{"id":"https://openalex.org/W7140461865","doi":"https://doi.org/10.48550/arxiv.2603.23780","title":"Lightweight Fairness for LLM-Based Recommendations via Kernelized Projection and Gated Adapters","display_name":"Lightweight Fairness for LLM-Based Recommendations via Kernelized Projection and Gated Adapters","publication_year":2026,"publication_date":"2026-03-24","ids":{"openalex":"https://openalex.org/W7140461865","doi":"https://doi.org/10.48550/arxiv.2603.23780"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.23780","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23780","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.23780","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130703253","display_name":"Nan Cui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cui, Nan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130635940","display_name":"Wendy Hui Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Wendy Hui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5034350199","display_name":"Yue Ning","orcid":"https://orcid.org/0000-0002-5368-2194"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ning, Yue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.1599999964237213,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.1599999964237213,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.14429999887943268,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.14380000531673431,"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/scalability","display_name":"Scalability","score":0.6958000063896179},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.5917999744415283},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5899999737739563},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.5156000256538391},{"id":"https://openalex.org/keywords/adapter","display_name":"Adapter (computing)","score":0.38690000772476196},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.34549999237060547}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8169000148773193},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6958000063896179},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.5917999744415283},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5899999737739563},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.5156000256538391},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4853000044822693},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42480000853538513},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.38690000772476196},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37139999866485596},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.34549999237060547},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.30570000410079956},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2928999960422516},{"id":"https://openalex.org/C2777042071","wikidata":"https://www.wikidata.org/wiki/Q6509304","display_name":"Leakage (economics)","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2766000032424927},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.27410000562667847},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2696000039577484},{"id":"https://openalex.org/C2989486834","wikidata":"https://www.wikidata.org/wiki/Q3808900","display_name":"True positive rate","level":2,"score":0.26820001006126404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.23780","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23780","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.23780","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23780","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.44894739985466003,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"have":[4],"introduced":[5],"new":[6],"capabilities":[7],"to":[8],"recommender":[9,19],"systems,":[10],"enabling":[11],"dynamic,":[12],"context-aware,":[13],"and":[14,22,55],"conversational":[15],"recommendations.":[16],"However,":[17],"LLM-based":[18],"systems":[20],"inherit":[21],"may":[23],"amplify":[24],"social":[25],"biases":[26],"embedded":[27],"in":[28],"their":[29],"pre-training":[30],"data,":[31],"especially":[32],"when":[33],"demographic":[34],"cues":[35],"are":[36],"present.":[37],"Existing":[38],"fairness":[39],"solutions":[40],"either":[41],"require":[42],"extra":[43],"parameters":[44],"fine-tuning,":[45],"or":[46,83],"suffer":[47],"from":[48,87],"optimization":[49],"instability.":[50],"We":[51],"propose":[52],"a":[53,62,69,77,101],"lightweight":[54],"scalable":[56],"bias":[57],"mitigation":[58],"method":[59,121],"that":[60,80,105,119],"combines":[61],"kernelized":[63],"Iterative":[64],"Null-space":[65],"Projection":[66],"(INLP)":[67],"with":[68,90],"gated":[70],"Mixture-of-Experts":[71],"(MoE)":[72],"adapter.":[73],"Our":[74],"approach":[75],"estimates":[76],"closed-form":[78],"projection":[79],"removes":[81],"single":[82],"multiple":[84,126],"sensitive":[85],"attributes":[86],"LLM":[88],"representations":[89],"no":[91],"additional":[92],"trainable":[93],"parameters.":[94],"To":[95],"preserve":[96],"task":[97],"utility,":[98],"we":[99],"introduce":[100],"two-level":[102],"MoE":[103],"adapter":[104],"selectively":[106],"restores":[107],"useful":[108],"signals":[109],"without":[110],"reintroducing":[111],"bias.":[112],"Experiments":[113],"on":[114],"two":[115],"public":[116],"datasets":[117],"show":[118],"our":[120],"reduces":[122],"attribute":[123],"leakage":[124],"across":[125],"protected":[127],"variables":[128],"while":[129],"maintaining":[130],"competitive":[131],"recommendation":[132],"accuracy.":[133]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-27T00:00:00"}