{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T07:36:31Z","timestamp":1773473791157,"version":"3.50.1"},"reference-count":110,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":["U1705262"],"award-info":[{"award-number":["U1705262"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2020J01130193"],"award-info":[{"award-number":["2020J01130193"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2018J07005"],"award-info":[{"award-number":["2018J07005"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s10115-021-01629-6","type":"journal-article","created":{"date-parts":[[2022,1,16]],"date-time":"2022-01-16T00:03:06Z","timestamp":1642291386000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["A review on matrix completion for recommender systems"],"prefix":"10.1007","volume":"64","author":[{"given":"Zhaoliang","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5195-9682","authenticated-orcid":false,"given":"Shiping","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,16]]},"reference":[{"issue":"1","key":"1629_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-018-9654-y","volume":"52","author":"Z Batmaz","year":"2019","unstructured":"Batmaz Z, Yurekli A, Bilge A, Kaleli C (2019) A review on deep learning for recommender systems: challenges and remedies. Artif Intell Rev 52(1):1\u201337","journal-title":"Artif Intell Rev"},{"key":"1629_CR2","doi-asserted-by":"crossref","unstructured":"Sun Y, Guo G, Chen X, Zhang P, Wang X (2020) Exploiting review embedding and user attention for item recommendation. Knowl Inform Syst 1\u201324","DOI":"10.1007\/s10115-020-01447-2"},{"issue":"12","key":"1629_CR3","doi-asserted-by":"publisher","first-page":"4625","DOI":"10.1007\/s10115-020-01499-4","volume":"62","author":"SM Hashemi","year":"2020","unstructured":"Hashemi SM, Rahmati M (2020) Cross-domain recommender system using generalized canonical correlation analysis. Knowl Inf Syst 62(12):4625\u20134651","journal-title":"Knowl Inf Syst"},{"key":"1629_CR4","doi-asserted-by":"crossref","unstructured":"Papagelis M, Plexousakis D, Kutsuras T (2005) Alleviating the sparsity problem of collaborative filtering using trust inferences. Trust Manag 224\u2013239","DOI":"10.1007\/11429760_16"},{"key":"1629_CR5","doi-asserted-by":"crossref","unstructured":"Martinez L, Rodriguez RM, Espinilla M, Reja (2009) A georeferenced hybrid recommender system for restaurants, in: Proceedings of the 2009 IEEE\/WIC\/ACM international joint conference on web intelligence and intelligent agent technology, pp 187\u2013190","DOI":"10.1109\/WI-IAT.2009.259"},{"issue":"1","key":"1629_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10115-018-1254-2","volume":"62","author":"M Singh","year":"2020","unstructured":"Singh M (2020) Scalability and sparsity issues in recommender datasets: a survey. Knowl Inf Syst 62(1):1\u201343","journal-title":"Knowl Inf Syst"},{"issue":"1","key":"1629_CR7","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1145\/963770.963775","volume":"22","author":"Z Huang","year":"2004","unstructured":"Huang Z, Chen H, Zeng D (2004) Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans Inf Syst 22(1):116\u2013142","journal-title":"ACM Trans Inf Syst"},{"key":"1629_CR8","doi-asserted-by":"crossref","unstructured":"Moshfeghi Y, Piwowarski B, Jose JM (2011) Handling data sparsity in collaborative filtering using emotion and semantic based features. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval, pp 625\u2013634","DOI":"10.1145\/2009916.2010001"},{"key":"1629_CR9","unstructured":"Li B, Yang Q, Xue X (2009) Can movies and books collaborate? Cross-domain collaborative filtering for sparsity reduction. In: Proceedings of the 21th international joint conference on artificial intelligence, vol\u00a09, pp 2052\u20132057"},{"key":"1629_CR10","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.cosrev.2019.01.001","volume":"31","author":"S Raza","year":"2019","unstructured":"Raza S, Ding C (2019) Progress in context-aware recommender systems\u2013an overview. Comput Sci Rev 31:84\u201397","journal-title":"Comput Sci Rev"},{"key":"1629_CR11","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.datak.2017.10.002","volume":"113","author":"I