{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T13:51:47Z","timestamp":1758981107105,"version":"3.37.3"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T00:00:00Z","timestamp":1620259200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T00:00:00Z","timestamp":1620259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"published-print":{"date-parts":[[2021,7]]},"DOI":"10.1007\/s42979-021-00668-8","type":"journal-article","created":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T12:02:53Z","timestamp":1620302573000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["On Addressing the Low Rating Prediction Coverage in Sparse Datasets Using Virtual Ratings"],"prefix":"10.1007","volume":"2","author":[{"given":"Dionisis","family":"Margaris","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dimitris","family":"Spiliotopoulos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gregory","family":"Karagiorgos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9940-1821","authenticated-orcid":false,"given":"Costas","family":"Vassilakis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dionysios","family":"Vasilopoulos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,6]]},"reference":[{"issue":"3","key":"668_CR1","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1145\/245108.245124","volume":"40","author":"M Balabanovic","year":"1997","unstructured":"Balabanovic M, Shoham Y. Fab: content-based, collaborative recommendation. Commun ACM. 1997;40(3):66\u201372. https:\/\/doi.org\/10.1145\/245108.245124.","journal-title":"Commun ACM"},{"issue":"2","key":"668_CR2","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1561\/1100000009","volume":"4","author":"M Ekstrand","year":"2011","unstructured":"Ekstrand M, Riedl R, Konstan J. Collaborative filtering recommender systems. Found Trends Hum Comput Interact. 2011;4(2):81\u2013173. https:\/\/doi.org\/10.1561\/1100000009.","journal-title":"Found Trends Hum Comput Interact"},{"key":"668_CR3","doi-asserted-by":"publisher","unstructured":"Margaris D, Vasilopoulos D, Vassilakis C, Spiliotopoulos D. Improving collaborative filtering\u2019s rating prediction coverage in sparse datasets through the introduction of virtual near neighbors. In: Proceedings of the 2019 10th international conference on information, intelligence, systems and applications (IISA). 2019. p. 1\u20138. https:\/\/doi.org\/10.1109\/IISA.2019.8900678.","DOI":"10.1109\/IISA.2019.8900678."},{"issue":"7","key":"668_CR4","doi-asserted-by":"publisher","first-page":"174","DOI":"10.3390\/a13070174","volume":"13","author":"D Margaris","year":"2020","unstructured":"Margaris D, Spiliotopoulos D, Karagiorgos G, Vassilakis C. An algorithm for density enrichment of sparse collaborative filtering datasets using robust predictions as derived ratings. Algorithms. 2020;13(7):174. https:\/\/doi.org\/10.3390\/a13070174.","journal-title":"Algorithms"},{"issue":"8","key":"668_CR5","doi-asserted-by":"publisher","first-page":"42","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. IEEE Comput. 2009;42(8):42\u20139. https:\/\/doi.org\/10.1109\/MC.2009.263.","journal-title":"IEEE Comput"},{"key":"668_CR6","doi-asserted-by":"publisher","unstructured":"McAuley JJ, Targett C, Shi Q, Van den Hengel A. Image-Based recommendations on styles and substitutes. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. 2015. p. 43\u201352. https:\/\/doi.org\/10.1145\/2766462.2767755..","DOI":"10.1145\/2766462.2767755."},{"key":"668_CR7","doi-asserted-by":"publisher","unstructured":"Margaris D, Vassilakis C. Improving collaborative filtering\u2019s rating prediction quality in dense datasets, by pruning old ratings. In: Proceedings of the 2017 IEEE symposium on computers and communications (ISCC). 2017. p. 1168\u20131174. https:\/\/doi.org\/10.1109\/ISCC.2017.8024683..","DOI":"10.1109\/ISCC.2017.8024683."