{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:12:31Z","timestamp":1766268751690},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T00:00:00Z","timestamp":1625616000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T00:00:00Z","timestamp":1625616000000},"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":["Appl Intell"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s10489-021-02355-w","type":"journal-article","created":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T02:02:33Z","timestamp":1625623353000},"page":"3499-3510","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Robust multi-objective visual bayesian personalized ranking for multimedia recommendation"],"prefix":"10.1007","volume":"52","author":[{"given":"Agyemang","family":"Paul","sequence":"first","affiliation":[]},{"given":"Zhefu","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Shufeng","family":"Gong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,7]]},"reference":[{"key":"2355_CR1","doi-asserted-by":"crossref","unstructured":"Wang M, Liu X, Wu X (2015) Visual classification by l1-hypergraph modeling [J]. IEEE Trans on Knowl and Data Eng:2564\u20132574","DOI":"10.1109\/TKDE.2015.2415497"},{"key":"2355_CR2","doi-asserted-by":"crossref","unstructured":"He R, McAuley J (2016) VBPR: Visual bayesian personalized ranking from implicit feedback [C]. In: AAAI, pp 144\u2013150","DOI":"10.1609\/aaai.v30i1.9973"},{"key":"2355_CR3","unstructured":"Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) BPR: Bayesian Personalized ranking from implicit feedback [C]. UAI:452\u2013461"},{"key":"2355_CR4","doi-asserted-by":"crossref","unstructured":"He X, Liao L, Zhang H, Nie L, Hu X, Chua T S (2017) Neural collaborative filtering [C]. In: WWW, pp 173\u2013182","DOI":"10.1145\/3038912.3052569"},{"key":"2355_CR5","doi-asserted-by":"crossref","unstructured":"Su J H, Huang W J, Philip S Y, Tseng V S (2011) Efficient relevance feedback for content-based image retrieval by mining user navigation patterns [J]. IEEE Trans Knowl Data Eng:360\u2013372","DOI":"10.1109\/TKDE.2010.124"},{"key":"2355_CR6","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition [C]. In: CVPR, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2355_CR7","doi-asserted-by":"crossref","unstructured":"Chen J, Zhang H, He X, Nie L, Liu W, Chua T S (2017) Attentive collaborative filtering: Multimedia recommendation with item and component-level attention [C]. In: SIGIR, pp 335\u2013344","DOI":"10.1145\/3077136.3080797"},{"key":"2355_CR8","doi-asserted-by":"crossref","unstructured":"Cheng Z, Chang X, Zhu L, Kanjirathinkal R C, Kankanhalli M (2019) Mmalfm: Explainable recommendation by leveraging reviews and images [J]. In: TOIS, vol 16, pp 1\u201316, vol 28","DOI":"10.1145\/3291060"},{"key":"2355_CR9","doi-asserted-by":"crossref","unstructured":"Lei C, Liu D, Li W, Zha Z J, Li H (2016) Comparative deep learning of hybrid representations for image recommendations [C]. In: CVPR, pp 2545\u20132553","DOI":"10.1109\/CVPR.2016.279"},{"key":"2355_CR10","doi-asserted-by":"crossref","unstructured":"Yu W, Zhang H, He X, Chen X, Xiong L, Qin Z (2019) Aesthetic-based clothing recommendation [C]. In: WWW, pp 649\u2013658","DOI":"10.1145\/3178876.3186146"},{"key":"2355_CR11","unstructured":"Yu W, He X, Pei J, Chen X, Xiong L, Liu J, Qin Z (2019) Visually-aware Recommendation with Aesthetic Features [J]. arXiv:1905.02009"},{"key":"2355_CR12","doi-asserted-by":"crossref","unstructured":"Huang L, Joseph A D, Nelson B, Rubinstein B I P, Tygar J D (2011) Adversarial machine learning [C]. In: ACM, pp 43\u201358","DOI":"10.1145\/2046684.2046692"},{"key":"2355_CR13","doi-asserted-by":"crossref","unstructured":"Akhtar N, Mian A S (2018) Threat of adversarial attacks on deep learning in computer vision: a survey [J]. IEEE Access 6:14410\u201314430","DOI":"10.1109\/ACCESS.2018.2807385"},{"key":"2355_CR14","doi-asserted-by":"crossref","unstructured":"Zhang W E, Sheng Q Z, Alhazmi A, Li C (2020) Adversarial attacks on Deep-Learning models in natural language processing: a survey [J]. ACM Trans Intell Syst Technol, pp 