Palomares","year":"2018","unstructured":"Palomares I, Browne F, Davis P (2018) Multi-view fuzzy information fusion in collaborative filtering recommender systems: application to the urban resilience domain. Data Knowl Eng 113:64\u201380","journal-title":"Data Knowl Eng"},{"issue":"8","key":"1629_CR12","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 (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30\u201337","journal-title":"Computer"},{"issue":"3","key":"1629_CR13","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1145\/1010614.1010618","volume":"22","author":"BN Miller","year":"2004","unstructured":"Miller BN, Konstan JA, Riedl J (2004) Pocketlens: toward a personal recommender system. ACM Trans Inf Syst 22(3):437\u2013476","journal-title":"ACM Trans Inf Syst"},{"issue":"2","key":"1629_CR14","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.1007\/s10115-018-1324-5","volume":"61","author":"ZD Champiri","year":"2019","unstructured":"Champiri ZD, Asemi A, Binti SSS (2019) Meta-analysis of evaluation methods and metrics used in context-aware scholarly recommender systems. Knowl Inf Syst 61(2):1147\u20131178","journal-title":"Knowl Inf Syst"},{"key":"1629_CR15","doi-asserted-by":"publisher","first-page":"100255","DOI":"10.1016\/j.cosrev.2020.100255","volume":"37","author":"S Kulkarni","year":"2020","unstructured":"Kulkarni S, Rodd SF (2020) Context aware recommendation systems: a review of the state of the art techniques. Comput Sci Rev 37:100255","journal-title":"Comput Sci Rev"},{"issue":"2","key":"1629_CR16","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1007\/s10462-019-09684-w","volume":"53","author":"J Shokeen","year":"2020","unstructured":"Shokeen J, Rana C (2020) A study on features of social recommender systems. Artif Intell Rev 53(2):965\u2013988","journal-title":"Artif Intell Rev"},{"key":"1629_CR17","doi-asserted-by":"crossref","unstructured":"Khan ZY, Niu Z, Sandiwarno S, Prince R (2020) Deep learning techniques for rating prediction: a survey of the state-of-the-art. Artif Intell Rev 1\u201341","DOI":"10.1007\/s10462-020-09892-9"},{"key":"1629_CR18","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.cosrev.2016.05.002","volume":"20","author":"M Elahi","year":"2016","unstructured":"Elahi M, Ricci F, Rubens N (2016) A survey of active learning in collaborative filtering recommender systems. Comput Sci Rev 20:29\u201350","journal-title":"Comput Sci Rev"},{"key":"1629_CR19","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.datak.2019.06.003","volume":"122","author":"L Coba","year":"2019","unstructured":"Coba L, Symeonidis P, Zanker M (2019) Personalised novel and explainable matrix factorisation. Data Knowl Eng 122:142\u2013158","journal-title":"Data Knowl Eng"},{"issue":"1","key":"1629_CR20","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/s10462-018-9655-x","volume":"53","author":"M Si","year":"2020","unstructured":"Si M, Li Q (2020) Shilling attacks against collaborative recommender systems: a review. Artif Intell Rev 53(1):291\u2013319","journal-title":"Artif Intell Rev"},{"key":"1629_CR21","doi-asserted-by":"crossref","unstructured":"Ali Z, Qi G, Kefalas P, Abro WA, Ali B (2020) A graph-based taxonomy of citation recommendation models. Artif Intell Rev 1\u201344","DOI":"10.1007\/s10462-020-09819-4"},{"issue":"3","key":"1629_CR22","first-page":"98","volume":"3","author":"A Dax","year":"2014","unstructured":"Dax A (2014) Imputing missing entries of a data matrix: a review. J Adv Comput 3(3):98\u2013222","journal-title":"J Adv Comput"},{"key":"1629_CR23","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1016\/j.future.2017.03.020","volume":"78","author":"DF Gurini","year":"2018","unstructured":"Gurini DF, Gasparetti F, Micarelli A, Sansonetti G (2018) Temporal people-to-people recommendation on social networks with sentiment-based matrix factorization. Futur Gener Comput Syst 78:430\u2013439","journal-title":"Futur Gener Comput Syst"},{"key":"1629_CR24","doi-asserted-by":"publisher","first-page":"94215","DOI":"10.1109\/ACCESS.2019.2928130","volume":"7","author":"LT Nguyen","year":"2019","unstructured":"Nguyen LT, Kim J, Shim B (2019) Low-rank matrix completion: a contemporary