},{"key":"668_CR8","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/978-3-662-55947-5_3","volume":"XXXIV","author":"D Margaris","year":"2017","unstructured":"Margaris D, Vassilakis C. Enhancing user rating database consistency through pruning. Trans Large Scale Data Knowl Cent Syst. 2017;XXXIV:33\u201364. https:\/\/doi.org\/10.1007\/978-3-662-55947-5_3.","journal-title":"Trans Large Scale Data Knowl Cent Syst"},{"key":"668_CR9","doi-asserted-by":"publisher","DOI":"10.1145\/2523813","author":"J Gama","year":"2013","unstructured":"Gama J, Zliobaite I, Bifet A, Pechenizkiy M, Bouchachia A. A survey on concept drift adaptation. ACM Comput Surv. 2013. https:\/\/doi.org\/10.1145\/2523813.","journal-title":"ACM Comput Surv"},{"issue":"1","key":"668_CR10","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/s40747-019-00124-4","volume":"6","author":"J Lu","year":"2020","unstructured":"Lu J, Liu A, Song Y, Zhang G. Data-driven decision support under concept drift in streamed big data. Complex Intell Syst. 2020;6(1):157\u201363. https:\/\/doi.org\/10.1007\/s40747-019-00124-4.","journal-title":"Complex Intell Syst"},{"issue":"6","key":"668_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3406243","volume":"14","author":"R Paudel","year":"2020","unstructured":"Paudel R, Eberle W. An approach for concept drift detection in a graph stream using discriminative subgraphs. ACM Trans Knowl Discov Data. 2020;14(6):1\u201325. https:\/\/doi.org\/10.1145\/3406243.","journal-title":"ACM Trans Knowl Discov Data"},{"issue":"7","key":"668_CR12","doi-asserted-by":"publisher","first-page":"745","DOI":"10.4304\/jsw.5.7.745-752","volume":"5","author":"S Gong","year":"2010","unstructured":"Gong S. A collaborative filtering recommendation algorithm based on user clustering and item clustering. J Softw. 2010;5(7):745\u201352.","journal-title":"J Softw"},{"issue":"1","key":"668_CR13","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1007\/s40747-019-00123-5","volume":"6","author":"J Chen","year":"2020","unstructured":"Chen J, Zhao C, Uliji CL. Collaborative filtering recommendation algorithm based on user correlation and evolutionary clustering. Complex Intell Syst. 2020;6(1):147\u201356. https:\/\/doi.org\/10.1007\/s40747-019-00123-5.","journal-title":"Complex Intell Syst"},{"issue":"4","key":"668_CR14","doi-asserted-by":"publisher","first-page":"583","DOI":"10.3217\/jucs-017-04-0583","volume":"17","author":"M Pham","year":"2011","unstructured":"Pham M, Cao Y, Klamma R, Jarke M. A clustering approach for collaborative filtering recommendation using social network analysis. J Univ Comput Sci. 2011;17(4):583\u2013604. https:\/\/doi.org\/10.3217\/jucs-017-04-0583.","journal-title":"J Univ Comput Sci"},{"key":"668_CR15","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1016\/j.future.2017.05.036","volume":"80","author":"A Kala\u00ef","year":"2018","unstructured":"Kala\u00ef A, Zayani CA, Amous I, Abdelghani W, S\u00e8des F. Social collaborative service recommendation approach based on user\u2019s trust and domain-specific expertise. Futur Gener Comput Syst. 2018;80:355\u201367. https:\/\/doi.org\/10.1016\/j.future.2017.05.036.","journal-title":"Futur Gener Comput Syst"},{"key":"668_CR16","doi-asserted-by":"publisher","unstructured":"Margaris D, Spiliotopoulos D, Vassilakis C. Social relations versus near neighbours: reliable recommenders in limited information social network collaborative filtering for online advertising. In: Proceedings of the 2019 IEEE\/ACM international conference on advances in social networks analysis and mining (ASONAM 2019). 2019. p. 1160\u20131167. https:\/\/doi.org\/10.1145\/3341161.3345620..","DOI":"10.1145\/3341161.3345620."