{"id":"https://openalex.org/W7124433424","doi":"https://doi.org/10.1109/access.2026.3654561","title":"Case-Based Mixture of Experts Recommendation With Federated and Reinforcement Learning for Bias Reduction","display_name":"Case-Based Mixture of Experts Recommendation With Federated and Reinforcement Learning for Bias Reduction","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7124433424","doi":"https://doi.org/10.1109/access.2026.3654561"},"language":null,"primary_location":{"id":"doi:10.1109/access.2026.3654561","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3654561","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3654561","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100702717","display_name":"Jae Hong Park","orcid":"https://orcid.org/0000-0001-9342-9890"},"institutions":[{"id":"https://openalex.org/I89015989","display_name":"Dankook University","ror":"https://ror.org/058pdbn81","country_code":"KR","type":"education","lineage":["https://openalex.org/I89015989"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Jihyeok Park","raw_affiliation_strings":["Department of Software, Dankook University, Gyeonggi-do, Yongin-si, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Software, Dankook University, Gyeonggi-do, Yongin-si, South Korea","institution_ids":["https://openalex.org/I89015989"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019393465","display_name":"Sohyun Park","orcid":"https://orcid.org/0009-0009-8185-5740"},"institutions":[{"id":"https://openalex.org/I89015989","display_name":"Dankook University","ror":"https://ror.org/058pdbn81","country_code":"KR","type":"education","lineage":["https://openalex.org/I89015989"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sohyun Park","raw_affiliation_strings":["Department of Software Science, Dankook University, Gyeonggi-do, Yongin-si, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Software Science, Dankook University, Gyeonggi-do, Yongin-si, South Korea","institution_ids":["https://openalex.org/I89015989"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100702717"],"corresponding_institution_ids":["https://openalex.org/I89015989"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22333333,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"12139","last_page":"12150"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11519","display_name":"Digital Mental Health Interventions","score":0.3711000084877014,"subfield":{"id":"https://openalex.org/subfields/3202","display_name":"Applied Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11519","display_name":"Digital Mental Health Interventions","score":0.3711000084877014,"subfield":{"id":"https://openalex.org/subfields/3202","display_name":"Applied Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.22509999573230743,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.05139999836683273,"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/lifelog","display_name":"Lifelog","score":0.7716000080108643},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7648000121116638},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.7483000159263611},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5806000232696533},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.4749999940395355},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.46709999442100525},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.42669999599456787}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8550000190734863},{"id":"https://openalex.org/C176168674","wikidata":"https://www.wikidata.org/wiki/Q763835","display_name":"Lifelog","level":2,"score":0.7716000080108643},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7648000121116638},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.7483000159263611},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5806000232696533},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5281999707221985},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5152000188827515},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.4749999940395355},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.46709999442100525},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.42669999599456787},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.3718999922275543},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.3546000123023987},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.3336000144481659},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.3327000141143799},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.32510000467300415},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.30889999866485596},{"id":"https://openalex.org/C54290928","wikidata":"https://www.wikidata.org/wiki/Q4845080","display_name":"Wearable