{"id":"https://openalex.org/W4385567939","doi":"https://doi.org/10.1145/3580305.3599820","title":"Explicit Feature Interaction-aware Uplift Network for Online Marketing","display_name":"Explicit Feature Interaction-aware Uplift Network for Online Marketing","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385567939","doi":"https://doi.org/10.1145/3580305.3599820"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599820","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599820","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599820","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599820","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","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":["Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-3612-709X","affiliations":[{"raw_affiliation_string":"Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071989106","display_name":"Xing Tang","orcid":"https://orcid.org/0000-0003-4360-0754"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xing Tang","raw_affiliation_strings":["FiT, Tencent, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-4360-0754","affiliations":[{"raw_affiliation_string":"FiT, Tencent, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101798814","display_name":"Han Gao","orcid":"https://orcid.org/0009-0008-8819-1054"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Han Gao","raw_affiliation_strings":["FiT, Tencent, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0008-8819-1054","affiliations":[{"raw_affiliation_string":"FiT, Tencent, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079054140","display_name":"Fuyuan Lyu","orcid":"https://orcid.org/0000-0001-9345-1828"},"institutions":[{"id":"https://openalex.org/I5023651","display_name":"McGill University","ror":"https://ror.org/01pxwe438","country_code":"CA","type":"education","lineage":["https://openalex.org/I5023651"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Fuyuan Lyu","raw_affiliation_strings":["McGill University, Montreal, Canada"],"raw_orcid":"https://orcid.org/0000-0001-9345-1828","affiliations":[{"raw_affiliation_string":"McGill University, Montreal, Canada","institution_ids":["https://openalex.org/I5023651"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083350101","display_name":"Xiuqiang He","orcid":"https://orcid.org/0000-0002-4115-8205"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiuqiang He","raw_affiliation_strings":["FiT, Tencent, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-4115-8205","affiliations":[{"raw_affiliation_string":"FiT, Tencent, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.2012,"has_fulltext":true,"cited_by_count":13,"citation_normalized_percentile":{"value":0.95538048,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4507","last_page":"4515"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9868000149726868,"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":0.9868000149726868,"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":0.9751999974250793,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7621280550956726},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.660234808921814},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.6359145045280457},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.5319243669509888},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.512852668762207},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.43828126788139343},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42974090576171875},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.416031152009964},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4104447066783905},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.3824867010116577},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34450894594192505},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.12255242466926575},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09494179487228394}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7621280550956726},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.660234808921814},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6359145045280457},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5319243669509888},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.512852668762207},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.43828126788139343},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42974090576171875},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.416031152009964},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4104447066783905},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3824867010116577},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34450894594192505},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.12255242466926575},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09494179487228394},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3580305.3599820","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599820","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599820","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3580305.3599820","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599820","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599820","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1769245271","display_name":null,"funder_award_id":"62272315","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G407513521","display_name":null,"funder_award_id":"61836005, 62272315, 62172283","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G686898326","display_name":null,"funder_award_id":"62172283","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7097245526","display_name":"\u7c7b\u4eba\u4efb\u52a1\u89c4\u5212\u3001\u63a8\u7406\u53ca\u5176\u9a8c\u8bc1\u7cfb\u7edf\u7684\u7814\u7a76\u548c\u5e94\u7528","funder_award_id":"61836005","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385567939.pdf","grobid_xml":"https://content.openalex.org/works/W4385567939.