{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T07:45:12Z","timestamp":1767771912787,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031208904"},{"type":"electronic","value":"9783031208911"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20891-1_40","type":"book-chapter","created":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T00:03:02Z","timestamp":1667779382000},"page":"564-578","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Towards Robust Recommender Systems via\u00a0Triple Cooperative Defense"],"prefix":"10.1007","author":[{"given":"Qingyang","family":"Wang","sequence":"first","affiliation":[]},{"given":"Defu","family":"Lian","sequence":"additional","affiliation":[]},{"given":"Chenwang","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,7]]},"reference":[{"key":"40_CR1","unstructured":"Athalye, A., Carlini, N., Wagner, D.: Obfuscated gradients give a false sense of security: circumventing defenses to adversarial examples. In: ICML, pp. 274\u2013283. PMLR (2018)"},{"key":"40_CR2","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.: Recommender systems survey. Knowl.-Based Syst. 46, 109\u2013132 (2013)","journal-title":"Knowl.-Based Syst."},{"key":"40_CR3","doi-asserted-by":"crossref","unstructured":"Burke, R., Mobasher, B., Williams, C., Bhaumik, R.: Classification features for attack detection in collaborative recommender systems. In: KDD, pp. 542\u2013547 (2006)","DOI":"10.1145\/1150402.1150465"},{"issue":"5\u20136","key":"40_CR4","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1007\/s11280-012-0164-6","volume":"16","author":"J Cao","year":"2013","unstructured":"Cao, J., Wu, Z., Mao, B., Zhang, Y.: Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system. WWW 16(5\u20136), 729\u2013748 (2013). https:\/\/doi.org\/10.1007\/s11280-012-0164-6","journal-title":"WWW"},{"issue":"3","key":"40_CR5","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1509\/jmkr.43.3.345","volume":"43","author":"JA Chevalier","year":"2006","unstructured":"Chevalier, J.A., Mayzlin, D.: The effect of word of mouth on sales: Online book reviews. J. Mark. Res. 43(3), 345\u2013354 (2006)","journal-title":"J. Mark. Res."},{"key":"40_CR6","doi-asserted-by":"crossref","unstructured":"Christakopoulou, K., Banerjee, A.: Adversarial attacks on an oblivious recommender. In: RecSys, pp. 322\u2013330 (2019)","DOI":"10.1145\/3298689.3347031"},{"issue":"2","key":"40_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439729","volume":"54","author":"Y Deldjoo","year":"2021","unstructured":"Deldjoo, Y., Noia, T.D., Merra, F.A.: A survey on adversarial recommender systems: from attack\/defense strategies to generative adversarial networks. CSUR 54(2), 1\u201338 (2021)","journal-title":"CSUR"},{"issue":"3","key":"40_CR8","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1109\/TMM.2018.2887018","volume":"21","author":"Y Du","year":"2018","unstructured":"Du, Y., Fang, M., Yi, J., Xu, C., Cheng, J., Tao, D.: Enhancing the robustness of neural collaborative filtering systems under malicious attacks. IEEE Trans. Multimedia 21(3), 555\u2013565 (2018)","journal-title":"IEEE Trans. Multimedia"},{"key":"40_CR9","doi-asserted-by":"crossref","unstructured":"Fan, W., et al.: Attacking black-box recommendations via copying cross-domain user profiles. In: ICDE, pp. 1583\u20131594. IEEE (2021)","DOI":"10.1109\/ICDE51399.2021.00140"},{"key":"40_CR10","doi-asserted-by":"crossref","unstructured":"Fang, M., Gong, N.Z., Liu, J.: Influence function based data poisoning attacks to top-n recommender systems. In: Proceedings of the Web Conference 2020, pp. 3019\u20133025 (2020)","DOI":"10.1145\/3366423.3380072"},{"key":"40_CR11","doi-asserted-by":"crossref","unstructured":"Fang, M., Yang, G., Gong, N.Z., Liu, J.: Poisoning attacks to graph-based recommender systems. In: ACSAC, pp. 381\u2013392 (2018)","DOI":"10.1145\/3274694.3274706"},{"key":"40_CR12","doi-asserted-by":"crossref","unstructured":"Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: DeepFM: a factorization-machine based neural network for CTR prediction. arXiv (2017)","DOI":"10.24963\/ijcai.2017\/239"},{"key":"40_CR13","doi-asserted-by":"crossref","unstructured":"He, X., He, Z., Du, X., Chua, T.S.: Adversarial personalized ranking for recommendation. In: SIGIR, pp. 355\u2013364 (2018)","DOI":"10.1145\/3209978.3209981"},{"key":"40_CR14","doi-asserted-by":"crossref","unstructured":"He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: WWW, pp. 173\u2013182 (2017)","DOI":"10.1145\/3038912.3052569"},{"key":"40_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2021.100439","volume":"43","author":"Y Himeur","year":"2022","unstructured":"Himeur, Y., et al.: Blockchain-based recommender systems: applications, challenges and future opportunities. Comput. Sci. Rev. 43, 100439 (2022)","journal-title":"Comput. Sci. Rev."},{"key":"40_CR16","unstructured":"Jin, B., et al.: Sampling-decomposable generative adversarial recommender. In: Advances in Neural Information Processing Systems, vol. 33, pp. 22629-22639 (2020)"},{"key":"40_CR17","doi-asserted-by":"crossref","unstructured":"Lam, S.K., Riedl, J.: Shilling recommender systems for fun and profit. In: WWW, pp. 393\u2013402 (2004)","DOI":"10.1145\/988672.988726"},{"key":"40_CR18","first-page":"1885","volume":"29","author":"B Li","year":"2016","unstructured":"Li, B., Wang, Y., Singh, A., Vorobeychik, Y.: Data poisoning attacks on factorization-based collaborative filtering. NIPS 29, 1885\u20131893 (2016)","journal-title":"NIPS"},{"key":"40_CR19","doi-asserted-by":"crossref","unstructured":"Li, R., Wu, X., Wang, W.: Adversarial learning to compare: Self-attentive prospective customer recommendation in location based social networks. In: WSDM, pp. 349\u2013357 (2020)","DOI":"10.1145\/3336191.3371841"},{"key":"40_CR20","doi-asserted-by":"crossref","unstructured":"Lian, D., Wu, Y., Ge, Y., Xie, X., Chen, E.: Geography-aware sequential location recommendation. In: Proceedings of KDD 2020, pp. 2009\u20132019 (2020)","DOI":"10.1145\/3394486.3403252"},{"key":"40_CR21","doi-asserted-by":"crossref","unstructured":"Lin, C., Chen, S., Li, H., Xiao, Y., Li, L., Yang, Q.: Attacking recommender systems with augmented user profiles. In: CIKM, pp. 855\u2013864 (2020)","DOI":"10.1145\/3340531.3411884"},{"key":"40_CR22","first-page":"156","volume":"56","author":"H Liu","year":"2014","unstructured":"Liu, H., Hu, Z., Mian, A., Tian, H., Zhu, X.: A new user similarity model to improve the accuracy of collaborative filtering. KBS 56, 156\u2013166 (2014)","journal-title":"KBS"},{"key":"40_CR23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3385896","volume":"1","author":"GR Machado","year":"2021","unstructured":"Machado, G.R., Silva, E., Goldschmidt, R.R.: Adversarial machine learning in image classification: a survey toward the defender\u2019s perspective. CSUR 1, 1\u201338 (2021)","journal-title":"CSUR"},{"key":"40_CR24","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. arXiv (2017)"},{"key":"40_CR25","doi-asserted-by":"crossref","unstructured":"Mobasher, B., Burke, R., Bhaumik, R., Williams, C.: Toward trustworthy recommender systems: an analysis of attack models and algorithm robustness. TOIT 7(4), 23-es (2007)","DOI":"10.1145\/1278366.1278372"},{"key":"40_CR26","doi-asserted-by":"crossref","unstructured":"Oh, S., Kumar, S.: Robustness of deep recommendation systems to untargeted interaction perturbations. arXiv (2022)","DOI":"10.1145\/3511808.3557425"},{"key":"40_CR27","doi-asserted-by":"crossref","unstructured":"Ovaisi, Z., Heinecke, S., Li, J., Zhang, Y., Zheleva, E., Xiong, C.: RGRecSys: a toolkit for robustness evaluation of recommender systems. arXiv (2022)","DOI":"10.1145\/3488560.3502192"},{"key":"40_CR28","doi-asserted-by":"crossref","unstructured":"Park, D.H., Chang, Y.: Adversarial sampling and training for semi-supervised information retrieval. In: The World Wide Web Conference, pp. 1443\u20131453 (2019)","DOI":"10.1145\/3308558.3313416"},{"issue":"1","key":"40_CR29","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.: Shilling attacks against collaborative recommender systems: a review. Artif. Intell. Rev. 53(1), 291\u2013319 (2020). https:\/\/doi.org\/10.1007\/s10462-018-9655-x","journal-title":"Artif. Intell. Rev."