{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T20:25:17Z","timestamp":1774124717356,"version":"3.50.1"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T00:00:00Z","timestamp":1721347200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T00:00:00Z","timestamp":1721347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LZ22F010005"],"award-info":[{"award-number":["LZ22F010005"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s13042-024-02288-z","type":"journal-article","created":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T07:02:41Z","timestamp":1721372561000},"page":"771-787","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["SPERM: sequential pairwise embedding recommendation with MI-FGSM"],"prefix":"10.1007","volume":"16","author":[{"given":"Agyemang","family":"Paul","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxuan","family":"Wan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boyu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhefu","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,19]]},"reference":[{"issue":"3","key":"2288_CR1","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/MIC.2017.72","volume":"21","author":"B Smith","year":"2017","unstructured":"Smith B, Linden G (2017) Two decades of recommender systems at Amazon. IEEE Internet Comput 21(3):12\u201318","journal-title":"IEEE Internet Comput"},{"issue":"2","key":"2288_CR2","doi-asserted-by":"publisher","first-page":"51","DOI":"10.4018\/ijdet.2014040103","volume":"12","author":"RY Toledo","year":"2014","unstructured":"Toledo RY, Mota YC (2014) An e-learning collaborative filtering approach to suggest problems to solve in programming online judges. Int J Distance Educ Technol 12(2):51\u201365","journal-title":"Int J Distance Educ Technol"},{"issue":"1","key":"2288_CR3","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1111\/j.1467-8640.2012.00427.x","volume":"29","author":"J Lu","year":"2013","unstructured":"Lu J, Shambour Q, Xu Y, Lin Q, Zhang G (2013) A web-based personalized business partner recommendation system using fuzzy semantic techniques. Comput Intell 29(1):37\u201369","journal-title":"Comput Intell"},{"key":"2288_CR4","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/978-1-4899-7637-6_2","volume-title":"Recommender systems handbook","author":"X Ning","year":"2015","unstructured":"Ning X, Desrosiers C, Karypis G (2015) A comprehensive survey of neighborhood-based recommendation methods. Recommender systems handbook. Springer, Boston, pp 37\u201376"},{"issue":"10","key":"2288_CR5","doi-asserted-by":"publisher","first-page":"4268","DOI":"10.1109\/TCYB.2019.2900159","volume":"50","author":"H Zhang","year":"2020","unstructured":"Zhang H, Sun Y, Zhao M, Chow TWS, Wu QMJ (2020) Bridging user interest to item content for recommender systems: an optimization model. IEEE Trans Cybern 50(10):4268\u20134280","journal-title":"IEEE Trans Cybern"},{"key":"2288_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-0-387-85820-3","volume-title":"Recommender systems handbook","author":"F Ricci","year":"2011","unstructured":"Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems. Recommender systems handbook. Springer, Boston, pp 1\u201335"},{"key":"2288_CR7","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.knosys.2013.03.012","volume":"46","author":"J Bobadilla","year":"2013","unstructured":"Bobadilla J, Ortega F, Hernando A, Guti\u00e9rrez A (2013) Recommender systems survey. Knowl Syst 46:109\u2013132","journal-title":"Knowl Syst"},{"issue":"8","key":"2288_CR8","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren Y, Bell RM, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30\u201337","journal-title":"Computer"},{"key":"2288_CR9","unstructured":"Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) BPR: Bayesian personalized ranking from implicit feedback. In: UAI, pp 452\u2013461"},{"key":"2288_CR10","doi-asserted-by":"crossref","unstructured":"He R, McAuley J (2016) VBPR: visual Bayesian personalized ranking from implicit feedback. In: AAAI, pp 144\u2013150","DOI":"10.1609\/aaai.v30i1.9973"},{"key":"2288_CR11","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1007\/s00778-021-00651-y","volume":"30","author":"W Yu","year":"2019","unstructured":"Yu W, He X, Pei J, Chen X, Xiong L, Liu J, Qin Z (2019) Visually aware recommendation with aesthetic features. VLDB J 