survey. IEEE Access 7:94215\u201394237","journal-title":"IEEE Access"},{"key":"1629_CR25","doi-asserted-by":"publisher","first-page":"114436","DOI":"10.1016\/j.eswa.2020.114436","volume":"168","author":"Z Chen","year":"2021","unstructured":"Chen Z, Zhao W, Wang S (2021) Kernel meets recommender systems: a multi-kernel interpolation for matrix completion. Expert Syst Appl 168:114436","journal-title":"Expert Syst Appl"},{"issue":"6","key":"1629_CR26","doi-asserted-by":"publisher","first-page":"1336","DOI":"10.1109\/TKDE.2012.51","volume":"25","author":"Y Wang","year":"2013","unstructured":"Wang Y, Zhang Y (2013) Nonnegative matrix factorization: a comprehensive review. IEEE Trans Knowl Data Eng 25(6):1336\u20131353","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1629_CR27","doi-asserted-by":"crossref","unstructured":"Da\u2019u A, Salim N (2019) Recommendation system based on deep learning methods: a systematic review and new directions. Artif Intell Rev 1\u201340","DOI":"10.1007\/s10462-019-09744-1"},{"key":"1629_CR28","doi-asserted-by":"crossref","unstructured":"Cheng H-T, Koc L, Harmsen J, Shaked T, Chandra T, Aradhye H, Anderson G, Corrado G, Chai W, Ispir M, et\u00a0al. (2016) Wide & deep learning for recommender systems. In: Proceedings of the 1st workshop on deep learning for recommender systems, pp 7\u201310","DOI":"10.1145\/2988450.2988454"},{"key":"1629_CR29","doi-asserted-by":"crossref","unstructured":"Covington P, Adams J, Sargin E (2016) Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM conference on recommender systems, pp 191\u2013198","DOI":"10.1145\/2959100.2959190"},{"key":"1629_CR30","doi-asserted-by":"crossref","unstructured":"He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on World Wide Web, pp 173\u2013182","DOI":"10.1145\/3038912.3052569"},{"issue":"3","key":"1629_CR31","doi-asserted-by":"publisher","first-page":"1457","DOI":"10.1007\/s10115-018-1320-9","volume":"61","author":"Q Gu","year":"2019","unstructured":"Gu Q, Trzasko JD, Banerjee A (2019) Scalable algorithms for locally low-rank matrix modeling. Knowl Inf Syst 61(3):1457\u20131484","journal-title":"Knowl Inf Syst"},{"key":"1629_CR32","doi-asserted-by":"crossref","unstructured":"Nie F, Huang H, Ding CHQ (2012) Low-rank matrix recovery via efficient schatten p-norm minimization. In: Proceedings of the 26th AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v26i1.8210"},{"key":"1629_CR33","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.future.2018.07.065","volume":"90","author":"B Shijila","year":"2019","unstructured":"Shijila B, Tom AJ, George SN (2019) Simultaneous denoising and moving object detection using low rank approximation. Futur Gener Comput Syst 90:198\u2013210","journal-title":"Futur Gener Comput Syst"},{"issue":"6755","key":"1629_CR34","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1038\/44565","volume":"401","author":"DD Lee","year":"1999","unstructured":"Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788","journal-title":"Nature"},{"key":"1629_CR35","unstructured":"Lee DD, Seung HS (1997) Unsupervised learning by convex and conic coding. In: Advances in neural information processing systems, pp 515\u2013521"},{"key":"1629_CR36","doi-asserted-by":"crossref","unstructured":"Fu L, Chen Z, Huang S, Huang S, Wang S (2021) Multi-view learning via low-rank tensor optimization. In: Proceedings of the 2021 IEEE international conference on multimedia and expo, pp 1\u20136","DOI":"10.1109\/ICME51207.2021.9428291"},{"key":"1629_CR37","doi-asserted-by":"publisher","first-page":"1192","DOI":"10.1016\/j.future.2018.04.071","volume":"108","author":"J Chen","year":"2020","unstructured":"Chen J, Fang J, Liu W, Tang T, Yang C (2020) clmf: A fine-grained and portable alternating least squares algorithm for parallel matrix factorization. Futur Gener Comput Syst 108:1192\u20131205","journal-title":"Futur Gener Comput Syst"},{"issue":"4","key":"1629_CR38","doi-asserted-by":"publisher","first-page":"1956","DOI":"10.1137\/080738970","volume":"20","author":"J-F Cai","year":"2010","unstructured":"Cai J-F, Cand\u00e8s EJ, Shen Z (2010) A singular value thresholding algorithm for matrix completion. SIAM J Optim 20(4):1956\u20131982","journal-title":"SIAM