{"id":"https://openalex.org/W4383557929","doi":"https://doi.org/10.1145/3580507.3597797","title":"Feature Based Dynamic Matching","display_name":"Feature Based Dynamic Matching","publication_year":2023,"publication_date":"2023-07-07","ids":{"openalex":"https://openalex.org/W4383557929","doi":"https://doi.org/10.1145/3580507.3597797"},"language":"en","primary_location":{"id":"doi:10.1145/3580507.3597797","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580507.3597797","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM Conference on Economics and Computation","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039527677","display_name":"Yilun Chen","orcid":"https://orcid.org/0000-0001-9638-2100"},"institutions":[{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yilun Chen","raw_affiliation_strings":["School of Data Science, Chinese University of Hong Kong, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"School of Data Science, Chinese University of Hong Kong, Shenzhen, China","institution_ids":["https://openalex.org/I4210116924"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000266593","display_name":"Yash Kanoria","orcid":"https://orcid.org/0000-0002-7221-357X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yash Kanoria","raw_affiliation_strings":["Decision, Risk and Operations, Columbia Business School, New York, United States of America"],"affiliations":[{"raw_affiliation_string":"Decision, Risk and Operations, Columbia Business School, New York, United States of America","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102785483","display_name":"Akshit Kumar","orcid":"https://orcid.org/0000-0002-3418-2514"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Akshit Kumar","raw_affiliation_strings":["Decision, Risk and Operations, Columbia Business School, New York, United States of America"],"affiliations":[{"raw_affiliation_string":"Decision, Risk and Operations, Columbia Business School, New York, United States of America","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103090157","display_name":"Wenxin Zhang","orcid":"https://orcid.org/0009-0004-1965-4335"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenxin Zhang","raw_affiliation_strings":["Decision, Risk and Operations, Columbia Business School, New York, USA"],"affiliations":[{"raw_affiliation_string":"Decision, Risk and Operations, Columbia Business School, New York, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5039527677"],"corresponding_institution_ids":["https://openalex.org/I4210116924"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11671743,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"451","last_page":"451"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10991","display_name":"Game Theory and Voting Systems","score":0.9027000069618225,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10991","display_name":"Game Theory and Voting Systems","score":0.9027000069618225,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/soar","display_name":"Soar","score":0.7691593170166016},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6958668828010559},{"id":"https://openalex.org/keywords/regret","display_name":"Regret","score":0.6928569078445435},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5979694724082947},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49093368649482727},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.4483005404472351},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4060696065425873},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24221858382225037},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.17565694451332092},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1304926872253418}],"concepts":[{"id":"https://openalex.org/C17305859","wikidata":"https://www.wikidata.org/wiki/Q382944","display_name":"Soar","level":2,"score":0.7691593170166016},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6958668828010559},{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.6928569078445435},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5979694724082947},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49093368649482727},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.4483005404472351},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4060696065425873},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24221858382225037},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.17565694451332092},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1304926872253418},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3580507.3597797","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580507.3597797","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM Conference on Economics and