41","DOI":"10.1145\/3374217"},{"key":"2355_CR15","doi-asserted-by":"crossref","unstructured":"Deldjoo Y, Noia T D, Merra F A (2020) Adversarial Machine Learning in Recommender Systems (AML-recsys) [C]. In: ACM, pp 869\u2013872","DOI":"10.1145\/3336191.3371877"},{"key":"2355_CR16","unstructured":"Goodfellow I J, Shlens J, Szegedy C (2015) Explaining and harnessing adversarial examples [C]. In: ICLR. arXiv:1412.6572"},{"key":"2355_CR17","doi-asserted-by":"crossref","unstructured":"Kurakin A, Goodfellow I J, Bengio S (2017) Adversarial examples in the physical world [C]. In: ICLR. https:\/\/openreview.net\/forum?id=HJGU3Rodl","DOI":"10.1201\/9781351251389-8"},{"key":"2355_CR18","doi-asserted-by":"crossref","unstructured":"He X, He Z, Du X, Chua T S (2018) Adversarial personalized ranking for recommendation [C]. In: SIGIR, pp 355\u2013 364","DOI":"10.1145\/3209978.3209981"},{"key":"2355_CR19","unstructured":"Tang J, Du X, He X, Yuan F, Tian Q, Chua T S (2019) Adversarial training towards robust multimedia recommender system [J]. IEEE Trans on Knowl and Data Eng:1\u20131"},{"key":"2355_CR20","doi-asserted-by":"crossref","unstructured":"Di Noia T, Malitesta D, Merra F A (2020) TAAMR: Targeted Adversarial Attack against Multimedia Recommender Systems [C]. DSN Worksh:1\u20138","DOI":"10.1109\/DSN-W50199.2020.00011"},{"key":"2355_CR21","doi-asserted-by":"crossref","unstructured":"Ricci F, Rokach L, Shapira B, Kantor P B (2011) Recommender systems handbook \u2014\u2014 advances in collaborative filtering. [M]. Springer, pp 145\u2013186","DOI":"10.1007\/978-0-387-85820-3_5"},{"key":"2355_CR22","doi-asserted-by":"crossref","unstructured":"Hu Y, Koren Y, Volinsky C (2008) Collaborative filtering for implicit feedback datasets [C]. In: ICDM, pp 263\u2013272","DOI":"10.1109\/ICDM.2008.22"},{"key":"2355_CR23","doi-asserted-by":"crossref","unstructured":"Goldberg D, Nichols D A, Oki B M, Terry D B (1992) Using collaborative filtering to weave an information tapestry [J]. In: ACM, pp 61\u201370","DOI":"10.1145\/138859.138867"},{"key":"2355_CR24","doi-asserted-by":"crossref","unstructured":"Anelli V W, Noia T D, Sciascio E D, Ragone A, Trotta J (2019) How to make latent factors interpretable by feeding factorization machines with knowledge graphs [C]. In: ISWC, pp 38\u201356","DOI":"10.1007\/978-3-030-30793-6_3"},{"key":"2355_CR25","doi-asserted-by":"crossref","unstructured":"Chen L, Chen G, Wang F (2015) Recommender systems based on user reviews: the state of the art [J]. User Model. User-Adapt Interact, pp 99\u2013154","DOI":"10.1007\/s11257-015-9155-5"},{"key":"2355_CR26","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 [C]. In: WWW, pp 507\u2013517","DOI":"10.1145\/2872427.2883037"},{"key":"2355_CR27","doi-asserted-by":"crossref","unstructured":"Lops P, Gemmis M D, Semeraro G (2011) Content-based recommender systems: State of the art and trends [M]. Springer, pp 73\u2013105","DOI":"10.1007\/978-0-387-85820-3_3"},{"key":"2355_CR28","doi-asserted-by":"crossref","unstructured":"Wu L, Chen L, Hong R, Fu Y, Xie X, Wang M (2020) A hierarchical attention model for social contextual image recommendation [J]. IEEE Trans Knowl Data Eng 32(10):1854\u2013 1867","DOI":"10.1109\/TKDE.2019.2913394"},{"key":"2355_CR29","doi-asserted-by":"crossref","unstructured":"Yin R, Li K, Lu J, Zhang G (2019) Enhancing fashion recommendation with visual compatibility relationship [C]. In: WWW, pp 3434\u20133440","DOI":"10.1145\/3308558.3313739"},{"key":"2355_CR30","doi-asserted-by":"crossref","unstructured":"Jian M, Jia T, Yang X, Wu L, Huo L (2019) Cross-modal Collaborative Manifold Propagation for Image Recommendation [C]. In: ICMR, pp 344\u2013348","DOI":"10.1145\/3323873.3325054"},{"key":"2355_CR31","doi-asserted-by":"crossref","unstructured":"Kang W C, Fang C, Wang Z, McAuley J J (2017) Visually-Aware Fashion recommendation and design with generative image models [C]. In: ICDM, pp 207\u2013216","DOI":"10.1109\/ICDM.2017.30"},{"key":"2355_CR32","doi-asserted-by":"crossref","unstructured":"Geng X, Zhang H, Bian J, Chua T S (2015) Learning image and user features for recommendation in social networks [C]. In: ICCV, pp 