J Optim"},{"key":"1629_CR39","unstructured":"Wright J, Ganesh A, Rao SR, Peng Y, Ma Y (2009) Robust principal component analysis: Exact recovery of corrupted low-rank matrices via convex optimization. In: Proceedings of the 23rd annual conference on neural information processing systems 2009, pp 2080\u20132088"},{"issue":"9","key":"1629_CR40","doi-asserted-by":"publisher","first-page":"2066","DOI":"10.1109\/TPAMI.2017.2748590","volume":"40","author":"F Shang","year":"2018","unstructured":"Shang F, Cheng J, Liu Y, Luo Z, Lin Z (2018) Bilinear factor matrix norm minimization for robust PCA: algorithms and applications. IEEE Trans Pattern Anal Mach Intell 40(9):2066\u20132080","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"1629_CR41","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/S0169-7439(96)00044-5","volume":"37","author":"P Paatero","year":"1997","unstructured":"Paatero P (1997) Least squares formulation of robust non-negative factor analysis. Chemom Intell Lab Syst 37(1):23\u201335","journal-title":"Chemom Intell Lab Syst"},{"key":"1629_CR42","doi-asserted-by":"crossref","unstructured":"Kivinen J, Warmuth MK (1995) Additive versus exponentiated gradient updates for linear prediction. In: ACM Press the 27th annual ACM symposium, pp 209\u2013218","DOI":"10.1145\/225058.225121"},{"key":"1629_CR43","doi-asserted-by":"crossref","unstructured":"Benesty J, Chen J, Huang Y, Cohen I (2009) Pearson correlation coefficient. In: Noise reduction in speech processing, pp 1\u20134","DOI":"10.1007\/978-3-642-00296-0_5"},{"key":"1629_CR44","doi-asserted-by":"crossref","unstructured":"Koren Y (2008) Factorization meets the neighborhood: A multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, pp 426\u2013434","DOI":"10.1145\/1401890.1401944"},{"key":"1629_CR45","unstructured":"Mnih A, Salakhutdinov RR (2008) Probabilistic matrix factorization. In: Advances in neural information processing systems, pp 1257\u20131264"},{"issue":"2","key":"1629_CR46","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1145\/1345448.1345465","volume":"9","author":"RM Bell","year":"2007","unstructured":"Bell RM, Koren Y (2007) Lessons from the netflix prize challenge. ACM SIGKDD Explorations Newsl 9(2):75\u201379","journal-title":"ACM SIGKDD Explorations Newsl"},{"key":"1629_CR47","doi-asserted-by":"crossref","unstructured":"Ning X, Karypis G (2011) Slim: Sparse linear methods for top-n recommender systems. In: 2011 11th IEEE international conference on data mining, pp 497\u2013506","DOI":"10.1109\/ICDM.2011.134"},{"key":"1629_CR48","doi-asserted-by":"crossref","unstructured":"Kabbur S, Ning X, Karypis G (2013) Fism: Factored item similarity models for top-n recommender systems. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, pp 659\u2013667","DOI":"10.1145\/2487575.2487589"},{"key":"1629_CR49","first-page":"3","volume":"5","author":"D Saad","year":"1998","unstructured":"Saad D (1998) Online algorithms and stochastic approximations, Online. Learning 5:3\u20136","journal-title":"Learning"},{"key":"1629_CR50","unstructured":"Lee J, Kim S, Lebanon G, Singer Y (2013) Local low-rank matrix approximation. In: Proceedings of the 30th international conference on machine learning, vol\u00a028, pp 82\u201390"},{"key":"1629_CR51","doi-asserted-by":"crossref","unstructured":"Wand MP, Jones MC (1995) Kernel smoothing","DOI":"10.1007\/978-1-4899-4493-1"},{"key":"1629_CR52","doi-asserted-by":"crossref","unstructured":"Bian J, Gao B, Liu T-Y (2014) Knowledge-powered deep learning for word embedding. In: Proceedings of the joint European conference on machine learning and knowledge discovery in databases, pp 132\u2013148","DOI":"10.1007\/978-3-662-44848-9_9"},{"key":"1629_CR53","doi-asserted-by":"crossref","unstructured":"Shin B, Yang H, Choi JD (2019) The pupil has become the master: Teacher-student model-based word embedding distillation with ensemble learning. In: Proceedings of the 28th international joint conference on artificial intelligence, pp 3439\u20133445","DOI":"10.24963\/ijcai.2019\/477"},{"key":"1629_CR54","doi-asserted-by":"crossref","unstructured":"Zhou T, Sedoc J, Rodu J (2019) Getting in shape: Word embedding subspaces. In: Proceedings of the 28th international joint conference on artificial intelligence, pp 5478\u20135484","DOI":"10.24963\/ijcai.2019\/761"},{"key":"1629_CR55","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"},{"key":"1629_CR56","doi-asserted-by":"crossref","unstructured":"Liang D, Altosaar J, Charlin L, Blei DM (2016) Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence. In: Proceedings of the 10th ACM conference on recommender systems, pp 59\u201366","DOI":"10.1145\/2959100.2959182"},{"issue":"1","key":"1629_CR57","first-page":"22","volume":"16","author":"KW Church","year":"1990","unstructured":"Church KW, Hanks P (1990) Word association norms, mutual information, and lexicography. Comput Linguist 16(1):22\u201329","journal-title":"Comput Linguist"},{"key":"1629_CR58","unstructured":"Levy O, Goldberg Y (2014) Neural word embedding as implicit matrix factorization. In: Advances in neural information processing systems, pp 2177\u20132185"},{"key":"1629_CR59","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.neucom.2013.09.055","volume":"139","author":"C Liou","year":"2014","unstructured":"Liou C, Cheng W, Liou J, Liou D (2014) Autoencoder for words. Neurocomputing 139:84\u201396","journal-title":"Neurocomputing"},{"key":"1629_CR60","unstructured":"Ap SC, Lauly S, Larochelle H, Khapra M, Ravindran B, Raykar VC, Saha A (2014) An autoencoder approach to learning bilingual word representations. In: Advances in neural information processing systems, pp 1853\u20131861"},{"key":"1629_CR61","unstructured":"Socher R, Huang EH, Pennin J, Manning CD, Ng AY (2011) Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In: Advances in neural information processing systems, pp 801\u2013809"},{"key":"1629_CR62","first-page":"1","volume":"8690","author":"J Zhang","year":"2014","unstructured":"Zhang J, Shan S, Kan M, Chen X (2014) Coarse-to-fine auto-encoder networks (cfan) for real-time face alignment, in. Eur Conf Comput Vis 8690:1\u201316","journal-title":"Eur Conf Comput Vis"},{"key":"1629_CR63","first-page":"835","volume":"9911","author":"J Walker","year":"2016","unstructured":"Walker J, Doersch C, Gupta A, Hebert M (2016) An uncertain future: forecasting from static images using variational autoencoders, in. Eur Conf Comput Vis 9911:835\u2013851","journal-title":"Eur Conf Comput Vis"},{"key":"1629_CR64","doi-asserted-by":"crossref","unstructured":"Tewari A, Zollhofer M, Kim H, Garrido P, Bernard F, Perez P, Theobalt C (2017) Mofa: Model-based deep convolutional face autoencoder for unsupervised monocular reconstruction. In: Proceedings of the IEEE international conference on computer vision, pp 3715\u20133724","DOI":"10.1109\/ICCV.2017.401"},{"key":"1629_CR65","doi-asserted-by":"crossref","unstructured":"Sedhain S, Menon AK, Sanner S, Xie L (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":"1629_CR66","doi-asserted-by":"crossref","unstructured":"Wu Y, DuBois C, Zheng AX, Ester M (2016) Collaborative denoising auto-encoders for top-n recommender systems, in: Proceedings of the 9th ACM international conference on Web Search and Data Mining, pp 153\u2013162","DOI":"10.1145\/2835776.2835837"},{"issue":"3","key":"1629_CR67","doi-asserted-by":"publisher","first-page":"143301","DOI":"10.1007\/s11704-019-8123-3","volume":"14","author":"Y Pan","year":"2020","unstructured":"Pan Y, He F, Yu H (2020) A correlative denoising autoencoder to model social influence for top-n recommender system. Front Comp Sci 14(3):143301","journal-title":"Front Comp Sci"},{"key":"1629_CR68","doi-asserted-by":"crossref","unstructured":"Wang H, Shi X, Yeung D-Y (2015) Relational stacked denoising autoencoder for tag recommendation. In: Proceedings of the 29th AAAI conference on artificial intelligence, pp 3052\u20133058","DOI":"10.1609\/aaai.v29i1.9548"},{"key":"1629_CR69","doi-asserted-by":"crossref","unstructured":"Ishii T, Komiyama H, Shinozaki T, Horiuchi Y, Kuroiwa S (2013) Reverberant speech recognition based on denoising autoencoder. In: Interspeech, pp 3512\u20133516","DOI":"10.21437\/Interspeech.2013-267"},{"key":"1629_CR70","doi-asserted-by":"crossref","unstructured":"Vincent P, Larochelle H, Bengio Y, Manzagol P-A (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on