},{"key":"40_CR30","doi-asserted-by":"crossref","unstructured":"Song, J., et al.: PoisonRec: an adaptive data poisoning framework for attacking black-box recommender systems. In: ICDE, pp. 157\u2013168. IEEE (2020)","DOI":"10.1109\/ICDE48307.2020.00021"},{"key":"40_CR31","doi-asserted-by":"crossref","unstructured":"Tang, J., Wen, H., Wang, K.: Revisiting adversarially learned injection attacks against recommender systems. In: RecSys, pp. 318\u2013327 (2020)","DOI":"10.1145\/3383313.3412243"},{"issue":"5","key":"40_CR32","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1109\/TKDE.2019.2893638","volume":"32","author":"J Tang","year":"2019","unstructured":"Tang, J., Du, X., He, X., Yuan, F., Tian, Q., Chua, T.S.: Adversarial training towards robust multimedia recommender system. IEEE Trans. Knowl. Data Eng. 32(5), 855\u2013867 (2019)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"40_CR33","doi-asserted-by":"crossref","unstructured":"Wu, C., Lian, D., Ge, Y., Zhu, Z., Chen, E.: Triple adversarial learning for influence based poisoning attack in recommender systems. In: Proceedings of KDD 2021, pp. 1830\u20131840 (2021)","DOI":"10.1145\/3447548.3467335"},{"key":"40_CR34","doi-asserted-by":"crossref","unstructured":"Wu, C., Lian, D., Ge, Y., Zhu, Z., Chen, E., Yuan, S.: Fight fire with fire: towards robust recommender systems via adversarial poisoning training. In: SIGIR, pp. 1074\u20131083 (2021)","DOI":"10.1145\/3404835.3462914"},{"issue":"7","key":"40_CR35","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1360\/972012-1712","volume":"59","author":"Z Wu","year":"2014","unstructured":"Wu, Z., Wang, Y., Cao, J.: A survey on shilling attack models and detection techniques for recommender systems. Chin. Sci. Bull. 59(7), 551\u2013560 (2014)","journal-title":"Chin. Sci. Bull."},{"key":"40_CR36","doi-asserted-by":"crossref","unstructured":"Yang, G., Gong, N.Z., Cai, Y.: Fake co-visitation injection attacks to recommender systems. In: NDSS (2017)","DOI":"10.14722\/ndss.2017.23020"},{"key":"40_CR37","doi-asserted-by":"crossref","unstructured":"Yuan, F., Yao, L., Benatallah, B.: Adversarial collaborative neural network for robust recommendation. In: SIGIR, pp. 1065\u20131068 (2019)","DOI":"10.1145\/3331184.3331321"},{"key":"40_CR38","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.knosys.2018.02.032","volume":"148","author":"F Zhang","year":"2018","unstructured":"Zhang, F., Zhang, Z., Zhang, P., Wang, S.: UD-HMM: an unsupervised method for shilling attack detection based on hidden Markov model and hierarchical clustering. Knowl.-Based Syst. 148, 146\u2013166 (2018)","journal-title":"Knowl.-Based Syst."},{"key":"40_CR39","unstructured":"Zhang, J., et al.: Attacks which do not kill training make adversarial learning stronger. In: ICML, pp. 11278\u201311287. PMLR (2020)"},{"key":"40_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Kulkarni, S.R.: Detection of shilling attacks in recommender systems via spectral clustering. In: FUSION, pp. 1\u20138. IEEE (2014)","DOI":"10.1109\/MLSP.2013.6661953"}],"container-title":["Lecture Notes in Computer Science","Web Information Systems Engineering \u2013 WISE 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20891-1_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T00:44:04Z","timestamp":1667781844000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20891-1_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031208904","9783031208911"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20891-1_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"7 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WISE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Web Information Systems Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Biarritz","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wise2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/wise2022.sigappfr.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"94","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"13","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The proceedings include 3 demo papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}