4274\u20134282","DOI":"10.1109\/ICCV.2015.486"},{"key":"2355_CR33","doi-asserted-by":"crossref","unstructured":"Deldjoo Y, Elahi M, Cremonesi P, Garzotto F, Piazzolla P, Quadrana M (2016) Content-based video recommendation system based on stylistic visual features [J]. J Data Semant:99\u2013 113","DOI":"10.1007\/s13740-016-0060-9"},{"key":"2355_CR34","doi-asserted-by":"crossref","unstructured":"Donaldson J (2007) A hybrid social-acoustic recommendation system for popular music [C]. In: ACM, pp 187\u2013190","DOI":"10.1145\/1297231.1297271"},{"key":"2355_CR35","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 [C]. WWW, pp 391\u2013400","DOI":"10.1145\/3038912.3052638"},{"key":"2355_CR36","doi-asserted-by":"crossref","unstructured":"Liu Q, Wu S, Wang L (2017) Deepstyle: Learning User Preferences for Visual Recommendation [C]. In: SIGIR, pp 841\u2013844","DOI":"10.1145\/3077136.3080658"},{"key":"2355_CR37","unstructured":"Deldjoo Y, Noia T D, Merra F A (2019) Assessing the impact of a user-item collaborative attack on class of users [C]. In: ACM. http:\/\/ceur-ws.org\/Vol-2462\/paper2.pdf"},{"key":"2355_CR38","doi-asserted-by":"publisher","unstructured":"Anelli V W, Deldjoo Y, Noia T D, Sciascio E D, Merra F A (2020) Sasha: Semantic-aware shilling attacks on recommender systems exploiting knowledge graphs [C]. In: ESWC, pp 307\u2013323. https:\/\/doi.org\/10.1007\/978-3-030-49461-2_18","DOI":"10.1007\/978-3-030-49461-2_18"},{"key":"2355_CR39","doi-asserted-by":"crossref","unstructured":"Fang M, Gong N Z, Liu J (2020) Influence function based data poisoning attacks to top-n recommender systems [C]. In: WWW, pp 3019\u20133025","DOI":"10.1145\/3366423.3380072"},{"key":"2355_CR40","doi-asserted-by":"crossref","unstructured":"Fang M, Yang G, Gong N Z, Liu J (2018) Poisoning attacks to graph-based recommender systems [C]. In: ACSAC, pp 381\u2013392","DOI":"10.1145\/3274694.3274706"},{"key":"2355_CR41","doi-asserted-by":"crossref","unstructured":"Lam S K, Riedl J (2004) Shilling recommender systems for fun and profit [C]. In: WWW, pp 393\u2013402","DOI":"10.1145\/988672.988726"},{"key":"2355_CR42","doi-asserted-by":"crossref","unstructured":"O\u2019Mahony M P, Hurley N J, Kushmerick N, Silvestre G C M (2004) Collaborative recommendation: A robustness analysis [J]. ACM Trans Internet Techn:344\u2013377","DOI":"10.1145\/1031114.1031116"},{"key":"2355_CR43","doi-asserted-by":"crossref","unstructured":"Chen H, Li J (2019) Adversarial tensor factorization for context-aware recommendation [C]. RecSys:363\u2013367","DOI":"10.1145\/3298689.3346987"},{"key":"2355_CR44","doi-asserted-by":"crossref","unstructured":"Yuan F, Yao L, Benatallah B (2019) Adversarial collaborative neural network for robust recommendation [C]. In: SIGIR, pp 1065\u20131068","DOI":"10.1145\/3331184.3331321"},{"key":"2355_CR45","doi-asserted-by":"crossref","unstructured":"Du Y, Fang M, Yi J, Xu C, Cheng J, Tao D (2019) Enhancing the robustness of neural collaborative filtering systems under malicious attacks [J]. IEEE Trans. Multimedia, pp 555\u2013565","DOI":"10.1109\/TMM.2018.2887018"},{"key":"2355_CR46","doi-asserted-by":"crossref","unstructured":"Rafailidis D, Crestani F (2019) Adversarial training for review-based recommendations [C]. In: SIGIR, pp 1057\u20131060","DOI":"10.1145\/3331184.3331313"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02355-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02355-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02355-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,3]],"date-time":"2023-01-03T02:45:18Z","timestamp":1672713918000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02355-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,7]]},"references-count":46,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["2355"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02355-w","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,7]]},"assertion":[{"value":"13 March 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2021","order":2,"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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Competing interests"}}]}}