},{"issue":"1","key":"668_CR17","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1007\/s10660-019-09390-3","volume":"20","author":"J Sun","year":"2020","unstructured":"Sun J, Ying R, Jiang Y, He J, Ding Z. Leveraging friend and group information to improve social recommender system. Electron Commer Res. 2020;20(1):147\u201372. https:\/\/doi.org\/10.1007\/s10660-019-09390-3.","journal-title":"Electron Commer Res"},{"key":"668_CR18","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1007\/978-1-4419-0221-4_57","volume":"296","author":"M Vozalis","year":"2009","unstructured":"Vozalis M, Markos A, Margaritis K. A hybrid approach for improving prediction coverage of collaborative filtering. Artif Intell Appl Innov. 2009;296:491\u20138. https:\/\/doi.org\/10.1007\/978-1-4419-0221-4_57.","journal-title":"Artif Intell Appl Innov"},{"key":"668_CR19","doi-asserted-by":"publisher","unstructured":"Zhang S, Yao L, Xu X. Autosvd++: an efficient hybrid collaborative filtering model via contractive auto-encoders. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval. 2017. p. 957\u2013960. https:\/\/doi.org\/10.1145\/3077136.3080689..","DOI":"10.1145\/3077136.3080689."},{"key":"668_CR20","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1007\/s11036-019-01246-2","volume":"25","author":"X Yang","year":"2020","unstructured":"Yang X, Zhou S, Cao M. An approach to alleviate the sparsity problem of hybrid collaborative filtering based recommendations: the product-attribute perspective from user reviews. Mob Netw Appl. 2020;25:376\u201390. https:\/\/doi.org\/10.1007\/s11036-019-01246-2.","journal-title":"Mob Netw Appl"},{"key":"668_CR21","doi-asserted-by":"publisher","first-page":"113452","DOI":"10.1016\/j.eswa.2020.113452","volume":"158","author":"B Walek","year":"2020","unstructured":"Walek B, Fojtik V. A hybrid recommender system for recommending relevant movies using an expert system. Expert Syst Appl. 2020;158:113452. https:\/\/doi.org\/10.1016\/j.eswa.2020.113452.","journal-title":"Expert Syst Appl"},{"key":"668_CR22","doi-asserted-by":"publisher","unstructured":"Adamopoulos P. Beyond rating prediction accuracy: on new perspectives in recommender systems. In: Proceedings of the 7th ACM conference on recommender systems (RecSys \u203213). 2013. p. 459\u2013462. https:\/\/doi.org\/10.1145\/2507157.2508073..","DOI":"10.1145\/2507157.2508073."},{"key":"668_CR23","doi-asserted-by":"publisher","first-page":"68301","DOI":"10.1109\/ACCESS.2020.2981567","volume":"8","author":"D Margaris","year":"2020","unstructured":"Margaris D, Kobusi\u0144ska A, Spiliotopoulos D, Vassilakis C. An adaptive social network-aware collaborative filtering algorithm for improved rating prediction accuracy. IEEE Access. 2020;8:68301\u201310. https:\/\/doi.org\/10.1109\/ACCESS.2020.2981567.","journal-title":"IEEE Access"},{"key":"668_CR24","doi-asserted-by":"publisher","unstructured":"Margaris D, Vassilakis C. Improving collaborative filtering's rating prediction coverage in sparse datasets by exploiting user dissimilarity. In: Proceedings of the 4th IEEE international conference on big data intelligence and computing. 2018. p. 1054\u20131059. https:\/\/doi.org\/10.1109\/DASC\/PiCom\/DataCom\/CyberSciTec.2018.00150..","DOI":"10.1109\/DASC\/PiCom\/DataCom\/CyberSciTec.2018.00150."},{"key":"668_CR25","doi-asserted-by":"publisher","unstructured":"Wang P, Huang H, Zhu J, Qi L. A trust-based prediction approach for recommendation system. In: Proceedings of the world congress on services 2018, LNCS, vol. 10975. Cham, Springer. 2018. p. 157\u2013164. https:\/\/doi.org\/10.1007\/978-3-319-94472-2_12..","DOI":"10.1007\/978-3-319-94472-2_12."},{"key":"668_CR26","doi-asserted-by":"publisher","unstructured":"Zarei MR, Moosavi MR. A memory-based collaborative filtering recommender system using social ties. In: Proceedings of the 4th international conference on pattern recognition and image analysis (IPRIA). 2019. p. 263\u2013267. https:\/\/doi.org\/10.1109\/PRIA.2019.8786023..","DOI":"10.1109\/PRIA.2019.8786023."