30:495\u2013513","journal-title":"VLDB J"},{"key":"2288_CR12","doi-asserted-by":"crossref","unstructured":"He R, McAuley J (2016) Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: WWW, pp 507\u2013517","DOI":"10.1145\/2872427.2883037"},{"key":"2288_CR13","doi-asserted-by":"crossref","unstructured":"He R, Fang C, Wang Z, McAuley J (2016) Vista: a visually, socially, and temporally-aware model for artistic recommendation. In: Proceedings of the tenth ACM conference on recommender systems, pp 309-316","DOI":"10.1145\/2959100.2959152"},{"issue":"10","key":"2288_CR14","doi-asserted-by":"publisher","first-page":"14573","DOI":"10.1007\/s11042-022-12259-7","volume":"81","author":"A Paul","year":"2022","unstructured":"Paul A, Wu Z, Liu K, Gong S (2022) Personalized recommendation: from clothing to academic. Multimedia Tools Appl 81(10):14573\u201314588","journal-title":"Multimedia Tools Appl"},{"key":"2288_CR15","unstructured":"Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow IJ, Fergus R (2014) Intriguing properties of neural networks. In: ICLR 2014"},{"key":"2288_CR16","unstructured":"Goodfellow IJ, Shlens J, Szegedy C (2015) Explaining and harnessing adversarial examples. In: ICLR 2015"},{"key":"2288_CR17","unstructured":"Madry A, Makelov A, Schmidt L, Tsipras D, Vladu A (2018) Towards deep learning models resistant to adversarial attacks. In: ICLR 2018"},{"key":"2288_CR18","doi-asserted-by":"crossref","unstructured":"Dong Y, Liao F, Pang T, Su H, Zhu J, Hu X, Li J (2018) Boosting adversarial attacks with momentum. In: Proceedings of IEEE CVPR\u201918, pp 9185\u20139193","DOI":"10.1109\/CVPR.2018.00957"},{"key":"2288_CR19","doi-asserted-by":"crossref","unstructured":"Carlini N, Wagner DA (2017) Towards evaluating the robustness of neural networks. In: SP, pp 39\u201357","DOI":"10.1109\/SP.2017.49"},{"key":"2288_CR20","unstructured":"Shafahi A, Najibi M, Ghiasi A, Xu Z, Dickerson JP, Studer C, Davis LS, Taylor G, Goldstein T (2019) Adversarial training for free!. In: NeurIPS, pp 3353\u20133364"},{"issue":"7","key":"2288_CR21","first-page":"38","volume":"14","author":"GE Hinton","year":"2015","unstructured":"Hinton GE, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. Comput Sci 14(7):38\u201339","journal-title":"Comput Sci"},{"key":"2288_CR22","doi-asserted-by":"crossref","unstructured":"Papernot N, Mcdaniel P, Wu X, Jha S, Swami A (2016) Distillation as a defense to adversarial perturbations against deep neural networks. In: IEEE symposium on security and privacy, pp 582\u2013597","DOI":"10.1109\/SP.2016.41"},{"issue":"8","key":"2288_CR23","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1109\/TPAMI.2018.2858821","volume":"41","author":"T Miyato","year":"2018","unstructured":"Miyato T, Maeda S, Koyama M, Ishii S (2018) Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Trans Pattern Anal Mach Intell 41(8):1979\u20131993","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6","key":"2288_CR24","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick RB, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2288_CR25","doi-asserted-by":"crossref","unstructured":"Yuan Z, Lu Y, Wang Z, Xue Y (2014) Droid-Sec: deep learning android malware detection. In: SIGCOMM, pp 371\u2013372","DOI":"10.1145\/2619239.2631434"},{"issue":"6","key":"2288_CR26","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","volume":"29","author":"GE Hinton","year":"2012","unstructured":"Hinton GE, Deng L, Yu D, Dahl GE, Mohamed A, Jaitly N, Senior AW, Vanhoucke V, Nguyen P, Sainath TN, Kingsbury B (2012) Deep neural networks for acoustic modeling speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29(6):82\u201397","journal-title":"IEEE Signal Process Mag"},{"key":"2288_CR27","doi-asserted-by":"crossref","unstructured":"Entezari N, Al-Sayouri SA, Darvishzadeh A, Papalexakis EE (2020) All you need is low (rank): defending against adversarial attacks on graphs. In: WSDM, pp 169\u2013177","DOI":"10.1145\/3336191.3371789"},{"issue":"5","key":"2288_CR28","first-page":"855","volume":"32","author":"J Tang","year":"2019","unstructured":"Tang J, Du X, He X, Yuan F, Tian Q, Chua T (2019) Adversarial training towards robust multimedia recommender system. TKDE 32(5):855\u2013867","journal-title":"TKDE"},{"key":"2288_CR29","doi-asserted-by":"publisher","first-page":"3499","DOI":"10.1007\/s10489-021-02355-w","volume":"52","author":"A Paul","year":"2021","unstructured":"Paul