{"id":"https://openalex.org/W4286750695","doi":"https://doi.org/10.1145/3534678.3539359","title":"Knowledge-enhanced Black-box Attacks for Recommendations","display_name":"Knowledge-enhanced Black-box Attacks for Recommendations","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4286750695","doi":"https://doi.org/10.1145/3534678.3539359"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539359","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539359","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2207.10307","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101642745","display_name":"Jingfan Chen","orcid":"https://orcid.org/0000-0002-7559-6924"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jingfan Chen","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043696243","display_name":"Wenqi Fan","orcid":"https://orcid.org/0000-0002-4049-1233"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Wenqi Fan","raw_affiliation_strings":["The Hong Kong Polytechnic University, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University, Hong Kong, China","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086985082","display_name":"Guanghui Zhu","orcid":"https://orcid.org/0000-0002-5069-5950"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanghui Zhu","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084614702","display_name":"Xiangyu Zhao","orcid":"https://orcid.org/0000-0001-7760-0512"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Xiangyu Zhao","raw_affiliation_strings":["City University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115598059","display_name":"Chunfeng Yuan","orcid":"https://orcid.org/0000-0002-8746-8137"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunfeng Yuan","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100404176","display_name":"Qing Li","orcid":"https://orcid.org/0000-0003-3370-471X"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Qing Li","raw_affiliation_strings":["The Hong Kong Polytechnic University, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University, Hong Kong, China","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007538828","display_name":"Yihua Huang","orcid":"https://orcid.org/0000-0003-1806-0936"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yihua Huang","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5101642745"],"corresponding_institution_ids":["https://openalex.org/I881766915"],"apc_list":null,"apc_paid":null,"fwci":4.1957,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.95436718,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"108","last_page":"117"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9909999966621399,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8417490720748901},{"id":"https://openalex.org/keywords/black-box","display_name":"Black box","score":0.7763844728469849},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7759650945663452},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.5488000512123108},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5294287800788879},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.47865235805511475},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.468144953250885},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.45013749599456787},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.41053587198257446},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3786410391330719},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.35182178020477295},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.333248496055603},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1449490487575531}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8417490720748901},{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.7763844728469849},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7759650945663452},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.5488000512123108},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5294287800788879},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.47865235805511475},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.468144953250885},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.45013749599456787},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.41053587198257446},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3786410391330719},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.35182178020477295},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.333248496055603},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1449490487575531},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3534678.3539359","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539359","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2207.10307","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2207.10307","pdf_url":"https://arxiv.org/pdf/2207.10307","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2207.10307","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2207.10307","pdf_url":"https://arxiv.org/pdf/2207.10307","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.6499999761581421,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G3076789159","display_name":null,"funder_award_id":"BK20210181","funder_id":"https://openalex.org/F4320322769","funder_display_name":"Natural Science Foundation of Jiangsu Province"},{"id":"https://openalex.org/G3860240491","display_name":null,"funder_award_id":"62102335;62102177;U1811461","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2157331557","https://openalex.org/W2257979135","https://openalex.org/W2475334473","https://openalex.org/W2790002550","https://openalex.org/W2792839191","https://openalex.org/W2892160417","https://openalex.org/W2945827670","https://openalex.org/W2963842088","https://openalex.org/W2964938963","https://openalex.org/W2972646741","https://openalex.org/W2972774416","https://openalex.org/W2979068607","https://openalex.org/W3025378468","https://openalex.org/W3048511744","https://openalex.org/W3088444111","https://openalex.org/W3098087397","https://openalex.org/W3100278010","https://openalex.org/W3101291735","https://openalex.org/W3104774544","https://openalex.org/W3104956872","https://openalex.org/W3119520312","https://openalex.org/W3136856676","https://openalex.org/W3165439069","https://openalex.org/W3170976035","https://openalex.org/W3187174779","https://openalex.org/W4284704639","https://openalex.org/W6733162650"],"related_works":["https://openalex.org/W2950183588","https://openalex.org/W3080754722","https://openalex.org/W3093978547","https://openalex.org/W2953536436","https://openalex.org/W3009622996","https://openalex.org/W3203790781","https://openalex.org/W4313346231","https://openalex.org/W2738001131","https://openalex.org/W4285785480","https://openalex.org/W2997056298"],"abstract_inverted_index":{"Recent":[0],"studies":[1],"have":[2,32],"shown":[3],"that":[4,29],"deep":[5,162],"neural":[6],"networks-based":[7],"recommender":[8,38],"systems":[9,81],"are":[10],"vulnerable":[11],"to":[12,40,55,65,103,137,156,177],"adversarial":[13,67,184],"attacks,":[14],"where":[15,73],"attackers":[16],"can":[17,128],"inject":[18],"carefully":[19],"crafted":[20],"fake":[21,30,91,142,179],"user":[22,92,143,180],"profiles":[23,93,181],"(i.e.,":[24,123],"a":[25,36,49,115,149],"set":[26,50],"of":[27,51,79,141,196],"items":[28],"users":[31],"interacted":[33],"with)":[34],"into":[35,173],"target":[37,52,80,104],"system":[39],"achieve":[41],"malicious":[42],"purposes,":[43],"such":[44],"as":[45],"promote":[46],"or":[47],"demote":[48],"items.":[53],"Due":[54],"the":[56,70,74,139,194,197,202],"security":[57],"and":[58,76,132],"privacy":[59],"concerns,":[60],"it":[61],"is":[62,97,170],"more":[63],"practical":[64],"perform":[66],"attacks":[68],"under":[69,94,201],"black-box":[71,95,152,185,203],"setting,":[72],"architecture/parameters":[75],"training":[77],"data":[78],"cannot":[82],"be":[83,129],"easily":[84],"accessed":[85],"by":[86,118],"attackers.":[87],"However,":[88],"generating":[89],"high-quality":[90],"setting":[96],"rather":[98],"challenging":[99],"with":[100],"limited":[101],"resources":[102],"systems.":[105],"To":[106],"address":[107],"this":[108,111],"challenge,":[109],"in":[110,166],"work,":[112],"we":[113,147],"introduce":[114],"novel":[116],"strategy":[117],"leveraging":[119],"items'":[120,124],"attribute":[121],"information":[122],"knowledge":[125,136,150,168],"graph),":[126],"which":[127,167],"publicly":[130],"accessible":[131],"provide":[133],"rich":[134],"auxiliary":[135],"enhance":[138],"generation":[140],"profiles.":[144],"More":[145],"specifically,":[146],"propose":[148],"graph-enhanced":[151],"attacking":[153,159,199],"framework":[154,200],"(KGAttack)":[155],"effectively":[157],"learn":[158],"policies":[160],"through":[161],"reinforcement":[163],"learning":[164],"techniques,":[165],"graph":[169],"seamlessly":[171],"integrated":[172],"hierarchical":[174],"policy":[175],"networks":[176],"generate":[178],"for":[182],"performing":[183],"attacks.":[186],"Comprehensive":[187],"experiments":[188],"on":[189],"various":[190],"real-world":[191],"datasets":[192],"demonstrate":[193],"effectiveness":[195],"proposed":[198],"setting.":[204]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":21},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-14T08:43:22.919905","created_date":"2025-10-10T00:00:00"}