},{"key":"668_CR27","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1007\/978-3-319-11116-2_27","volume":"2014","author":"H Wen","year":"2014","unstructured":"Wen H, Ding G, Liu C, Wang J. Matrix factorization meets cosine similarity: addressing sparsity problem in collaborative filtering recommender system. Proc APWeb. 2014;2014:306\u201317. https:\/\/doi.org\/10.1007\/978-3-319-11116-2_27.","journal-title":"Proc APWeb"},{"key":"668_CR28","doi-asserted-by":"publisher","first-page":"27668","DOI":"10.1109\/ACCESS.2017.2772226","volume":"5","author":"X Guan","year":"2017","unstructured":"Guan X, Li C, Guan Y. Matrix factorization with rating completion: an enhanced SVD model for collaborative filtering recommender systems. IEEE Access. 2017;5:27668\u201378. https:\/\/doi.org\/10.1109\/ACCESS.2017.2772226.","journal-title":"IEEE Access"},{"key":"668_CR29","doi-asserted-by":"publisher","unstructured":"Poirier D, Fessant F, Tellier I. Reducing the cold-start problem in content recommendation through opinion classification. In: Proceedings of the 2010 IEEE\/WIC\/ACM international conference on web intelligence and intelligent agent technology. 2010. p. 204\u2013207. https:\/\/doi.org\/10.1109\/WI-IAT.2010.87..","DOI":"10.1109\/WI-IAT.2010.87."},{"key":"668_CR30","doi-asserted-by":"publisher","unstructured":"Moshfeghi Y, Piwowarski B, Jose JM. Handling data sparsity in collaborative filtering using emotion and semantic based features. In: Proceedings of 34th international ACM SIGIR conference. 2011. p. 625\u2013634. https:\/\/doi.org\/10.1145\/2009916.2010001..","DOI":"10.1145\/2009916.2010001."},{"key":"668_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-019-0610-x","volume":"64","author":"D Margaris","year":"2019","unstructured":"Margaris D, Vassilakis C, Spiliotopoulos D. Handling uncertainty in social media textual information for improving venue recommendation formulation quality in social networks. Soc Netw Anal Min. 2019;64:1\u201319. https:\/\/doi.org\/10.1007\/s13278-019-0610-x.","journal-title":"Soc Netw Anal Min"},{"issue":"1","key":"668_CR32","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1504\/IJBDI.2020.106178","volume":"7","author":"D Margaris","year":"2020","unstructured":"Margaris D, Vassilakis C. Improving collaborative filtering\u2019s rating prediction coverage in sparse datasets by exploiting the \u2018friend of a friend\u2019 concept. Int J Big Data Intell. 2020;7(1):47\u201357. https:\/\/doi.org\/10.1504\/IJBDI.2020.106178.","journal-title":"Int J Big Data Intell"},{"key":"668_CR33","doi-asserted-by":"publisher","unstructured":"O\u2032Mahony MP, Hurley NJ, Silvestre G. Detecting noise in recommender system databases. In: Proceedings of the 11th international conference on intelligent user interfaces. 2006. p. 109\u2013115. https:\/\/doi.org\/10.1145\/1111449.1111477..","DOI":"10.1145\/1111449.1111477."},{"issue":"1","key":"668_CR34","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.dss.2013.01.020","volume":"55","author":"CY Chung","year":"2013","unstructured":"Chung CY, Hsu PY, Huang SH. \u03b2P: a novel approach to filter out malicious rating profiles from recommender systems. Decis Support Syst. 2013;55(1):314\u201325. https:\/\/doi.org\/10.1016\/j.dss.2013.01.020.","journal-title":"Decis Support Syst"},{"issue":"1","key":"668_CR35","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1287\/ijoc.1100.0440","volume":"24","author":"JS Lee","year":"2012","unstructured":"Lee JS, Zhu D. Shilling attack detection-a new approach for a trustworthy recommender system. INFORMS J Comput. 2012;24(1):117\u201331. https:\/\/doi.org\/10.1287\/ijoc.1100.0440.","journal-title":"INFORMS J Comput"},{"key":"668_CR36","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.knosys.2014.12.011","volume":"76","author":"RY Toledo","year":"2015","unstructured":"Toledo RY, Mota YC. Correcting noisy ratings in collaborative recommender systems. Knowl Based Syst. 2015;76:96\u2013108. https:\/\/doi.org\/10.1016\/j.knosys.2014.12.011.","journal-title":"Knowl Based Syst"},{"key":"668_CR37","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.asoc.2015.10.060","volume":"40","author":"R Yera","year":"2016","unstructured":"Yera R, Castro J, Mart\u00ednez L. A fuzzy model for managing natural noise in recommender systems. Appl Soft Comput. 