{"id":"https://openalex.org/W4409150462","doi":"https://doi.org/10.1145/3690624.3709293","title":"Robust Uplift Modeling with Large-Scale Contexts for Real-time Marketing","display_name":"Robust Uplift Modeling with Large-Scale Contexts for Real-time Marketing","publication_year":2025,"publication_date":"2025-04-04","ids":{"openalex":"https://openalex.org/W4409150462","doi":"https://doi.org/10.1145/3690624.3709293"},"language":"en","primary_location":{"id":"doi:10.1145/3690624.3709293","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3690624.3709293","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","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/A5087242567","display_name":"Zexu Sun","orcid":"https://orcid.org/0000-0002-6727-6242"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zexu Sun","raw_affiliation_strings":["Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045163903","display_name":"Qiyu Han","orcid":"https://orcid.org/0009-0005-3196-304X"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiyu Han","raw_affiliation_strings":["School of Statistics, Renmin University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Statistics, Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092346286","display_name":"Minqin Zhu","orcid":"https://orcid.org/0009-0008-9527-8895"},"institutions":[{"id":"https://openalex.org/I168879160","display_name":"Zhejiang University of Science and Technology","ror":"https://ror.org/05mx0wr29","country_code":"CN","type":"education","lineage":["https://openalex.org/I168879160"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minqin Zhu","raw_affiliation_strings":["Department of Computer Science and Technology, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I168879160"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027281304","display_name":"Hao Gong","orcid":"https://orcid.org/0009-0006-8401-3686"},"institutions":[{"id":"https://openalex.org/I4401726859","display_name":"Kuaishou (China)","ror":"https://ror.org/0258as409","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Gong","raw_affiliation_strings":["Kuaishou Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Kuaishou Technology, Beijing, China","institution_ids":["https://openalex.org/I4401726859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003106644","display_name":"Dugang Liu","orcid":"https://orcid.org/0000-0003-3612-709X"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dugang Liu","raw_affiliation_strings":["College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100410908","display_name":"Chen Ma","orcid":"https://orcid.org/0000-0001-7933-9813"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Chen Ma","raw_affiliation_strings":["Department of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5087242567"],"corresponding_institution_ids":["https://openalex.org/I78988378"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.0740534,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1325","last_page":"1336"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.963100016117096,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9574000239372253,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5586696267127991},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5574514865875244},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3246194124221802},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11661034822463989}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5586696267127991},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5574514865875244},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3246194124221802},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11661034822463989},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3690624.3709293","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3690624.3709293","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4399999976158142,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W229053775","https://openalex.org/W1516659296","https://openalex.org/W2011485768","https://openalex.org/W2069065514","https://openalex.org/W2108999965","https://openalex.org/W2127403366","https://openalex.org/W2140899775","https://openalex.org/W2177065549","https://openalex.org/W2200830626","https://openalex.org/W2624816748","https://openalex.org/W2774133674","https://openalex.org/W2809290718","https://openalex.org/W2945343630","https://openalex.org/W2949676527","https://openalex.org/W2971576018","https://openalex.org/W3003604602","https://openalex.org/W3093945404","https://openalex.org/W3132126111","https://openalex.org/W3153687269","https://openalex.org/W3202432965","https://openalex.org/W3204398858","https://openalex.org/W4207029607","https://openalex.org/W4221156845","https://openalex.org/W4367046712","https://openalex.org/W4384661053","https://openalex.org/W4387841511","https://openalex.org/W4387848901","https://openalex.org/W4405022419","https://openalex.org/W6600266142"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Improving":[0],"user":[1,47,78,92,124,145,154],"engagement":[2],"and":[3,67,135,147,194,253,262],"platform":[4,62],"revenue":[5],"is":[6,14,84],"crucial":[7],"for":[8,76,108,174,189,205,220,229],"online":[9,61],"marketing":[10],"platforms.":[11],"Uplift":[12,167],"modeling":[13,222],"proposed":[15],"to":[16,27,72,96,113,239,258],"solve":[17,157],"this":[18,34,211],"problem,":[19],"which":[20,83,102],"applies":[21],"different":[22],"treatments":[23],"(e.g.,":[24,63],"discounts,":[25],"bonus)":[26],"satisfy":[28],"corresponding":[29],"users.":[30],"Despite":[31],"progress":[32],"in":[33,51,59,132],"field,":[35],"limitations":[36],"persist.":[37],"Firstly,":[38],"most":[39],"of":[40,99,236,264],"them":[41],"focus":[42],"on":[43,79,249],"scenarios":[44],"where":[45],"only":[46,89],"features":[48,93,121,125,146,149,192],"exist.":[49],"However,":[50],"real-world":[52,255],"scenarios,":[53],"there":[54],"are":[55],"rich":[56],"contexts":[57],"available":[58],"the":[60,68,80,91,100,105,114,119,123,133,140,144,153,158,223,231,242,260],"short":[64],"videos,":[65],"news),":[66],"uplift":[69,109,208],"model":[70],"needs":[71],"infer":[73],"an":[74],"incentive":[75],"each":[77,237],"specific":[81],"item,":[82],"called":[85],"real-time":[86],"marketing.":[87],"Thus,":[88],"considering":[90],"will":[94,126],"lead":[95],"biased":[97],"prediction":[98],"responses,":[101],"may":[103],"cause":[104,127],"cumulative":[106],"error":[107],"prediction.":[110,209],"Moreover,":[111,244],"due":[112],"large-scale":[115],"contexts,":[116],"directly":[117],"concatenating":[118],"context":[120,148,186,191],"with":[122,169],"a":[128,163,216,225,250,254],"severe":[129],"distribution":[130],"shift":[131],"treatment":[134,232],"control":[136],"groups.":[137],"Secondly,":[138],"capturing":[139],"interaction":[141,203,218,227],"relationship":[142],"between":[143],"can":[150],"better":[151,207,221,240],"predict":[152,241],"response.":[155],"To":[156],"above":[159],"limitations,":[160],"we":[161,245],"propose":[162],"novel":[164],"model-agnostic":[165],"Robust":[166],"Modeling":[168],"Large-Scale":[170],"Contexts":[171],"(UMLC)":[172],"framework":[173],"Real-time":[175],"Marketing.":[176],"Our":[177],"UMLC":[178],"includes":[179],"two":[180,214],"customized":[181],"modules.":[182],"1)":[183],"A":[184,201],"response-guided":[185],"grouping":[187],"module":[188,204,212],"extracting":[190],"information":[193],"condensing":[195],"value":[196],"space":[197],"through":[198],"clusters.":[199],"2)":[200],"feature":[202,235],"obtaining":[206],"Specifically,":[210],"contains":[213],"parts:":[215],"user-context":[217],"component":[219,228],"response;":[224],"treatment-feature":[226],"discovering":[230],"assignment":[233],"sensitive":[234],"instance":[238],"uplift.":[243],"conduct":[246],"extensive":[247],"experiments":[248],"synthetic":[251],"dataset":[252,257],"product":[256],"verify":[259],"effectiveness":[261],"compatibility":[263],"our":[265],"UMLC.":[266]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}