technology","level":3,"score":0.30070000886917114},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2662999927997589},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.26339998841285706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2026.3654561","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3654561","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3654561","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3654561","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.5150595307350159,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2049229728","https://openalex.org/W2114433479","https://openalex.org/W2171960770","https://openalex.org/W2341865734","https://openalex.org/W2512840713","https://openalex.org/W2759190050","https://openalex.org/W2783920628","https://openalex.org/W2787991113","https://openalex.org/W2886209086","https://openalex.org/W2957191877","https://openalex.org/W2963367478","https://openalex.org/W2984100107","https://openalex.org/W3012501605","https://openalex.org/W3014901735","https://openalex.org/W3018464563","https://openalex.org/W3045674654","https://openalex.org/W3164845984","https://openalex.org/W4200141046","https://openalex.org/W4296604546","https://openalex.org/W4313889993","https://openalex.org/W4380875841","https://openalex.org/W4386730175","https://openalex.org/W4389520274","https://openalex.org/W4391282591","https://openalex.org/W4391801184","https://openalex.org/W4398169514","https://openalex.org/W4399941009","https://openalex.org/W4406458387"],"related_works":[],"abstract_inverted_index":{"Wearable":[0],"lifelog":[1,74,123],"data":[2],"provides":[3,194],"continuous":[4],"physiological":[5,98,130],"and":[6,26,36,58,84,96,104,125,136,152,164,176,190,197],"behavioral":[7,24,177],"signals,":[8],"enabling":[9],"contextual":[10,30],"understanding":[11],"of":[12],"users\u2019":[13],"real-world":[14],"states.":[15],"However,":[16],"conventional":[17],"recommendation":[18,204],"models":[19],"rely":[20],"heavily":[21],"on":[22],"static":[23,95],"logs":[25],"fail":[27],"to":[28,33,110,161,170],"incorporate":[29],"variability,":[31,79],"leading":[32],"limited":[34],"personalization":[35],"biased":[37],"decision-making.":[38],"To":[39],"address":[40],"these":[41],"limitations,":[42],"this":[43],"study":[44],"proposes":[45],"a":[46,195],"case-based":[47,184],"Mixture-of-Experts":[48],"(MoE)":[49],"framework":[50,145],"that":[51,142,182],"integrates":[52],"case":[53],"routing,":[54,185],"federated":[55],"learning":[56,60,115,127],"(FL),":[57],"reinforcement":[59,126],"(RL)":[61],"for":[62,200],"context-adaptive":[63],"action":[64,137,203],"recommendation.":[65],"The":[66,154],"model":[67,118],"first":[68],"classifies":[69],"user":[70],"states\u2014derived":[71],"from":[72,159,168],"multimodal":[73],"features":[75],"such":[76],"as":[77],"heart-rate":[78],"activity":[80],"intensity,":[81],"sleep":[82],"patterns,":[83,99],"fatigue\u2014into":[85],"multiple":[86],"context-specific":[87],"cases.":[88],"A":[89],"shared":[90],"DeepFM\u2013SASRec":[91],"backbone":[92],"encodes":[93],"both":[94,150],"sequential":[97],"while":[100],"case-specific":[101],"Adapter,":[102],"LoRA,":[103],"MoE":[105,186],"expert":[106],"heads":[107],"refine":[108],"representations":[109],"capture":[111],"intra-case":[112],"variability.":[113],"Federated":[114],"enables":[116],"robust":[117],"training":[119],"under":[120],"heterogeneous;":[121],"privacy-sensitive":[122],"distributions,":[124],"optimizes":[128],"long-term":[129],"improvement":[131],"by":[132],"modeling":[133],"state":[134],"transitions":[135],"outcomes.":[138],"Experimental":[139],"results":[140],"demonstrate":[141],"the":[143],"proposed":[144],"achieves":[146],"substantial":[147],"gains":[148],"in":[149],"accuracy":[151],"fairness.":[153],"final":[155],"variant":[156],"improves":[157],"HitRate@20":[158],"0.628":[160],"0.881":[162],"(+40.3%)":[163],"reduces":[165],"Bias":[166],"Ratio":[167],"0.92":[169],"0.64":[171],"(\u201330%),":[172],"effectively":[173],"balancing":[174],"precision":[175],"diversity.":[178],"These":[179],"findings":[180],"confirm":[181],"combining":[183],"specialization,":[187],"FL-driven":[188],"robustness,":[189],"RL-based":[191],"policy":[192],"optimization":[193],"powerful":[196],"scalable":[198],"foundation":[199],"next-generation":[201],"lifelog-driven":[202],"systems.":[205]},"counts_by_year":[],"updated_date":"2026-01-29T23:13:10.619473","created_date":"2026-01-17T00:00:00"}