2016;40:187\u201398. https:\/\/doi.org\/10.1016\/j.asoc.2015.10.060.","journal-title":"Appl Soft Comput"},{"key":"668_CR38","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.knosys.2015.03.001","volume":"82","author":"BK Patra","year":"2015","unstructured":"Patra BK, Launonen R, Ollikainen V, Nandib S. A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data. Knowl Based Syst. 2015;82:163\u201377. https:\/\/doi.org\/10.1016\/j.knosys.2015.03.001.","journal-title":"Knowl Based Syst"},{"key":"668_CR39","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.dss.2019.01.001","volume":"118","author":"S Bag","year":"2019","unstructured":"Bag S, Kumar S, Awasthi A, Tiwaria K. A noise correction-based approach to support a recommender system in a highly sparse rating environment. Decis Support Syst. 2019;118:46\u201357. https:\/\/doi.org\/10.1016\/j.dss.2019.01.001.","journal-title":"Decis Support Syst"},{"key":"668_CR40","unstructured":"Amazon product data. Available online: http:\/\/jmcauley.ucsd.edu\/data\/amazon\/links.html. Accessed 4 Apr 2019."},{"key":"668_CR41","doi-asserted-by":"publisher","unstructured":"McAuley JJ, Pandey R, Leskovec J. Inferring networks of substitutable and complementary products. In: Proceedings of the 21th ACM SIGKDD conference. 2015. p. 785\u2013794. https:\/\/doi.org\/10.1145\/2783258.2783381.","DOI":"10.1145\/2783258.2783381."},{"key":"668_CR42","unstructured":"MovieLens datasets. http:\/\/grouplens.org\/datasets\/movielens\/. Accessed 4 Apr 2019."},{"issue":"4","key":"668_CR43","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1145\/2827872","volume":"5","author":"FM Harper","year":"2015","unstructured":"Harper FM, Konstan JA. The MovieLens datasets: history and context. ACM Trans Interact Intell Syst. 2015;5(4):19. https:\/\/doi.org\/10.1145\/2827872.","journal-title":"ACM Trans Interact Intell Syst"},{"issue":"3","key":"668_CR44","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1109\/MIS.2007.49","volume":"2","author":"M Zanker","year":"2007","unstructured":"Zanker M, Jessenitschnig M, Jannach D, Gordea S. Comparing recommendation strategies in a commercial context. IEEE Intell Syst. 2007;2(3):69\u201373. https:\/\/doi.org\/10.1109\/MIS.2007.49.","journal-title":"IEEE Intell Syst"},{"key":"668_CR45","doi-asserted-by":"publisher","unstructured":"Ramadhan Z, Siahaan A, Mesran M. Prim and Floyd\u2013Warshall comparative algorithms in shortest path problem. In: Proceedings of the joint workshop KO2PI and the 1st international conference on advance and scientific innovation. 2018. p. 47\u201358. https:\/\/doi.org\/10.4108\/eai.23-4-2018.2277598..","DOI":"10.4108\/eai.23-4-2018.2277598."},{"issue":"1","key":"668_CR46","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1145\/963770.963772","volume":"22","author":"JL Herlocker","year":"2004","unstructured":"Herlocker JL, Konstan JA, Terveen LG, Riedl JT. Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst. 2004;22(1):5\u201353. https:\/\/doi.org\/10.1145\/963770.963772.","journal-title":"ACM Trans Inf Syst"},{"key":"668_CR47","doi-asserted-by":"publisher","first-page":"1607","DOI":"10.1007\/s00521-020-05085-1","volume":"33","author":"H Tahmasebi","year":"2021","unstructured":"Tahmasebi H, Ravanmehr R, Mohamadrezaei R. Social movie recommender system based on deep autoencoder network using Twitter data. Neural Comput Appl. 2021;33:1607\u201323. https:\/\/doi.org\/10.1007\/s00521-020-05085-1.","journal-title":"Neural Comput Appl"},{"issue":"2","key":"668_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3127873","volume":"12","author":"GN Hu","year":"2018","unstructured":"Hu GN, Dai XY, Qiu FY, Xia R, Li T, Huang SJ, Chen JJ. Collaborative filtering with topic and social latent factors incorporating implicit feedback. ACM Trans Knowl Discov Data (TKDD). 