans Knowl Data Eng 30(9):1616\u20131637","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9949_CR11","doi-asserted-by":"crossref","unstructured":"Cao S, Lu W, Xu Q (2015) GraRep: learning graph representations with global structural information. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp 891\u2013900","DOI":"10.1145\/2806416.2806512"},{"key":"9949_CR12","doi-asserted-by":"crossref","unstructured":"Chang S, Han W, Tang J, Qi GJ, Aggarwal CC, Huang TS (2015) Heterogeneous network embedding via deep architectures. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 119\u2013128","DOI":"10.1145\/2783258.2783296"},{"key":"9949_CR13","doi-asserted-by":"crossref","unstructured":"da Costa Fortes A, Manzato MG (2014) Ensemble learning in recommender systems: Combining multiple user interactions for ranking personalization. In: Proceedings of the 20th Brazilian Symposium on Multimedia and the Web, pp 47\u201354","DOI":"10.1145\/2664551.2664556"},{"key":"9949_CR14","doi-asserted-by":"crossref","unstructured":"Dietterich TG (2000) Ensemble methods in machine learning. In: International Workshop on Multiple Classifier Systems. Springer, Berlin, Heidelberg, pp 1\u201315","DOI":"10.1007\/3-540-45014-9_1"},{"key":"9949_CR15","doi-asserted-by":"crossref","unstructured":"Forouzandeh S, Aghdam AR (2019) Health recommender system in social networks: a case of facebook. Webology 16(1):1\u201316","DOI":"10.14704\/WEB\/V16I1\/a178"},{"issue":"4","key":"9949_CR16","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/MCSE.2018.2875321","volume":"22","author":"S Forouzandeh","year":"2020","unstructured":"Forouzandeh S, Aghdam AR, Forouzandeh S, Xu S (2020) Addressing the cold-start problem using data mining techniques and improving recommender systems by cuckoo algorithm: a case study of Facebook. Comput Sci Eng 22(4):62\u201373","journal-title":"Comput Sci Eng"},{"key":"9949_CR17","unstructured":"Forouzandeh S, Soltanpanah H, Sheikhahmadi A (2014) Content marketing through data mining on Facebook social network. Webology 11(1):1\u201311"},{"issue":"1","key":"9949_CR18","first-page":"1","volume":"124","author":"S Forouzandeh","year":"2015","unstructured":"Forouzandeh S, Soltanpanah H, Sheikhahmadi A (2015) Application of data mining in designing a recommender system on social networks. Int J Comput Appl 124(1):1\u20137","journal-title":"Int J Comput Appl"},{"issue":"8","key":"9949_CR19","first-page":"46","volume":"17","author":"S Forouzandeh","year":"2017","unstructured":"Forouzandeh S et al (2017) Recommender system for users of internet of things (IOT). IJCSNS 17(8):46","journal-title":"IJCSNS"},{"key":"9949_CR20","doi-asserted-by":"crossref","unstructured":"Forouzandeh S, Sheikhahmadi A, Aghdam AR, Xu S (2018) New centrality measure for nodes based on user social status and behavior on Facebook. Int J Web Inf Syst 14(2):158\u2013176","DOI":"10.1108\/IJWIS-07-2017-0053"},{"key":"9949_CR21","doi-asserted-by":"publisher","first-page":"121269","DOI":"10.1016\/j.physa.2019.121269","volume":"527","author":"E Golzardi","year":"2019","unstructured":"Golzardi E, Sheikhahmadi A, Abdollahpouri A (2019) Detection of trust links on social networks using dynamic features. Physica A 527:121269","journal-title":"Physica A"},{"key":"9949_CR22","doi-asserted-by":"crossref","unstructured":"Grbovic M, Radosavljevic V, Djuric N, Bhamidipati N, Savla J, Bhagwan V, Sharp D (2015) E-commerce in your inbox: Product recommendations at scale. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1809\u20131818","DOI":"10.1145\/2783258.2788627"},{"key":"9949_CR23","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"key":"9949_CR24","doi-asserted-by":"crossref","unstructured":"Gu\u00e0rdia-Sebaoun E, Guigue V, Gallinari P (2015) Latent trajectory modeling: a light and efficient way to introduce time in recommender systems. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp 281\u2013284","DOI":"10.1145\/2792838.2799676"},{"key":"9949_CR25","unstructured":"Hamilton WL, Ying R, Leskovec J (2017) Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584"},{"issue":"1","key":"9949_CR26","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1109\/34.273716","volume":"16","author":"TK Ho","year":"1994","unstructured":"Ho TK, Hull JJ, Srihari SN (1994) Decision combination in multiple classifier systems. IEEE Trans Pattern Anal Mach Intell 16(1):66\u201375","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9949_CR27","doi-asserted-by":"crossref","unstructured":"Islam MZ, Liu J, Liu L, Li J, Kang W (2019) Semantic explanations in ensemble learning. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Cham, pp 29\u201341","DOI":"10.1007\/978-3-030-16148-4_3"},{"key":"9949_CR28","doi-asserted-by":"crossref","unstructured":"Jahrer M, T\u00f6scher A, Legenstein R (2010) Combining predictions for accurate recommender systems. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol 25, pp 693\u2013702","DOI":"10.1145\/1835804.1835893"},{"key":"9949_CR29","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.knosys.2017.01.014","volume":"121","author":"S Jendoubi","year":"2017","unstructured":"Jendoubi S, Martin A, Li\u00e9tard L, Ben Hadji H, Ben Yaghlane B (2017) Two evidential data based models for influence maximization in twitter. Knowl-Based Syst 121:58\u201370","journal-title":"Knowl-Based Syst"},{"key":"9949_CR30","doi-asserted-by":"crossref","unstructured":"Khan Z, Iltaf N, Afzal H, Abbas H (2019) Enriching non-negative matrix factorization with contextual Embeddings for recommender systems. Neurocomputing 380:246\u2013258","DOI":"10.1016\/j.neucom.2019.09.080"},{"key":"9949_CR31","doi-asserted-by":"crossref","unstructured":"Koren Y, Bell R (2015) Advances in collaborative filtering. In: Recommender systems handbook. Springer, Boston, pp 77\u2013118","DOI":"10.1007\/978-1-4899-7637-6_3"},{"key":"9949_CR32","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.inffus.2017.02.004","volume":"37","author":"B Krawczyk","year":"2017","unstructured":"Krawczyk B, Minku LL, Gama J, Stefanowski J, Wo\u017aniak M (2017) Ensemble learning for data stream analysis: a survey. Inf Fusion 37:132\u2013156","journal-title":"Inf Fusion"},{"key":"9949_CR33","unstructured":"Krogh A, Vedelsby J (1995) Neural network ensembles, cross validation, and active learning. Adv Neural Inf Process Syst 7:231\u2013238"},{"key":"9949_CR34","unstructured":"Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International Conference on Machine Learning, vol 27, pp 1188\u20131196"},{"key":"9949_CR35","doi-asserted-by":"crossref","unstructured":"Lerato M, Esan OA, Ebunoluwa AD, Ngwira SM, Zuva T (2015) A survey of recommender system feedback techniques, comparison and evaluation metrics. In: 2015 International Conference on Computing, Communication and Security (ICCCS). IEEE, Pamplemousses, pp 1\u20134","DOI":"10.1109\/CCCS.2015.7374146"},{"key":"9949_CR36","doi-asserted-by":"crossref","unstructured":"Lin Y, Liu Z, Sun M, Liu Y, Zhu X (2015) Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp 2181\u20132187","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"9949_CR37","unstructured":"Mikolov T et al (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781"},{"key":"9949_CR38","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp 3111\u20133119"},{"issue":"6","key":"9949_CR39","doi-asserted-by":"publisher","first-page":"1902","DOI":"10.1016\/j.ygeno.2019.01.001","volume":"111","author":"T Mohammadpour","year":"2019","unstructured":"Mohammadpour T, Bidgoli AM, Enayatifar R, Javadi HHS (2019) Efficient clustering in collaborative filtering recommender system: hybrid method based on genetic algorithm and gravitational emulation local search algorithm. Genomics 111(6):1902\u20131912","journal-title":"Genomics"},{"key":"9949_CR40","doi-asserted-by":"crossref","unstructured":"Nie F, Zhu W, Li X (2017) Unsupervised large graph embedding. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp 2422\u20132428","DOI":"10.1609\/aaai.v31i1.10814"},{"key":"9949_CR41","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.cie.2017.05.016","volume":"109","author":"M Nilashi","year":"2017","unstructured":"Nilashi M, Bagherifard K, Rahmani M, Rafe V (2017) A recommender system for tourism industry using cluster ensemble and prediction machine learning techniques. Comput Ind Eng 109:357\u2013368","journal-title":"Comput Ind Eng"},{"key":"9949_CR42","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1016\/j.eswa.2017.09.058","volume":"92","author":"M Nilashi","year":"2018","unstructured":"Nilashi M, Ibrahim O, Bagherifard K (2018) A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Syst Appl 92:507\u2013520","journal-title":"Expert Syst Appl"},{"issue":"8","key":"9949_CR43","doi-asserted-by":"publisher","first-page":"3879","DOI":"10.1016\/j.eswa.2013.12.023","volume":"41","author":"M Nilashi","year":"2014","unstructured":"Nilashi M, Ibrahim OB, Ithnin N (2014) Hybrid recommendation approaches for multi-criteria collaborative filtering. Expert Syst Appl 41(8):3879\u20133900","journal-title":"Expert Syst Appl"},{"key":"9949_CR44","doi-asserted-by":"publisher","first-page":"113235","DOI":"10.1016\/j.eswa.2020.113235","volume":"151","author":"E Palumbo","year":"2020","unstructured":"Palumbo E, Monti D, Rizzo G, Troncy R, Baralis E (2020) entity2rec: Property-specific knowledge graph embeddings for item recommendation. Expert Syst Appl 151:113235","journal-title":"Expert Syst Appl"},{"key":"9949_CR45","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"key":"9949_CR46","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, vol 24, pp 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"issue":"11","key":"9949_CR47","doi-asserted-by":"publisher","first-page":"1282","DOI":"10.1109\/10.959324","volume":"48","author":"A Porta","year":"2001","unstructured":"Porta A, Guzzetti S, Montano N, Furlan R, Pagani M, Malliani A, Cerutti S (2001) Entropy, entropy rate, and pattern classification as tools to typify complexity in short heart period variability series. IEEE Trans Biomed Eng 48(11):1282\u20131291","journal-title":"IEEE Trans Biomed Eng"},{"key":"9949_CR48","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.ins.2019.03.064","volume":"490","author":"A Pujahari","year":"2019","unstructured":"Pujahari A, Sisodia DS (2019) Modeling side information in preference relation based restricted boltzmann machine for recommender systems. Inf Sci 490:126\u2013145","journal-title":"Inf Sci"},{"key":"9949_CR49","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.neucom.2017.05.100","volume":"278","author":"L Qiu","year":"2018","unstructured":"Qiu L, Gao S, Lyu Q, Guo J, Gallinari P (2018) A novel non-Gaussian embedding based model for recommender systems. Neurocomputing 278:144\u2013152","journal-title":"Neurocomputing"},{"key":"9949_CR50","doi-asserted-by":"publisher","first-page":"113115","DOI":"10.1016\/j.dss.2019.113115","volume":"125","author":"J Ren","year":"2019","unstructured":"Ren J, Long J, Xu Z (2019) Financial news recommendation based on graph embeddings. Decis Support Syst 125:113115","journal-title":"Decis Support Syst"},{"key":"9949_CR51","doi-asserted-by":"crossref","unstructured":"Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. In: Recommender systems handbook. Springer, Boston, pp 1\u201335","DOI":"10.1007\/978-0-387-85820-3_1"},{"key":"9949_CR52","doi-asserted-by":"crossref","unstructured":"Ristoski P, Menc\u00eda EL, Paulheim H (2014) A hybrid multi-strategy recommender system using linked open data. In: Semantic web evaluation challenge. Springer, Cham, pp 150\u2013156","DOI":"10.1007\/978-3-319-12024-9_19"},{"issue":"8","key":"9949_CR53","doi-asserted-by":"publisher","first-page":"4370","DOI":"10.1016\/j.ygeno.2020.07.027","volume":"112","author":"M