{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:51:19Z","timestamp":1742914279112,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031466731"},{"type":"electronic","value":"9783031466748"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-46674-8_26","type":"book-chapter","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:02:29Z","timestamp":1699102949000},"page":"371-386","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Poisoning Attack Based on\u00a0Variant Generative Adversarial Networks in\u00a0Recommender Systems"],"prefix":"10.1007","author":[{"given":"Hongyun","family":"Cai","sequence":"first","affiliation":[]},{"given":"Shiyun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Meiling","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ao","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"key":"26_CR1","unstructured":"Li, B., Wang, Y., Singh, A., Vorobeychik, Y.: Data poisoning attacks on factorization-based collaborative filtering. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain, December 5\u201310, 2016, pp. 1893\u20131901. Curran Associates Inc., Red Hook, NY, USA (2016)"},{"issue":"5","key":"26_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.103031","volume":"59","author":"S Wang","year":"2022","unstructured":"Wang, S., Zhang, P., Wang, H., Yu, H., Zhang, F.: Detecting shilling groups in online recommender systems based on graph convolutional network. Inf. Process. Manage. 59(5), 103031 (2022)","journal-title":"Inf. Process. Manage."},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Wilson, D.C., Seminario, C.E.: When power users attack: assessing impacts in collaborative recommender systems. In: Seventh ACM Conference on Recommender Systems, pp. 427\u2013430. ACM, New York, NY, USA (2013)","DOI":"10.1145\/2507157.2507220"},{"issue":"1","key":"26_CR4","volume":"1","author":"A Aggarwal","year":"2021","unstructured":"Aggarwal, A., Mittal, M., Battineni, G.: Generative adversarial network: an overview of theory and applications. Int. J. Inf. Manage. Data Insights 1(1), 100004 (2021)","journal-title":"Int. J. Inf. Manage. Data Insights"},{"key":"26_CR5","doi-asserted-by":"publisher","first-page":"82:1","DOI":"10.1145\/3512352","volume":"13","author":"Z Wang","year":"2022","unstructured":"Wang, Z., Gao, M., Li, J., Zhang, J., Zhong, J.: Gray-box shilling attack: an adversarial learning approach. ACM Trans. Intell. Syst. Technol. 13, 82:1-82:21 (2022)","journal-title":"ACM Trans. Intell. Syst. Technol."},{"issue":"2","key":"26_CR6","doi-asserted-by":"publisher","first-page":"35:1","DOI":"10.1145\/3439729","volume":"54","author":"Y Deldjoo","year":"2022","unstructured":"Deldjoo, Y., Noia, T.D., Merra, F.A.: A survey on adversarial recommender systems: from attack\/defense strategies to generative adversarial networks. ACM Comput. Surv. 54(2), 35:1-35:38 (2022)","journal-title":"ACM Comput. Surv."},{"key":"26_CR7","doi-asserted-by":"crossref","unstructured":"Fan, W., et al.: Attacking black-box recommendations via copying cross-domain user profiles. In: 37th IEEE International Conference on Data Engineering, pp. 1583\u20131594. IEEE (2021)","DOI":"10.1109\/ICDE51399.2021.00140"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Song, J., Li, Z., Hu, Z., Wu, Y., Li, Z., Li, J., Gao, J.: PoisonREC: an adaptive data poisoning framework for attacking black-box recommender systems. In: 36th IEEE International Conference on Data Engineering, pp. 157\u2013168. IEEE (2020)","DOI":"10.1109\/ICDE48307.2020.00021"},{"key":"26_CR9","doi-asserted-by":"crossref","unstructured":"Tang, J., Wen, H., Wang, K.: Revisiting adversarially learned injection attacks against recommender systems. In: Proceedings of the 14th ACM Conference on Recommender Systems, pp. 318\u2013327. Association for Computing Machinery, New York, NY, USA (2020)","DOI":"10.1145\/3383313.3412243"},{"key":"26_CR10","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: The 29th ACM International Conference on Information and Knowledge Management, pp. 855\u2013864. Association for Computing Machinery, New York, NY, USA (2020)","DOI":"10.1145\/3340531.3411884"},{"key":"26_CR11","doi-asserted-by":"publisher","first-page":"4788","DOI":"10.1109\/TIFS.2021.3117078","volume":"16","author":"X Zhang","year":"2021","unstructured":"Zhang, X., Chen, J., Zhang, R., Wang, C., Liu, L.: Attacking recommender systems with plausible profile. IEEE Trans. Inf. Forensics Secur. 16, 4788\u20134800 (2021)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"26_CR12","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1016\/j.ins.2021.07.041","volume":"578","author":"F Wu","year":"2021","unstructured":"Wu, F., Gao, M., Yu, J., Wang, Z., Liu, K., Wang, X.: Ready for emerging threats to recommender systems? A graph convolution-based generative shilling attack. Inf. Sci. 578, 683\u2013701 (2021)","journal-title":"Inf. Sci."},{"key":"26_CR13","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: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1830\u20131840. Association for Computing Machinery, New York, NY, USA (2021)","DOI":"10.1145\/3447548.3467335"},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Lin, C., Chen, S., Zeng, M., Zhang, S., Gao, M., Li, H.: Shilling black-box recommender systems by learning to generate fake user profiles. CoRR abs\/2206.11433 (2022)","DOI":"10.1109\/TNNLS.2022.3183210"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Fang, M., Yang, G., Gong, N.Z., Liu, J.: Poisoning attacks to graph-based recommender systems. In: Proceedings of the 34th Annual Computer Security Applications Conference, pp. 381\u2013392. Association for Computing Machinery, New York, NY, USA (2018)","DOI":"10.1145\/3274694.3274706"},{"issue":"11","key":"26_CR16","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Feng, Y., et al.: Deep session interest network for click-through rate prediction. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pp. 2301\u20132307. AAAI Press, Palo Alto, California, USA (2019)","DOI":"10.24963\/ijcai.2019\/319"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Seminario, C.E., Wilson, D.C.: Attacking item-based recommender systems with power items. In: Eighth ACM Conference on Recommender Systems, pp. 57\u201364. ACM, New York, NY, USA (2014)","DOI":"10.1145\/2645710.2645722"},{"key":"26_CR19","doi-asserted-by":"crossref","unstructured":"Chae, D., Kang, J., Kim, S., Lee, J.: CFGAN: A generic collaborative filtering framework based on generative adversarial networks. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 137\u2013146. Association for Computing Machinery, New York, NY, USA (2018)","DOI":"10.1145\/3269206.3271743"},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Fang, M., Gong, N.Z., Liu, J.: Influence function based data poisoning attacks to top-n recommender systems. In: WWW \u201920: The Web Conference 2020, pp. 3019\u20133025. Association for Computing Machinery, New York, NY, USA (2020)","DOI":"10.1145\/3366423.3380072"},{"key":"26_CR21","unstructured":"Zhang, Y., Tan, Y., Zhang, M., Liu, Y., Tat-Seng, C., Ma, S.: Catch the black sheep: Unified framework for shilling attack detection based on fraudulent action propagation. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, pp. 2408\u20132414. AAAI Press (2015)"},{"key":"26_CR22","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."}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46674-8_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:18:44Z","timestamp":1699103924000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46674-8_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031466731","9783031466748"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46674-8_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2023.uqcloud.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes. Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"503","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":"216","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":"0","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":"43% - 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":"2.97","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":"3.77","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}