machine learning, pp 1096\u20131103","DOI":"10.1145\/1390156.1390294"},{"issue":"Jul","key":"1629_CR71","first-page":"2121","volume":"12","author":"J Duchi","year":"2011","unstructured":"Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12(Jul):2121\u20132159","journal-title":"J Mach Learn Res"},{"key":"1629_CR72","doi-asserted-by":"crossref","unstructured":"Xue H, Dai X, Zhang J, Huang S, Chen J (2017) Deep matrix factorization models for recommender systems. In: Proceedings of the 26th international joint conference on artificial intelligence, pp 3203\u20133209","DOI":"10.24963\/ijcai.2017\/447"},{"key":"1629_CR73","doi-asserted-by":"crossref","unstructured":"Zheng L, Lu C-T, Jiang F, Zhang J, Yu PS (2018) Spectral collaborative filtering. In: Proceedings of the 12th ACM conference on recommender systems, pp 311\u2013319","DOI":"10.1145\/3240323.3240343"},{"key":"1629_CR74","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th international conference on learning representations"},{"key":"1629_CR75","unstructured":"Berg Rvd, Kipf TN, Welling M (2017) Graph convolutional matrix completion, arXiv preprint arXiv:1706.02263"},{"key":"1629_CR76","unstructured":"Monti F, Bronstein MM, Bresson X (2017) Geometric matrix completion with recurrent multi-graph neural networks. In: Advances in neural information processing systems, pp 3697\u20133707"},{"key":"1629_CR77","doi-asserted-by":"crossref","unstructured":"Wang X, He X, Wang M, Feng F, Chua T-S (2019) Neural graph collaborative filtering. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, pp 165\u2013174","DOI":"10.1145\/3331184.3331267"},{"issue":"2","key":"1629_CR78","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1080\/00401706.1993.10485033","volume":"35","author":"LE Frank","year":"1993","unstructured":"Frank LE, Friedman JH (1993) A statistical view of some chemometrics regression tools. Technometrics 35(2):109\u2013135","journal-title":"Technometrics"},{"issue":"3","key":"1629_CR79","doi-asserted-by":"publisher","first-page":"722","DOI":"10.1016\/j.ijforecast.2012.05.001","volume":"28","author":"JH Friedman","year":"2012","unstructured":"Friedman JH (2012) Fast sparse regression and classification. Int J Forecast 28(3):722\u2013738","journal-title":"Int J Forecast"},{"issue":"7","key":"1629_CR80","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1109\/83.392335","volume":"4","author":"D Geman","year":"1995","unstructured":"Geman D, Yang C (1995) Nonlinear image recovery with half-quadratic regularization. IEEE Trans Image Process 4(7):932\u2013946","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"1629_CR81","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1109\/TMI.2008.927346","volume":"28","author":"JD Trzasko","year":"2009","unstructured":"Trzasko JD, Manduca A (2009) Highly undersampled magnetic resonance image reconstruction via homotopic $$\\ell _{0}$$ -minimization. IEEE Trans Med Imaging 28(1):106\u2013121","journal-title":"IEEE Trans Med Imaging"},{"key":"1629_CR82","doi-asserted-by":"crossref","unstructured":"Gao C, Wang N, Yu QR, Zhang Z (2011) A feasible nonconvex relaxation approach to feature selection. In: Proceedings of the 25th AAAI conference on artificial intelligence, pp 356\u2013361","DOI":"10.1609\/aaai.v25i1.7921"},{"key":"1629_CR83","unstructured":"Border K (2001) The supergradient of a concave function, http:\/\/www.hss.caltech.edu\/-kcb\/Notes\/Supergrad.pdf"},{"key":"1629_CR84","doi-asserted-by":"crossref","unstructured":"Lu C, Tang J, Yan S, Lin Z (2014) Generalized nonconvex nonsmooth low-rank minimization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4130\u20134137","DOI":"10.1109\/CVPR.2014.526"},{"issue":"10","key":"1629_CR85","doi-asserted-by":"publisher","first-page":"2916","DOI":"10.1109\/TNNLS.2019.2900572","volume":"30","author":"H Zhang","year":"2019","unstructured":"Zhang H, Gong C, Qian J, Zhang B, Xu C, Yang J (2019) Efficient recovery of low-rank matrix via double nonconvex nonsmooth rank minimization. IEEE Trans Neural Netw Learn Syst 30(10):2916\u20132925","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"5","key":"1629_CR86","doi-asserted-by":"publisher","first-page":"1722","DOI":"10.1109\/TCYB.2018.2811764","volume":"49","author":"H