{"id":"https://openalex.org/W4387848954","doi":"https://doi.org/10.1145/3583780.3614760","title":"A Generalized Propensity Learning Framework for Unbiased Post-Click Conversion Rate Estimation","display_name":"A Generalized Propensity Learning Framework for Unbiased Post-Click Conversion Rate Estimation","publication_year":2023,"publication_date":"2023-10-21","ids":{"openalex":"https://openalex.org/W4387848954","doi":"https://doi.org/10.1145/3583780.3614760"},"language":"en","primary_location":{"id":"doi:10.1145/3583780.3614760","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3614760","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3614760","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3614760","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071075370","display_name":"Yuqing Zhou","orcid":"https://orcid.org/0009-0002-8357-7704"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yuqing Zhou","raw_affiliation_strings":["George Mason University, Fairfax, VA, USA"],"affiliations":[{"raw_affiliation_string":"George Mason University, Fairfax, VA, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080932337","display_name":"Tianshu Feng","orcid":"https://orcid.org/0000-0002-8798-0341"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianshu Feng","raw_affiliation_strings":["George Mason University, Fairfax, VA, USA"],"affiliations":[{"raw_affiliation_string":"George Mason University, Fairfax, VA, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071391342","display_name":"Mingrui Liu","orcid":"https://orcid.org/0000-0002-5181-3429"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mingrui Liu","raw_affiliation_strings":["George Mason University, Fairfax, VA, USA"],"affiliations":[{"raw_affiliation_string":"George Mason University, Fairfax, VA, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019994221","display_name":"Ziwei Zhu","orcid":"https://orcid.org/0000-0002-3990-4774"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ziwei Zhu","raw_affiliation_strings":["George Mason University, Fairfax, VA, USA"],"affiliations":[{"raw_affiliation_string":"George Mason University, Fairfax, VA, USA","institution_ids":["https://openalex.org/I162714631"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5071075370"],"corresponding_institution_ids":["https://openalex.org/I162714631"],"apc_list":null,"apc_paid":null,"fwci":2.7533,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.92114956,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3554","last_page":"3563"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9955000281333923,"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/T11478","display_name":"Caching and Content Delivery","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/debiasing","display_name":"Debiasing","score":0.8567123413085938},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.74680495262146},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.6925305128097534},{"id":"https://openalex.org/keywords/propensity-score-matching","display_name":"Propensity score matching","score":0.6745221614837646},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6434012651443481},{"id":"https://openalex.org/keywords/complement","display_name":"Complement (music)","score":0.6293989419937134},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4803410768508911},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.46940934658050537},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.42118608951568604},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3941829800605774},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3756970763206482},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33162009716033936},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.28334277868270874},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1530258059501648}],"concepts":[{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.8567123413085938},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.74680495262146},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.6925305128097534},{"id":"https://openalex.org/C17923572","wikidata":"https://www.wikidata.org/wiki/Q7250160","display_name":"Propensity score matching","level":2,"score":0.6745221614837646},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6434012651443481},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.6293989419937134},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4803410768508911},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.46940934658050537},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.42118608951568604},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3941829800605774},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3756970763206482},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33162009716033936},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.28334277868270874},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1530258059501648},{"id":"https://openalex.org/C188082640","wikidata":"https://www.wikidata.org/wiki/Q1780899","display_name":"Complementation","level":4,"score":0.0},{"id":"https://openalex.org/C127716648","wikidata":"https://www.wikidata.org/wiki/Q104053","display_name":"Phenotype","level":3,"score":0.0},{"id":"https://openalex.org/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3583780.3614760","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3614760","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3614760","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3583780.3614760","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3614760","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3614760","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387848954.pdf","grobid_xml":"https://content.openalex.org/works/W4387848954.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W126907439","https://openalex.org/W151167705","https://openalex.org/W1970210633","https://openalex.org/W1992665562","https://openalex.org/W2020631728","https://openalex.org/W2054141820","https://openalex.org/W2090883204","https://openalex.org/W2137370054","https://openalex.org/W2295739661","https://openalex.org/W2475334473","https://openalex.org/W2509235963","https://openalex.org/W2728796024","https://openalex.org/W2768307941","https://openalex.org/W2797400361","https://openalex.org/W2798283910","https://openalex.org/W2892888989","https://openalex.org/W2944145800","https://openalex.org/W2962989965","https://openalex.org/W2964182926","https://openalex.org/W2987016235","https://openalex.org/W3012576969","https://openalex.org/W3035178789","https://openalex.org/W3035397484","https://openalex.org/W3035596828","https://openalex.org/W3088432326","https://openalex.org/W3102871501","https://openalex.org/W3105712174","https://openalex.org/W3153682915","https://openalex.org/W4248949446","https://openalex.org/W4290927708"],"related_works":["https://openalex.org/W4362554880","https://openalex.org/W4281684980","https://openalex.org/W4386875279","https://openalex.org/W2171721708","https://openalex.org/W4390963114","https://openalex.org/W3214527415","https://openalex.org/W4287887864","https://openalex.org/W1495104519","https://openalex.org/W4225584739","https://openalex.org/W2199432031"],"abstract_inverted_index":{"This":[0],"paper":[1],"addresses":[2],"the":[3,7,36,42,65,108],"critical":[4],"gap":[5],"in":[6,15,69],"unbiased":[8],"estimation":[9,40],"of":[10,38],"post-click":[11],"conversion":[12],"rate":[13],"(CVR)":[14],"recommender":[16],"systems.":[17],"Existing":[18],"CVR":[19,70],"prediction":[20,71,91],"methods,":[21],"such":[22],"as":[23,77],"Inverse":[24],"Propensity":[25,58],"Score":[26],"(IPS)":[27],"and":[28,45,67,87,97,104,118],"various":[29],"Doubly":[30],"Robust":[31],"(DR)":[32],"based":[33],"estimators,":[34],"overlook":[35],"impact":[37],"propensity":[39],"on":[41,101],"model":[43],"bias":[44,66,96],"variance,":[46],"thus":[47],"leading":[48],"to":[49,62,80,89],"a":[50,56,78],"debiasing":[51],"performance":[52,92,110],"gap.":[53],"We":[54],"propose":[55],"Generalized":[57],"Learning":[59],"(GPL)":[60],"framework":[61],"directly":[63],"minimize":[64],"variance":[68],"models.":[72],"The":[73],"proposed":[74,115],"method":[75],"works":[76],"complement":[79],"existing":[81],"methods":[82],"like":[83],"IPS,":[84],"DR,":[85],"MRDR,":[86],"DRMSE":[88],"improve":[90],"by":[93,113],"reducing":[94],"their":[95],"variance.":[98],"Extensive":[99],"experiments":[100],"real-world":[102],"datasets":[103,106],"semi-synthetic":[105],"demonstrate":[107],"significant":[109],"promotion":[111],"brought":[112],"our":[114],"method.":[116],"Data":[117],"code":[119],"can":[120],"be":[121],"found":[122],"at:":[123],"https://github.com/yuqing-zhou/GPL.":[124]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}