Computation","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5954070948","display_name":null,"funder_award_id":"CMMI-","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7054234334","display_name":null,"funder_award_id":"1653477","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8299737091","display_name":null,"funder_award_id":"CMMI-1653477","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337391","display_name":"Division of Civil, Mechanical and Manufacturing Innovation","ror":"https://ror.org/028yd4c30"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2163445345","https://openalex.org/W177098144","https://openalex.org/W1972687012","https://openalex.org/W2506478959","https://openalex.org/W4250121399","https://openalex.org/W2583284593","https://openalex.org/W1578440997","https://openalex.org/W4376155396","https://openalex.org/W39838470","https://openalex.org/W1652675861"],"abstract_inverted_index":{"Motivated":[0],"by":[1,47,168],"matching":[2,18,62,86,105,202,229],"platforms":[3],"that":[4,70,160,233],"match":[5,185,213],"agents":[6],"in":[7,127,215,261,374],"a":[8,13,64,68,84,153,242,341,362],"centralized":[9,114],"manner,":[10],"we":[11,93,231,339,367],"introduce":[12],"model":[14,38,169,259],"of":[15,32,44,63,83,97,112,177,255,265,277,300,305,348,355,364],"dynamic":[16],"two-sided":[17],"where":[19,287],"both":[20,308],"demand":[21,49,125,285,311,323],"and":[22,29,52,75,107,163,187,284,291,302,310,319,322,358],"supply":[23,33,54,120,190,283,309,321],"are":[24,136,313,325],"heterogeneous":[25,85],"with":[26,57,257],"many":[27],"types":[28],"the":[30,41,45,80,101,113,140,147,175,205,210,216,227,236,248,269,273,278,282,293,346],"pool":[31],"units":[34,121,126],"is":[35,116],"limited.":[36],"We":[37,151,194,245],"heterogeneity":[39],"on":[40,72,104,226],"two":[42,263,303],"sides":[43],"market":[46,87],"i.i.d.":[48,53],"weight":[50,74],"vectors":[51,286],"feature":[55,76,108],"vectors,":[56],"possibly":[58],"different":[59],"distributions.":[60,109],"The":[61,110],"demand-supply":[65],"pair":[66],"generates":[67],"utility":[69,186,211,266,295],"depends":[71],"their":[73],"vectors.":[77],"To":[78],"reflect":[79],"realistic":[81],"structure":[82],"while":[88],"also":[89,368],"avoid":[90],"impossibility":[91],"results,":[92],"consider":[94],"various":[95],"levels":[96],"assumptions":[98,225],"(in":[99],"particular,":[100,262],"spatial":[102],"structure)":[103],"utilities":[106],"goal":[111],"platform":[115],"to":[117,122,129,179,209,241,335],"dynamically":[118],"assign":[119],"sequentially":[123],"arriving":[124],"order":[128],"maximize":[130],"utility.":[131],"Many":[132],"popular":[133],"heuristic":[134],"policies":[135],"either":[137],"sub-optimal":[138],"(like":[139,146],"myopic":[141],"policy)":[142],"or":[143],"computationally":[144],"inefficient":[145],"certainty":[148],"equivalent":[149],"policy).":[150],"propose":[152],"forward-looking":[154],"supply-aware":[155],"policy":[156,351],"dubbed":[157],"Simulate-Optimize-Assign-Repeat":[158],"(SOAR)":[159],"combines":[161],"practicality":[162],"strong":[164],"theoretical":[165],"guarantee.":[166],"Inspired":[167],"predictive":[170],"control":[171],"(MPC),":[172],"SOAR":[173,234,350],"leverages":[174],"power":[176,276],"simulation":[178],"balance":[180],"between":[181,281],"producing":[182],"immediate":[183],"high":[184],"preserving":[188],"valuable":[189],"for":[191,201,252,344],"future":[192],"demands.":[193],"use":[195],"regret":[196,238,250],"as":[197],"our":[198,337,349,365],"performance":[199,347],"metric":[200],"policies,":[203],"specifically":[204],"additive":[206],"loss":[207],"relative":[208],"per":[212],"achievable":[214],"continuum":[217],"limit":[218],"(n":[219],"\u2192":[220],"\u221e).":[221],"Under":[222],"mild":[223,331],"regularity":[224],"offline":[228],"instances,":[230],"prove":[232],"achieves":[235],"optimal":[237,249],"scaling":[239,251],"(up":[240],"log":[243],"factor).":[244],"further":[246],"characterize":[247],"interesting":[253],"classes":[254,264,304],"problems":[256],"additional":[258],"structure,":[260],"functions:":[267],"(i)":[268,307],"\"spatial":[270],"utilities\",":[271],"namely":[272],"negative":[274],"p-th":[275],"Euclidean":[279],"distance":[280],"p":[288,297],"\u2265":[289],"1;":[290],"(ii)":[292,320],"dot-product":[294],"(equivalently":[296],"=":[298],"2":[299],"(i)),":[301],"distributions:":[306],"distributions":[312,324],"smooth":[314],"(a":[315,330],"more":[316],"stringent":[317],"assumption)":[318],"supported":[326],"over":[327],"compact":[328],"sets":[329],"assumption).":[332],"En":[333],"route":[334],"proving":[336],"guarantees":[338],"develop":[340],"novel":[342],"framework":[343],"analyzing":[345],"which":[352],"may":[353],"be":[354],"wider":[356],"applicability":[357],"independent":[359],"interest.":[360],"As":[361],"corollary":[363],"techniques,":[366],"resolve":[369],"an":[370],"open":[371],"problem":[372],"posed":[373],"Kanoria":[375],"2022.":[376]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
