{"id":"https://openalex.org/W7154222140","doi":"https://doi.org/10.48550/arxiv.2604.09964","title":"From Recency Bias to Stable Convergence Block Kaczmarz Methods for Online Preference Learning in Matchmaking Applications","display_name":"From Recency Bias to Stable Convergence Block Kaczmarz Methods for Online Preference Learning in Matchmaking Applications","publication_year":2026,"publication_date":"2026-04-11","ids":{"openalex":"https://openalex.org/W7154222140","doi":"https://doi.org/10.48550/arxiv.2604.09964"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.09964","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09964","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.09964","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133617149","display_name":"James Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Nguyen, James","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5133617149"],"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/T10203","display_name":"Recommender Systems and Techniques","score":0.8382999897003174,"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":0.8382999897003174,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.04670000076293945,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.013299999758601189,"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/normalization","display_name":"Normalization (sociology)","score":0.5457000136375427},{"id":"https://openalex.org/keywords/tikhonov-regularization","display_name":"Tikhonov regularization","score":0.5095999836921692},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.4894999861717224},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.48410001397132874},{"id":"https://openalex.org/keywords/cosine-similarity","display_name":"Cosine similarity","score":0.4339999854564667},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.3797999918460846},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.3714999854564667},{"id":"https://openalex.org/keywords/exponential-function","display_name":"Exponential function","score":0.34769999980926514},{"id":"https://openalex.org/keywords/rate-of-convergence","display_name":"Rate of convergence","score":0.3465999960899353}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5740000009536743},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5504000186920166},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5457000136375427},{"id":"https://openalex.org/C152442038","wikidata":"https://www.wikidata.org/wiki/Q2778212","display_name":"Tikhonov regularization","level":3,"score":0.5095999836921692},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.4894999861717224},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.48410001397132874},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.4339999854564667},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41999998688697815},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.3797999918460846},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.3714999854564667},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3650999963283539},{"id":"https://openalex.org/C151376022","wikidata":"https://www.wikidata.org/wiki/Q168698","display_name":"Exponential function","level":2,"score":0.34769999980926514},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.3465999960899353},{"id":"https://openalex.org/C2779623668","wikidata":"https://www.wikidata.org/wiki/Q7652842","display_name":"SwIPe","level":2,"score":0.337799996137619},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.33009999990463257},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.3221000134944916},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.3084000051021576},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.30799999833106995},{"id":"https://openalex.org/C97399411","wikidata":"https://www.wikidata.org/wiki/Q825367","display_name":"Coin flipping","level":2,"score":0.29490000009536743},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C2777742833","wikidata":"https://www.wikidata.org/wiki/Q1964083","display_name":"Reciprocal","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C311688","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Time complexity","level":2,"score":0.28189998865127563},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2547000050544739},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C181204326","wikidata":"https://www.wikidata.org/wiki/Q7239820","display_name":"Preference learning","level":3,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.09964","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09964","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.09964","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09964","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,51,115],"present":[1],"a":[2,60,118,127,211],"family":[3],"of":[4,31,209,243],"Kaczmarz-based":[5],"preference":[6,156,200],"learning":[7,113],"algorithms":[8],"for":[9],"real-time":[10],"personalized":[11],"matchmaking":[12],"in":[13,21,81,87,181],"reciprocal":[14],"recommender":[15],"systems.":[16],"Post-step":[17],"L2":[18,151],"normalization,":[19,152],"common":[20],"Kaczmarz-inspired":[22],"online":[23,107],"learners,":[24],"induces":[25],"exponential":[26],"recency":[27],"bias:":[28],"the":[29,32,56,90,145,154,161,170,194,198,207,233,241,248],"influence":[30],"t-th":[33],"interaction":[34,71],"decays":[35],"as":[36,80,126],"eta^(n":[37],"-":[38],"t),":[39],"reaching":[40],"approximately":[41],"1e-6":[42],"after":[43],"just":[44],"20":[45],"swipes":[46,136],"at":[47,206],"eta":[48],"=":[49,159,167],"0.5.":[50],"resolve":[52],"this":[53],"by":[54],"replacing":[55],"normalization":[57,245],"step":[58,66,100],"with":[59,110,149],"Tikhonov-regularized":[61],"projection":[62],"denominator":[63,92],"that":[64,121,138,191,214],"bounds":[65],"size":[67],"analytically":[68],"without":[69],"erasing":[70],"history.":[72],"When":[73],"candidate":[74,195],"tag":[75,88],"vectors":[76],"are":[77],"not":[78],"pre-normalized,":[79],"realistic":[82],"deployments":[83],"where":[84],"candidates":[85],"vary":[86],"density,":[89],"Tikhonov":[91,249],"||a||^2":[93],"+":[94],"alpha":[95,251],"produces":[96],"genuinely":[97],"per-candidate":[98],"adaptive":[99],"sizes,":[101],"making":[102],"it":[103],"structurally":[104],"distinct":[105],"from":[106,218],"gradient":[108],"descent":[109],"any":[111],"fixed":[112],"rate.":[114],"further":[116,189],"derive":[117],"block":[119],"variant":[120],"processes":[122],"full":[123],"swipe":[124],"sessions":[125],"single":[128],"Gram":[129,147],"matrix":[130],"solve.":[131],"Population-scale":[132],"simulation":[133,230],"over":[134,237],"6,400":[135],"reveals":[137],"Block":[139],"Normalized":[140],"Kaczmarz":[141,239],"(BlockNK),":[142],"which":[143],"combines":[144],"batch":[146],"solve":[148],"post-session":[150],"achieves":[153],"highest":[155],"alignment":[157],"(Align@20":[158],"0.698),":[160],"strongest":[162],"inter-session":[163],"direction":[164,201],"stability":[165],"(delta":[166],"0.994),":[168],"and":[169],"flattest":[171],"degradation":[172],"profile":[173],"under":[174,185,228],"label":[175],"noise":[176],"across":[177],"flip":[178],"ratios":[179],"p_flip":[180],"[0.10,":[182],"0.35].":[183],"Experiments":[184],"cosine":[186],"similarity":[187],"subsampling":[188],"show":[190],"adaptively":[192],"filtering":[193],"pool":[196],"toward":[197],"current":[199],"substantially":[202],"improves":[203],"asymptotic":[204],"alignment,":[205],"cost":[208],"introducing":[210],"feedback":[212],"loop":[213],"may":[215],"slow":[216],"recovery":[217],"miscalibration.":[219],"The":[220],"sequential":[221],"Tikhonov-Kaczmarz":[222],"method":[223],"performs":[224],"comparably":[225],"to":[226],"K-NoNorm":[227],"our":[229],"conditions,":[231],"suggesting":[232],"dominant":[234],"practical":[235],"gain":[236],"normalized":[238],"is":[240],"removal":[242],"per-step":[244],"rather":[246],"than":[247],"constant":[250],"itself.":[252]},"counts_by_year":[],"updated_date":"2026-04-15T06:04:33.058270","created_date":"2026-04-15T00:00:00"}