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W1516659296","https://openalex.org/W2002834872","https://openalex.org/W2040276865","https://openalex.org/W2137370054","https://openalex.org/W2295598076","https://openalex.org/W2305754340","https://openalex.org/W2509235963","https://openalex.org/W2602856279","https://openalex.org/W2604662567","https://openalex.org/W2604738573","https://openalex.org/W2604924934","https://openalex.org/W2619014254","https://openalex.org/W2624816748","https://openalex.org/W2912083425","https://openalex.org/W2913954081","https://openalex.org/W2914304175","https://openalex.org/W2963052087","https://openalex.org/W2979450518","https://openalex.org/W3013503642","https://openalex.org/W3049690396","https://openalex.org/W3093945404","https://openalex.org/W3104439459","https://openalex.org/W3106521244","https://openalex.org/W3118814446","https://openalex.org/W3124999902","https://openalex.org/W3153687269","https://openalex.org/W3171671666","https://openalex.org/W3192708477","https://openalex.org/W3204398858","https://openalex.org/W4207029607","https://openalex.org/W4220930599","https://openalex.org/W4283816376","https://openalex.org/W4285145620","https://openalex.org/W4285238482","https://openalex.org/W4286588534","https://openalex.org/W4290875571","https://openalex.org/W4367046995"],"related_works":["https://openalex.org/W2357256365","https://openalex.org/W2348502264","https://openalex.org/W2188500270","https://openalex.org/W2303858293","https://openalex.org/W2365486383","https://openalex.org/W2362059367","https://openalex.org/W2915512527","https://openalex.org/W2798198862","https://openalex.org/W51364034","https://openalex.org/W2385445231"],"abstract_inverted_index":{"As":[0],"a":[1,91,118,136,154,164,168,209,217,249,256,262],"key":[2],"component":[3],"in":[4,78,248],"online":[5,258],"marketing,":[6],"uplift":[7,103,211],"modeling":[8],"aims":[9,139],"to":[10,15,90,106,140,162,189,234],"accurately":[11,141,158],"capture":[12],"the":[13,30,42,125,132,143,150,160,173,191,197,204,236],"degree":[14,161],"which":[16,163],"different":[17,20,57],"treatments":[18],"motivate":[19],"users,":[21],"such":[22],"as":[23,29],"coupons":[24],"or":[25],"discounts,":[26],"also":[27,65,131],"known":[28],"estimation":[31],"of":[32,194,238,255],"individual":[33],"treatment":[34,45,82,133,151,166,174,200],"effect":[35],"(ITE).":[36],"In":[37,59,94,241],"an":[38,99,183],"actual":[39],"business":[40],"scenario,":[41],"options":[43],"for":[44],"may":[46,53,64],"be":[47,54],"numerous":[48],"and":[49,51,69,84,127,176,181,199,230],"complex,":[50],"there":[52],"correlations":[55],"between":[56,172,196],"treatments.":[58],"addition,":[60,242],"each":[61],"marketing":[62,220],"instance":[63],"have":[66],"rich":[67],"user":[68,126,169],"contextual":[70,128],"features.":[71],"However,":[72],"existing":[73],"methods":[74],"still":[75,207],"fall":[76],"short":[77],"both":[79],"fully":[80],"exploiting":[81],"information":[83],"mining":[85],"features":[86,175],"that":[87,203],"are":[88],"sensitive":[89],"particular":[92,165],"treatment.":[93],"this":[95],"paper,":[96],"we":[97],"propose":[98],"explicit":[100],"feature":[101,119],"interaction-aware":[102],"network":[104],"(EFIN)":[105],"address":[107],"these":[108],"two":[109,227],"problems.":[110],"Our":[111],"EFIN":[112,244],"includes":[113],"four":[114],"customized":[115],"modules:":[116],"1)":[117],"encoding":[120],"module":[121,138,157,186],"encodes":[122],"not":[123],"only":[124],"features,":[129,178],"but":[130,149],"features;":[134,152],"2)":[135],"self-interaction":[137],"model":[142,205],"user's":[144],"natural":[145],"response":[146],"with":[147,261],"all":[148],"3)":[153],"treatment-aware":[155],"interaction":[156],"models":[159],"motivates":[167],"through":[170],"interactions":[171],"other":[177],"i.e.,":[179],"ITE;":[180],"4)":[182],"intervention":[184,219],"constraint":[185],"is":[187],"used":[188],"balance":[190],"ITE":[192],"distribution":[193],"users":[195],"control":[198],"groups":[201],"so":[202],"would":[206],"achieve":[208],"accurate":[210],"ranking":[212],"on":[213,226],"data":[214],"collected":[215],"from":[216],"non-random":[218],"scenario.":[221],"We":[222],"conduct":[223],"extensive":[224],"experiments":[225],"public":[228],"datasets":[229],"one":[231],"product":[232],"dataset":[233],"verify":[235],"effectiveness":[237],"our":[239,243],"EFIN.":[240],"has":[245],"been":[246],"deployed":[247],"credit":[250],"card":[251],"bill":[252],"payment":[253],"scenario":[254],"large":[257],"financial":[259],"platform":[260],"significant":[263],"improvement.":[264]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}