A, Wu Z, Liu K, Gong S (2021) Robust multi-objective visual Bayesian personalized ranking for multimedia recommendation. Appl Intell 52:3499\u20133510","journal-title":"Appl Intell"},{"key":"2288_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13042-023-01868-9","volume":"14","author":"A Paul","year":"2023","unstructured":"Paul A, Wu Z, Luo K, Ma Y, Fang L (2023) Robust multimedia recommender system based on dynamic collaborative filtering and directed adversarial learning. Int J Mach Learn Cybern 14:1\u201315","journal-title":"Int J Mach Learn Cybern"},{"key":"2288_CR31","doi-asserted-by":"crossref","unstructured":"Noia TD, Malitesta D, Merra FA (2020) TAaMR: targeted adversarial attack against multimedia recommender systems. In: DSN-DSML, pp 1\u20138","DOI":"10.1109\/DSN-W50199.2020.00011"},{"key":"2288_CR32","doi-asserted-by":"crossref","unstructured":"He X, He Z, Du X, Chua T (2018) Adversarial personalized ranking for recommendation. In: SIGIR, pp 355\u2013364","DOI":"10.1145\/3209978.3209981"},{"key":"2288_CR33","doi-asserted-by":"crossref","unstructured":"Deldjoo Y, Noia TD, Merra FA (2020) Adversarial machine learning in recommender systems (AML-RecSys). In: Thirteenth ACM international conference on web search and data mining, pp 869\u2013872","DOI":"10.1145\/3336191.3371877"},{"key":"2288_CR34","unstructured":"Xu Y, Chen L, Xie F, Hu W, Zhu J, Chen C, Zheng Z (2020) Directional adversarial training for recommender systems. In: European conference on artificial intelligence, pp 553\u2013560"},{"key":"2288_CR35","doi-asserted-by":"crossref","unstructured":"Rendle S, Freudenthaler C, Schmidt-Thieme L (2010) Factorizing personalized Markov chains for nextbasket recommendation. In: Proceedings of the 19th international conference on world wide web, pp 811\u2013820","DOI":"10.1145\/1772690.1772773"},{"key":"2288_CR36","doi-asserted-by":"crossref","unstructured":"Niu W, Caverlee J, Lu H (2018) Neural personalized ranking for image recommendation. In: WSDM, pp 423\u2013431","DOI":"10.1145\/3159652.3159728"},{"key":"2288_CR37","doi-asserted-by":"crossref","unstructured":"Kordan SB, Kotov A (2018) Deep neural architecture for multi-modal retrieval based on joint embedding space for text and images. In: WSDM, pp 28\u201336","DOI":"10.1145\/3159652.3159735"},{"key":"2288_CR38","doi-asserted-by":"crossref","unstructured":"Wu Z, Liu Y, Zhang Q, Wu K, Zhang M, Ma S (2019) The influence of image search intents on user behavior and satisfaction. In: WSDM, pp 645\u2013653","DOI":"10.1145\/3289600.3291013"},{"key":"2288_CR39","doi-asserted-by":"crossref","unstructured":"Grauman K (2020) Computer vision for fashion: from individual recommendations to world-wide trends. In: WSDM, pp 3","DOI":"10.1145\/3336191.3372192"},{"key":"2288_CR40","doi-asserted-by":"crossref","unstructured":"Kang W, Fang C, Wang Z, McAuley J (2017) Visually-aware fashion recommendation and design with generative image models. In: ICDM, pp 207\u2013216","DOI":"10.1109\/ICDM.2017.30"},{"key":"2288_CR41","doi-asserted-by":"crossref","unstructured":"Chu W, Tsai Y (2017) A hybrid recommendation system considering visual information for predicting favorite restaurants. In: WWW, pp 1313\u20131331","DOI":"10.1007\/s11280-017-0437-1"},{"key":"2288_CR42","doi-asserted-by":"crossref","unstructured":"Wang S, Wang Y, Tang J, Shu K, Ranganath S, Liu H (2017) What your images reveal: exploiting visual contents for point-of-interest recommendation. In: WWW, pp 391\u2013400","DOI":"10.1145\/3038912.3052638"},{"key":"2288_CR43","doi-asserted-by":"crossref","unstructured":"Zhang Y, Caverlee J (2019) Instagrammers, fashionistas, and me: recurrent fashion recommendation with implicit visual influence. In: CIKM, pp 1583\u20131592","DOI":"10.1145\/3357384.3358042"},{"key":"2288_CR44","doi-asserted-by":"crossref","unstructured":"Mobasher B, Dai H, Luo T, Nakagawa M (2002) Using sequential and non-sequential patterns in predictive web usage mining tasks. In: Proceedings of the IEEE thirteenth international conference on data mining, pp 669\u2013672","DOI":"10.1109\/ICDM.2002.1184025"},{"key":"2288_CR45","doi-asserted-by":"crossref","unstructured":"Wang S, Zhou X, Wang Z, Zhang M (2012) Please spread: recommending tweets for retweeting with implicit feedback. In: Proceedings of the workshop on data-driven user behavioral modeling and mining from social media, pp 