2018;12(2):1\u201330. https:\/\/doi.org\/10.1145\/3127873.","journal-title":"ACM Trans Knowl Discov Data (TKDD)"},{"key":"668_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-019-0621-7","volume":"8","author":"M Aivazoglou","year":"2020","unstructured":"Aivazoglou M, Roussos A, Margaris D, Vassilakis C, Ioannidis S, Polakis J, Spiliotopoulos D. A fine-grained social network recommender system. Soc Netw Anal Min. 2020;8:1\u201318. https:\/\/doi.org\/10.1007\/s13278-019-0621-7.","journal-title":"Soc Netw Anal Min"},{"key":"668_CR50","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1007\/978-1-4899-7637-6_15","volume-title":"Recommender systems handbook","author":"I Guy","year":"2015","unstructured":"Guy I. Social recommender systems. In: Ricci F, Rokach L, Shapira B, editors. Recommender systems handbook. Boston: Springer; 2015. p. 511\u201343."},{"key":"668_CR51","doi-asserted-by":"publisher","first-page":"13326","DOI":"10.1109\/ACCESS.2018.2806488","volume":"6","author":"X Zuo","year":"2018","unstructured":"Zuo X, Liu X, Yang B. Coupled low rank approximation for collaborative filtering in social networks. IEEE Access. 2018;6:13326\u201335. https:\/\/doi.org\/10.1109\/ACCESS.2018.2806488.","journal-title":"IEEE Access"},{"key":"668_CR52","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.future.2020.02.041","volume":"108","author":"S Ojagh","year":"2020","unstructured":"Ojagh S, Malek MR, Saeedi S, Liang S. A location-based orientation-aware recommender system using IoT smart devices and social networks. Futur Gener Comput Syst. 2020;108:97\u2013118. https:\/\/doi.org\/10.1016\/j.future.2020.02.041.","journal-title":"Futur Gener Comput Syst"},{"issue":"6","key":"668_CR53","doi-asserted-by":"publisher","first-page":"3184","DOI":"10.1007\/s11227-018-2331-8","volume":"75","author":"V Subramaniyaswamy","year":"2019","unstructured":"Subramaniyaswamy V, Manogaran G, Logesh R, Vijayakumar V, Chilamkurti N, Malathi D, Senthilselvan N. An ontology-driven personalized food recommendation in IoT-based healthcare system. J Supercomput. 2019;75(6):3184\u2013216. https:\/\/doi.org\/10.1007\/s11227-018-2331-8.","journal-title":"J Supercomput"},{"issue":"4","key":"668_CR54","doi-asserted-by":"publisher","first-page":"4133","DOI":"10.1007\/s11042-017-4527-y","volume":"77","author":"C Ren","year":"2018","unstructured":"Ren C, Chen J, Kuo Y, Wu D, Yang M. Recommender system for mobile users. Multimed Tools Appl. 2018;77(4):4133\u201353. https:\/\/doi.org\/10.1007\/s11042-017-4527-y.","journal-title":"Multimed Tools Appl"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-021-00668-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-021-00668-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-021-00668-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,26]],"date-time":"2021-06-26T20:15:11Z","timestamp":1624738511000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-021-00668-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,6]]},"references-count":54,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["668"],"URL":"https:\/\/doi.org\/10.1007\/s42979-021-00668-8","relation":{},"ISSN":["2662-995X","2661-8907"],"issn-type":[{"type":"print","value":"2662-995X"},{"type":"electronic","value":"2661-8907"}],"subject":[],"published":{"date-parts":[[2021,5,6]]},"assertion":[{"value":"14 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving human participants and\/or animals"}},{"value":"This research is solely based on anonymized datasets, where no user identifiable information is present. The datasets were prepared and published in the public domain by third parties.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"255"}}