{"id":"https://openalex.org/W4392736350","doi":"https://doi.org/10.48550/arxiv.2403.06737","title":"Post-Training Attribute Unlearning in Recommender Systems","display_name":"Post-Training Attribute Unlearning in Recommender Systems","publication_year":2024,"publication_date":"2024-03-11","ids":{"openalex":"https://openalex.org/W4392736350","doi":"https://doi.org/10.48550/arxiv.2403.06737"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2403.06737","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2403.06737","pdf_url":"https://arxiv.org/pdf/2403.06737","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2403.06737","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028791879","display_name":"Chaochao Chen","orcid":"https://orcid.org/0000-0003-1419-964X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Chen, Chaochao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041086326","display_name":"Yizhao Zhang","orcid":"https://orcid.org/0009-0008-0241-8706"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yizhao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101535331","display_name":"Yuyuan Li","orcid":"https://orcid.org/0000-0003-4896-2885"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yuyuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100722013","display_name":"Jun Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Qi, Lianyong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qi, Lianyong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Xu, Xiaolong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Xiaolong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zheng, Xiaolin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Xiaolin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5069353502","display_name":"Jianwei Yin","orcid":"https://orcid.org/0000-0003-4703-7348"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Jianwei","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5028791879"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9865999817848206,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9865999817848206,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.9783999919891357,"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/recommender-system","display_name":"Recommender system","score":0.797174334526062},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.6665012240409851},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6013535261154175},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4199647903442383},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.2921227812767029},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10380089282989502}],"concepts":[{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.797174334526062},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6665012240409851},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6013535261154175},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4199647903442383},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2921227812767029},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10380089282989502},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2403.06737","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2403.06737","pdf_url":"https://arxiv.org/pdf/2403.06737","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2403.06737","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2403.06737","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2403.06737","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2403.06737","pdf_url":"https://arxiv.org/pdf/2403.06737","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G59283274","display_name":null,"funder_award_id":"72192823","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/F4320326701","display_name":"Recruitment Program of Global Experts","ror":null},{"id":"https://openalex.org/F4320336605","display_name":"National Ten Thousand Talent Program","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4392736350.pdf","grobid_xml":"https://content.openalex.org/works/W4392736350.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W4390273403","https://openalex.org/W4386781444","https://openalex.org/W2150182025","https://openalex.org/W3092950680","https://openalex.org/W3197542405","https://openalex.org/W2056712470","https://openalex.org/W3125580266","https://openalex.org/W4317039510","https://openalex.org/W4238861846"],"abstract_inverted_index":{"With":[0],"the":[1,33,60,99,102,109,193],"growing":[2],"privacy":[3],"concerns":[4],"in":[5,112],"recommender":[6,113],"systems,":[7,114],"recommendation":[8,103,169],"unlearning":[9,24,56,93],"is":[10,105,124,157],"getting":[11],"increasing":[12],"attention.":[13],"Existing":[14],"studies":[15],"predominantly":[16],"use":[17,176],"training":[18,100],"data,":[19],"i.e.,":[20],"model":[21,34,104],"inputs,":[22],"as":[23,50,55],"target.":[25,57],"However,":[26],"attackers":[27],"can":[28,94],"extract":[29],"private":[30],"information":[31,49],"from":[32,138],"even":[35],"if":[36],"it":[37,54],"has":[38],"not":[39],"been":[40],"explicitly":[41],"encountered":[42],"during":[43],"training.":[44],"We":[45,140,175],"name":[46],"this":[47,75,143],"unseen":[48],"\\textit{attribute}":[51],"and":[52],"treat":[53],"To":[58,107],"protect":[59],"sensitive":[61],"attribute":[62,135,148],"of":[63,85,101,195],"users,":[64],"Attribute":[65,89],"Unlearning":[66,90],"(AU)":[67],"aims":[68],"to":[69,133,145,172,181],"make":[70,134],"target":[71],"attributes":[72],"indistinguishable.":[73],"In":[74],"paper,":[76],"we":[77,115,128,161],"focus":[78],"on":[79,188],"a":[80,117,130,163],"strict":[81],"but":[82],"practical":[83],"setting":[84],"AU,":[86],"namely":[87],"Post-Training":[88],"(PoT-AU),":[91],"where":[92,127,160],"only":[95],"be":[96],"performed":[97],"after":[98],"completed.":[106],"address":[108],"PoT-AU":[110],"problem":[111],"propose":[116],"two-component":[118],"loss":[119],"function.":[120],"The":[121,154],"first":[122],"component":[123,156],"distinguishability":[125],"loss,":[126,159],"design":[129],"distribution-based":[131],"measurement":[132,144,165],"labels":[136],"indistinguishable":[137],"attackers.":[139],"further":[141],"extend":[142],"handle":[146],"multi-class":[147],"cases":[149],"with":[150],"efficient":[151],"computational":[152],"overhead.":[153],"second":[155],"regularization":[158],"explore":[162],"function-space":[164],"that":[166],"effectively":[167],"maintains":[168],"performance":[170],"compared":[171],"parameter-space":[173],"regularization.":[174],"stochastic":[177],"gradient":[178],"descent":[179],"algorithm":[180],"optimize":[182],"our":[183,196],"proposed":[184,197],"loss.":[185],"Extensive":[186],"experiments":[187],"four":[189],"real-world":[190],"datasets":[191],"demonstrate":[192],"effectiveness":[194],"methods.":[198]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
