{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T09:58:47Z","timestamp":1769162327011,"version":"3.49.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,4,30]],"date-time":"2022-04-30T00:00:00Z","timestamp":1651276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,30]],"date-time":"2022-04-30T00:00:00Z","timestamp":1651276800000},"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":[[2022,7]]},"DOI":"10.1007\/s42979-022-01131-y","type":"journal-article","created":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T13:12:06Z","timestamp":1651497126000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep Learning for Visual-Features Extraction\u00a0Based Personalized User Modeling"],"prefix":"10.1007","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4101-4578","authenticated-orcid":false,"given":"Aymen","family":"Ben Hassen","sequence":"first","affiliation":[]},{"given":"Sonia","family":"Ben Ticha","sequence":"additional","affiliation":[]},{"given":"Anja Habacha","family":"Chaibi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,30]]},"reference":[{"key":"1131_CR1","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1109\/TKDE.2005.99","volume":"6","author":"G Adomavicius","year":"2005","unstructured":"Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng. 2005;6:734\u201349.","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1131_CR2","doi-asserted-by":"crossref","unstructured":"Be\u00a0Hassen A, Ticha SB. Transfer learning to extract features for personalized user modeling. In: WEBIST, 2020;15\u201325.","DOI":"10.5220\/0010109400150025"},{"key":"1131_CR3","doi-asserted-by":"crossref","unstructured":"Bengio Y. Learning deep architectures for AI. Now Publishers Inc 2009.","DOI":"10.1561\/9781601982957"},{"key":"1131_CR4","doi-asserted-by":"crossref","unstructured":"Biadsy N, Rokach L, Shmilovici A. Transfer learning for content-based recommender systems using tree matching. In: International Conference on Availability, Reliability, and Security, pp. 387\u2013399. Springer 2013.","DOI":"10.1007\/978-3-642-40511-2_28"},{"issue":"6","key":"1131_CR5","doi-asserted-by":"publisher","first-page":"1313","DOI":"10.1007\/s11280-017-0437-1","volume":"20","author":"WT Chu","year":"2017","unstructured":"Chu WT, Tsai YL. A hybrid recommendation system considering visual information for predicting favorite restaurants. World Wide Web. 2017;20(6):1313\u201331.","journal-title":"World Wide Web"},{"key":"1131_CR6","unstructured":"Cui Q, Wu S, Liu Q, Zhong W, Wang L. Mv-rnn: a multi-view recurrent neural network for sequential recommendation. IEEE Trans Knowl Data Eng. 2018."},{"issue":"1","key":"1131_CR7","doi-asserted-by":"publisher","first-page":"41","DOI":"10.2478\/jaiscr-2018-0023","volume":"9","author":"GB de Souza","year":"2019","unstructured":"de Souza GB, da Silva Santos DF, Pires RG, Marana AN, Papa JP. Deep features extraction for robust fingerprint spoofing attack detection. J Artif Intell Soft Comput Res. 2019;9(1):41\u20139.","journal-title":"J Artif Intell Soft Comput Res"},{"issue":"5","key":"1131_CR8","doi-asserted-by":"publisher","first-page":"1164","DOI":"10.1109\/TNNLS.2016.2514368","volume":"28","author":"S Deng","year":"2016","unstructured":"Deng S, Huang L, Xu G, Wu X, Wu Z. On deep learning for trust-aware recommendations in social networks. IEEE Trans Neural Netw Learn Syst. 2016;28(5):1164\u201377.","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1131_CR9","doi-asserted-by":"crossref","unstructured":"Desrosiers C, Karypis G. A comprehensive survey of neighborhood-based recommendation methods. In: Recommender systems handbook. Springer, 2011;107\u201344.","DOI":"10.1007\/978-0-387-85820-3_4"},{"issue":"1","key":"1131_CR10","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1002\/aris.1440380105","volume":"38","author":"ST Dumais","year":"2004","unstructured":"Dumais ST. Latent semantic analysis. Ann Rev Inf Sci Technol. 