Rostami","year":"2020","unstructured":"Rostami M, Forouzandeh S, Berahmand K, Soltani M (2020) Integration of multi-objective PSO based feature selection and node centrality for medical datasets. Genomics 112(8):4370\u20134384","journal-title":"Genomics"},{"key":"9949_CR54","unstructured":"Sadeghian A et al (2019) Hotel2vec: Learning Attribute-Aware Hotel Embeddings with Self-Supervision. arXiv preprint arXiv:1910.03943"},{"key":"9949_CR55","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.eswa.2016.10.024","volume":"69","author":"Y-D Seo","year":"2017","unstructured":"Seo Y-D, Kim YG, Lee E, Baik DK (2017) Personalized recommender system based on friendship strength in social network services. Expert Syst Appl 69:135\u2013148","journal-title":"Expert Syst Appl"},{"key":"9949_CR56","doi-asserted-by":"crossref","unstructured":"Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, vol 18, pp 1067\u20131077","DOI":"10.1145\/2736277.2741093"},{"key":"9949_CR57","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1016\/j.engappai.2019.06.020","volume":"85","author":"D Valcarce","year":"2019","unstructured":"Valcarce D, Landin A, Parapar J, Barreiro \u00c1 (2019) Collaborative filtering embeddings for memory-based recommender systems. Eng Appl Artif Intell 85:347\u2013356","journal-title":"Eng Appl Artif Intell"},{"key":"9949_CR58","doi-asserted-by":"crossref","unstructured":"Vasile F, Smirnova E, Conneau A (2016) Meta-prod2vec: product embeddings using side-information for recommendation. In: Proceedings of the 10th ACM Conference on Recommender Systems, vol 7, pp 225\u2013232","DOI":"10.1145\/2959100.2959160"},{"key":"9949_CR59","doi-asserted-by":"crossref","unstructured":"Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. AAAI 14(2014):1112\u20131119","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"9949_CR60","doi-asserted-by":"crossref","unstructured":"Wang X, Cui P, Wang J, Pei J, Zhu W, Yang S (2017) Community preserving network embedding. AAAI 17:203\u2013209","DOI":"10.1609\/aaai.v31i1.10488"},{"key":"9949_CR61","volume-title":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management","author":"H Wang","year":"2018","unstructured":"Wang H et al (2018) Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management"},{"key":"9949_CR62","doi-asserted-by":"crossref","unstructured":"Wei X, Xu L, Cao B, Yu PS (2017) Cross view link prediction by learning noise-resilient representation consensus. In: Proceedings of the 26th International Conference on World Wide Web, vol 3, pp 1611\u20131619","DOI":"10.1145\/3038912.3052575"},{"key":"9949_CR63","doi-asserted-by":"crossref","unstructured":"Wolpert DH (2002) The supervised learning no-free-lunch theorems. In: Soft computing and industry. Springer, London, pp. 25\u201342","DOI":"10.1007\/978-1-4471-0123-9_3"},{"key":"9949_CR64","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.inffus.2013.04.006","volume":"16","author":"M Wo\u017aniak","year":"2014","unstructured":"Wo\u017aniak M, Gra\u00f1a M, Corchado E (2014) A survey of multiple classifier systems as hybrid systems. Inf Fusion 16:3\u201317","journal-title":"Inf Fusion"},{"key":"9949_CR65","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.ins.2019.05.001","volume":"495","author":"Y Xie","year":"2019","unstructured":"Xie Y, Gong M, Wang S, Liu W, Yu B (2019) Sim2vec: node similarity preserving network embedding. Inf Sci 495:37\u201351","journal-title":"Inf Sci"},{"key":"9949_CR66","doi-asserted-by":"crossref","unstructured":"Yu L, Cui P, Song C, Zhang T, Yang S (2017) A temporally heterogeneous survival framework with application to social behavior dynamics. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol 13, pp 1295\u20131304","DOI":"10.1145\/3097983.3098189"},{"issue":"4","key":"9949_CR67","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1093\/bioinformatics\/btz718","volume":"36","author":"X Yue","year":"2020","unstructured":"Yue X, Wang Z, Huang J, Parthasarathy S, Moosavinasab S, Huang Y, Lin SM, Zhang W, Zhang P, Sun H (2020) Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4):1241\u20131251","journal-title":"Bioinformatics"},{"key":"9949_CR68","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.ins.2019.04.033","volume":"493","author":"A Zareie","year":"2019","unstructured":"Zareie A, Sheikhahmadi A, Jalili M (2019) Identification of influential users in social networks based on users\u2019 interest. Inf Sci 493:217\u2013231","journal-title":"Inf Sci"},{"key":"9949_CR69","doi-asserted-by":"crossref","unstructured":"Zenobi G, Cunningham P (2001) Using diversity in preparing ensembles of classifiers based on different feature subsets to minimize generalization error. In: European Conference on Machine Learning. Springer, Berlin, Heidelberg, pp 576\u2013587","DOI":"10.1007\/3-540-44795-4_49"},{"key":"9949_CR70","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.knosys.2015.12.025","volume":"96","author":"F Zhang","year":"2016","unstructured":"Zhang F, Gong T, Lee VE, Zhao G, Rong C, Qu G (2016) Fast algorithms to evaluate collaborative filtering recommender systems. Knowl-Based Syst 96:96\u2013103","journal-title":"Knowl-Based Syst"},{"key":"9949_CR71","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1016\/j.ins.2018.04.022","volume":"453","author":"M Zhang","year":"2018","unstructured":"Zhang M, Wang J, Wang W (2018) HeteRank: a general similarity measure in heterogeneous information networks by integrating multi-type relationships. Inf Sci 453:389\u2013407","journal-title":"Inf Sci"},{"key":"9949_CR72","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.neucom.2019.01.028","volume":"334","author":"W Zhang","year":"2019","unstructured":"Zhang W, Zhang X, Wang H, Chen D (2019) A deep variational matrix factorization method for recommendation on large scale sparse dataset. Neurocomputing 334:206\u2013218","journal-title":"Neurocomputing"},{"key":"9949_CR73","doi-asserted-by":"publisher","first-page":"979","DOI":"10.1016\/j.neucom.2015.08.054","volume":"173","author":"W Zhang","year":"2016","unstructured":"Zhang W, Zou H, Luo L, Liu Q, Wu W, Xiao W (2016) Predicting potential side effects of drugs by recommender methods and ensemble learning. Neurocomputing 173:979\u2013987","journal-title":"Neurocomputing"},{"key":"9949_CR74","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.eswa.2019.06.045","volume":"136","author":"H Zhou","year":"2019","unstructured":"Zhou H, Zhao Z, Li C, Liang Y, Zeng Q (2019) Rank2vec: learning node embeddings with local structure and global ranking. Expert Syst Appl 136:276\u2013287","journal-title":"Expert Syst Appl"},{"key":"9949_CR75","doi-asserted-by":"crossref","unstructured":"Zhou C, Liu Y, Liu X, Liu Z, Gao J (2017) Scalable graph embedding for asymmetric proximity. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp 2942\u20132948","DOI":"10.1609\/aaai.v31i1.10878"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09949-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11042-020-09949-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09949-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T00:16:51Z","timestamp":1696983411000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11042-020-09949-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,30]]},"references-count":75,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,2]]}},"alternative-id":["9949"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-09949-5","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,30]]},"assertion":[{"value":"8 February 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 September 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 October 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}