Zhang","year":"2018","unstructured":"Zhang H, Yang J, Shang F, Gong C, Zhang Z (2018) Lrr for subspace segmentation via tractable schatten-$$ p $$ norm minimization and factorization. IEEE Trans Cybern 49(5):1722\u20131734","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"1629_CR87","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.jvcir.2012.10.006","volume":"24","author":"W Cao","year":"2013","unstructured":"Cao W, Sun J, Xu Z (2013) Fast image deconvolution using closed-form thresholding formulas of lq (q = 1\/2, 2\/3) regularization. J Vis Commun Image Represent 24(1):31\u201341","journal-title":"J Vis Commun Image Represent"},{"issue":"9","key":"1629_CR88","doi-asserted-by":"publisher","first-page":"2168","DOI":"10.1109\/TNNLS.2016.2573644","volume":"28","author":"L Luo","year":"2016","unstructured":"Luo L, Yang J, Qian J, Tai Y, Lu G-F (2016) Robust image regression based on the extended matrix variate power exponential distribution of dependent noise. IEEE Trans Neural Netw Learn Syst 28(9):2168\u20132182","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1629_CR89","doi-asserted-by":"crossref","unstructured":"Xu C, Lin Z, Zha H (2017) A unified convex surrogate for the schatten-p norm. In: Proceedings of the thirty-First AAAI conference on artificial intelligence, pp 926\u2013932","DOI":"10.1609\/aaai.v31i1.10646"},{"key":"1629_CR90","first-page":"1329","volume":"17","author":"N Srebro","year":"2004","unstructured":"Srebro N, Rennie JDM, Jaakkola TS (2004) Maximum-margin matrix factorization. Adv Neural Inf Process Syst 17:1329\u20131336","journal-title":"Adv Neural Inf Process Syst"},{"key":"1629_CR91","doi-asserted-by":"crossref","unstructured":"Shang F, Liu Y, Cheng J (2016) Scalable algorithms for tractable schatten quasi-norm minimization. In: Proceedings of the 30th AAAI conference on artificial intelligence, pp 2016\u20132022","DOI":"10.1609\/aaai.v30i1.10266"},{"key":"1629_CR92","unstructured":"Shang F, Liu Y, Cheng J (2016) Tractable and scalable schatten quasi-norm approximations for rank minimization. In: Proceedings of the 19th international conference on artificial intelligence and statistics, vol\u00a051, pp 620\u2013629"},{"issue":"1\u20132","key":"1629_CR93","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/s10107-013-0701-9","volume":"146","author":"J Bolte","year":"2014","unstructured":"Bolte J, Sabach S, Teboulle M (2014) Proximal alternating linearized minimization for nonconvex and nonsmooth problems. Math Program 146(1\u20132):459\u2013494","journal-title":"Math Program"},{"issue":"3","key":"1629_CR94","doi-asserted-by":"publisher","first-page":"1758","DOI":"10.1137\/120887795","volume":"6","author":"Y Xu","year":"2013","unstructured":"Xu Y, Yin W (2013) A block coordinate descent method for regularized multiconvex optimization with applications to nonnegative tensor factorization and completion. SIAM J Imag Sci 6(3):1758\u20131789","journal-title":"SIAM J Imag Sci"},{"key":"1629_CR95","doi-asserted-by":"publisher","first-page":"3132","DOI":"10.1109\/TIP.2019.2957925","volume":"29","author":"H Zhang","year":"2020","unstructured":"Zhang H, Qian J, Zhang B, Yang J, Gong C, Wei Y (2020) Low-rank matrix recovery via modified schatten-$$p$$ norm minimization with convergence guarantees. IEEE Trans Image Process 29:3132\u20133142","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"1629_CR96","doi-asserted-by":"publisher","first-page":"79","DOI":"10.3354\/cr030079","volume":"30","author":"CJ Willmott","year":"2005","unstructured":"Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance. Climate Res 30(1):79\u201382","journal-title":"Climate Res"},{"issue":"4","key":"1629_CR97","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1145\/582415.582418","volume":"20","author":"K J\u00e4rvelin","year":"2002","unstructured":"J\u00e4rvelin K, Kek\u00e4l\u00e4inen J (2002) Cumulated gain-based evaluation of ir techniques. ACM Trans Inf Syst 20(4):422\u2013446","journal-title":"ACM Trans Inf Syst"},{"issue":"1","key":"1629_CR98","first-page":"37","volume":"2","author":"DM Powers","year":"2011","unstructured":"Powers DM (2011) Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. J