2004;38(1):188\u2013230.","journal-title":"Ann Rev Inf Sci Technol"},{"key":"1131_CR11","doi-asserted-by":"crossref","unstructured":"Elkahky AM, Song Y, He X. A multi-view deep learning approach for cross domain user modeling in recommendation systems. In: Proceedings of the 24th International Conference on World Wide Web, pp. 278\u201388. International World Wide Web Conferences Steering Committee 2015.","DOI":"10.1145\/2736277.2741667"},{"key":"1131_CR12","doi-asserted-by":"crossref","unstructured":"Geng X, Zhang H, Bian J, Chua TS. Learning image and user features for recommendation in social networks. In: Proceedings of the IEEE International Conference on Computer Vision, 2015;4274\u201382.","DOI":"10.1109\/ICCV.2015.486"},{"issue":"1","key":"1131_CR13","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 (TOIS). 2004;22(1):5\u201353.","journal-title":"ACM Trans Inf Syst (TOIS)"},{"issue":"5786","key":"1131_CR14","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science. 2006;313(5786):504\u20137.","journal-title":"Science"},{"issue":"7","key":"1131_CR15","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput. 2006;18(7):1527\u201354.","journal-title":"Neural Comput"},{"key":"1131_CR16","doi-asserted-by":"crossref","unstructured":"Hongliang C, Xiaona Q. The video recommendation system based on DBN. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, pp. 1016\u201321. IEEE 2015.","DOI":"10.1109\/CIT\/IUCC\/DASC\/PICOM.2015.154"},{"key":"1131_CR17","unstructured":"IMDB: Internet movie database, 2019; https:\/\/www.imdb.com\/, accessed Jun 2019."},{"key":"1131_CR18","doi-asserted-by":"crossref","unstructured":"Karatzoglou A, Hidasi B. Deep learning for recommender systems. In: Proceedings of the eleventh ACM conference on recommender systems. ACM, 2017; pp. 396\u20137.","DOI":"10.1145\/3109859.3109933"},{"key":"1131_CR19","unstructured":"Karpathy A. et\u00a0al. Cs231n convolutional neural networks for visual recognition. Neural Netw 2016;1."},{"key":"1131_CR20","doi-asserted-by":"crossref","unstructured":"Koren Y. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2008; pp. 426\u201334.","DOI":"10.1145\/1401890.1401944"},{"key":"1131_CR21","doi-asserted-by":"crossref","unstructured":"Lei C, Liu D, Li W, Zha ZJ, Li H. Comparative deep learning of hybrid representations for image recommendations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016;pp. 2545\u201353.","DOI":"10.1109\/CVPR.2016.279"},{"key":"1131_CR22","doi-asserted-by":"crossref","unstructured":"McAuley J, Targett C, Shi Q, Van Den\u00a0Hengel 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; pp. 43\u201352.","DOI":"10.1145\/2766462.2767755"},{"key":"1131_CR23","doi-asserted-by":"crossref","unstructured":"Nguyen HT, Wistuba M, Schmidt-Thieme L. Personalized tag recommendation for images using deep transfer learning. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 2017; pp. 705\u2013720.","DOI":"10.1007\/978-3-319-71246-8_43"},{"issue":"12","key":"1131_CR24","doi-asserted-by":"publisher","first-page":"15751","DOI":"10.1007\/s11042-018-7031-0","volume":"78","author":"M Rashid","year":"2019","unstructured":"Rashid M, Khan MA, Sharif M, Raza M, Sarfraz MM, Afza F. Object detection and classification: a joint selection and fusion strategy of deep convolutional neural network and sift point features. Multimed Tools Appl. 2019;78(12):15751\u201377.","journal-title":"Multimed Tools Appl"},{"key":"1131_CR25","doi-asserted-by":"crossref","unstructured":"Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J. Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM conference on Computer supported cooperative work, 1994; pp. 175\u201386.","DOI":"10.1145\/192844.192905"},{"key":"1131_CR26","doi-asserted-by":"crossref","unstructured":"Schafer JB, Frankowski D, Herlocker J, Sen S. Collaborative filtering recommender systems. In: The adaptive web. Springer, 2007; pp. 291\u2013324.","DOI":"10.1007\/978-3-540-72079-9_9"},{"key":"1131_CR27","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015;61:85\u2013117.","journal-title":"Neural Netw"},{"issue":"4","key":"1131_CR28","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1080\/0952813X.2019.1572657","volume":"33","author":"M Sharif","year":"2019","unstructured":"Sharif M, Attique\u00a0Khan M, Rashid M, Yasmin M, Afza F, Tanik UJ. Deep CNN and geometric features-based gastrointestinal tract diseases detection and classification from wireless capsule endoscopy images. J Exp Theor Artif Intell. 2019;33(4):577\u2013599.","journal-title":"J Exp Theor Artif Intell"},{"key":"1131_CR29","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint. 2014. arXiv:1409.1556."},{"key":"1131_CR30","unstructured":"Targ S, Almeida D, Lyman K. Resnet in resnet: Generalizing residual architectures. arXiv preprint. 2016. arXiv:1603.08029."},{"key":"1131_CR31","unstructured":"Ticha SB. Hybrid personalized recommendation. Ph.D. thesis, Faculty of Sciences of Tunis 2015. https:\/\/hal.univ-lorraine.fr\/tel-01752090"},{"key":"1131_CR32","unstructured":"Ticha SB, Roussanaly A, Boyer A, Bsa\u00efes K. Feature frequency inverse user frequency for dependant attribute to enhance recommendations. In: The Third Int. Conf. on Social Eco-Informatics - SOTICS. IARIA, Lisbon, Portugal 2013."},{"key":"1131_CR33","unstructured":"TMDB: The movie database. 2019. https:\/\/www.themoviedb.org\/, accessed Jun 2019."},{"key":"1131_CR34","unstructured":"Van Den\u00a0Oord A, Dieleman S, Schrauwen B. Deep content-based music recommendation. In: Neural Information Processing Systems Conference (NIPS 2013), vol.\u00a026. Neural Information Processing Systems Foundation (NIPS) 2013."},{"key":"1131_CR35","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1016\/j.neucom.2015.08.104","volume":"184","author":"Y Wang","year":"2016","unstructured":"Wang Y, Yao H, Zhao S. Auto-encoder based dimensionality reduction. Neurocomputing. 2016;184:232\u201342.","journal-title":"Neurocomputing"},{"key":"1131_CR36","unstructured":"Wei Y, Xia W, Huang J, Ni B, Dong J, Zhao Y, Yan S. Cnn: Single-label to multi-label. arXiv preprint arXiv:1406.5726 2014."},{"key":"1131_CR37","doi-asserted-by":"crossref","unstructured":"Yu W, Zhang H, He X, Chen X, Xiong L, Qin Z. Aesthetic-based clothing recommendation. In: Proceedings of the 2018 World Wide Web Conference, pp. 649\u2013658. International World Wide Web Conferences Steering Committee 2018.","DOI":"10.1145\/3178876.3186146"},{"issue":"1","key":"1131_CR38","first-page":"5","volume":"52","author":"S Zhang","year":"2019","unstructured":"Zhang S, Yao L, Sun A, Tay Y. Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv (CSUR). 2019;52(1):5.","journal-title":"ACM Comput Surv (CSUR)"},{"key":"1131_CR39","doi-asserted-by":"crossref","unstructured":"Zhou J, Albatal R, Gurrin C. Applying visual user interest profiles for recommendation and personalisation. In: International Conference on Multimedia Modeling. Springer, 2016; pp. 361\u20136.","DOI":"10.1007\/978-3-319-27674-8_34"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-022-01131-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-022-01131-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-022-01131-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T16:37:57Z","timestamp":1656002277000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-022-01131-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,30]]},"references-count":39,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["1131"],"URL":"https:\/\/doi.org\/10.1007\/s42979-022-01131-y","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,30]]},"assertion":[{"value":"24 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no confict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"261"}}