19\u201322","DOI":"10.1145\/2390131.2390140"},{"key":"2288_CR46","doi-asserted-by":"crossref","unstructured":"Biggio B, Corona I, Maiorca D, Nelson B, Srndic N, Laskov P, Giacinto G, Roli F (2013) Evasion attacks against machine learning at test time. In: ECML-PKDD, pp 387\u2013402","DOI":"10.1007\/978-3-642-40994-3_25"},{"key":"2288_CR47","doi-asserted-by":"crossref","unstructured":"Kurakin A, Goodfellow IJ, Bengio S (2017) Adversarial examples the physical world. In: ICLR 2017","DOI":"10.1201\/9781351251389-8"},{"key":"2288_CR48","doi-asserted-by":"crossref","unstructured":"Carlini N, Wagner DA (2017) Adversarial examples are not easily detected: bypassing ten detection methods. In: AISec@CCS, pp 3\u201314","DOI":"10.1145\/3128572.3140444"},{"key":"2288_CR49","unstructured":"Carlini N, Wagner DA (2016) Defensive distillation is not robust to adversarial examples. CoRR 2016"},{"key":"2288_CR50","doi-asserted-by":"crossref","unstructured":"Lam SK, Riedl J (2004) Shilling recommender systems for fun and profit. In: WWW, pp 393\u2013402","DOI":"10.1145\/988672.988726"},{"key":"2288_CR51","unstructured":"Bhaumik R, Williams C, Mobasher B, Burke R (2006) Securing collaborative filtering against malicious attacks through anomaly detection. In: ITWP 2006"},{"issue":"4","key":"2288_CR52","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1145\/1031114.1031116","volume":"4","author":"MP O\u2019Mahony","year":"2004","unstructured":"O\u2019Mahony MP, Hurley NJ, Kushmerick N, Silvestre GC (2004) Collaborative recommendation: a robustness analysis. ACM Trans Internet Technol 4(4):344\u2013377","journal-title":"ACM Trans Internet Technol"},{"key":"2288_CR53","doi-asserted-by":"crossref","unstructured":"Tang X, Li Y, Sun Y, Yao H, Mitra P, Wang S (2020) Transferring robustness for graph neural network against poisoning attacks. In: WSDM, pp 600\u2013608","DOI":"10.1145\/3336191.3371851"},{"key":"2288_CR54","doi-asserted-by":"crossref","unstructured":"He R, Packer C, McAuley J (2016) Learning compatibility across categories for heterogeneous item recommendation. In: Proceedings of the sixteenth IEEE international conference on data mining, pp 937\u2013942","DOI":"10.1109\/ICDM.2016.0116"},{"issue":"1","key":"2288_CR55","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1162\/neco.1991.3.1.79","volume":"3","author":"RA Jacobs","year":"1991","unstructured":"Jacobs RA, Jordan MI, Nowlan SJ, Hinton GE (1991) Adaptive mixtures of local experts. Neural Comput 3(1):79\u201387","journal-title":"Neural Comput"},{"key":"2288_CR56","first-page":"453","volume":"2002","author":"G Shani","year":"2002","unstructured":"Shani G, Brafman RI, Heckerman D (2002) An MDP-based recommender system. UAI 2002:453\u2013460","journal-title":"UAI"},{"key":"2288_CR57","first-page":"2121","volume":"12","author":"JC Duchi","year":"2010","unstructured":"Duchi JC, Hazan E, Singer Y (2010) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121\u20132159","journal-title":"J Mach Learn Res"},{"key":"2288_CR58","doi-asserted-by":"crossref","unstructured":"McAuley J, Targett C, Shi J, Hengel AV (2015) Image-based recommendations on styles and substitutes. In: Proceedings of the thirty eighth international ACM SIGIR conference on research and development in information retrieval, pp 43\u201352","DOI":"10.1145\/2766462.2767755"},{"key":"2288_CR59","doi-asserted-by":"crossref","unstructured":"Kang W, McAuley J (2018) Self-attentive sequential recommendation. In: 2018 IEEE international conference on data mining (ICDM), pp 197\u2013206","DOI":"10.1109\/ICDM.2018.00035"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02288-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02288-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02288-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T10:41:25Z","timestamp":1738665685000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02288-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,19]]},"references-count":59,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["2288"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02288-z","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,19]]},"assertion":[{"value":"10 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2024","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 known conflict of interest or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}