Mach Learn Technol 2(1):37\u201363","journal-title":"J Mach Learn Technol"},{"key":"1629_CR99","doi-asserted-by":"crossref","unstructured":"Baltrunas L, Ludwig B, Ricci F (2011) Matrix factorization techniques for context aware recommendation. In: Proceedings of the ACM conference on recommender systems, pp 301\u2013304","DOI":"10.1145\/2043932.2043988"},{"issue":"2","key":"1629_CR100","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1007\/s10618-015-0417-y","volume":"30","author":"B Hidasi","year":"2016","unstructured":"Hidasi B, Tikk D (2016) General factorization framework for context-aware recommendations. Data Min Knowl Disc 30(2):342\u2013371","journal-title":"Data Min Knowl Disc"},{"key":"1629_CR101","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.knosys.2016.04.020","volume":"104","author":"M Unger","year":"2016","unstructured":"Unger M, Bar A, Shapira B, Rokach L (2016) Towards latent context-aware recommendation systems. Knowl Based Syst 104:165\u2013178","journal-title":"Knowl Based Syst"},{"key":"1629_CR102","doi-asserted-by":"crossref","unstructured":"Rendle S, Freudenthaler C, Schmidt-Thieme L (2010) Factorizing personalized Markov chains for next-basket recommendation. In: Proceedings of the 19th international conference on World Wide Web, pp 811\u2013820","DOI":"10.1145\/1772690.1772773"},{"issue":"3","key":"1629_CR103","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1007\/s10115-013-0682-2","volume":"41","author":"H Yu","year":"2014","unstructured":"Yu H, Hsieh C, Si S, Dhillon IS (2014) Parallel matrix factorization for recommender systems. Knowl Inf Syst 41(3):793\u2013819","journal-title":"Knowl Inf Syst"},{"key":"1629_CR104","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.knosys.2018.01.003","volume":"145","author":"H Wu","year":"2018","unstructured":"Wu H, Zhang Z, Yue K, Zhang B, He J, Sun L (2018) Dual-regularized matrix factorization with deep neural networks for recommender systems. Knowl Based Syst 145:46\u201358","journal-title":"Knowl Based Syst"},{"key":"1629_CR105","doi-asserted-by":"crossref","unstructured":"Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv 52(1):5:1\u20135:38","DOI":"10.1145\/3285029"},{"key":"1629_CR106","doi-asserted-by":"crossref","unstructured":"Dacrema MF, Cremonesi P, Jannach D (2019) Are we really making much progress? A worrying analysis of recent neural recommendation approaches. In: Proceedings of the 13th ACM conference on recommender systems, pp 101\u2013109","DOI":"10.1145\/3298689.3347058"},{"key":"1629_CR107","doi-asserted-by":"publisher","unstructured":"Wang S, Chen Z, Du S, Lin Z (2021) Learning deep sparse regularizers with applications to multi-view clustering and semi-supervised classification. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2021.3082632","DOI":"10.1109\/TPAMI.2021.3082632"},{"key":"1629_CR108","unstructured":"Xie X, Wu J, Liu G, Zhong Z, Lin Z (2019) Differentiable linearized ADMM. In: Proceedings of the 26th international conference on machine learning, pp 6902\u20136911"},{"key":"1629_CR109","unstructured":"Yang Y, Sun J, Li H, Z. (2016) Xu, Deep admm-net for compressive sensing MRI. In: Advances in neural information processing systems, pp 10\u201318"},{"key":"1629_CR110","unstructured":"Gregor K, LeCun Y (2010) Learning fast approximations of sparse coding. In: Proceedings of the twenty-seventh international conference on machine learning, pp. 399\u2013406"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-021-01629-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-021-01629-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-021-01629-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T06:25:47Z","timestamp":1676096747000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-021-01629-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1]]},"references-count":110,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["1629"],"URL":"https:\/\/doi.org\/10.1007\/s10115-021-01629-6","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1]]},"assertion":[{"value":"1 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 November 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}