{"id":"https://openalex.org/W4306317375","doi":"https://doi.org/10.1145/3511808.3557299","title":"Dually Enhanced Propensity Score Estimation in Sequential Recommendation","display_name":"Dually Enhanced Propensity Score Estimation in Sequential Recommendation","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4306317375","doi":"https://doi.org/10.1145/3511808.3557299"},"language":"en","primary_location":{"id":"doi:10.1145/3511808.3557299","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557299","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","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/A5100385709","display_name":"Xu Chen","orcid":"https://orcid.org/0000-0002-3070-9358"},"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":"Chen Xu","raw_affiliation_strings":["Renmin University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020766468","display_name":"Jun Xu","orcid":"https://orcid.org/0000-0001-7170-111X"},"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":"Jun Xu","raw_affiliation_strings":["Renmin University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100647362","display_name":"Chen Xu","orcid":"https://orcid.org/0000-0003-4397-4465"},"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":"Xu Chen","raw_affiliation_strings":["Renmin University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010241615","display_name":"Zhenghua Dong","orcid":"https://orcid.org/0009-0008-9997-2411"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenghua Dong","raw_affiliation_strings":["Huawei Noah's Ark Lab, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025631695","display_name":"Ji-Rong Wen","orcid":"https://orcid.org/0000-0002-9777-9676"},"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":"Ji-Rong Wen","raw_affiliation_strings":["Renmin University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100385709"],"corresponding_institution_ids":["https://openalex.org/I78988378"],"apc_list":null,"apc_paid":null,"fwci":1.7555,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.87530388,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2260","last_page":"2269"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":1.0,"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":1.0,"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":0.9983999729156494,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9965000152587891,"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/propensity-score-matching","display_name":"Propensity score matching","score":0.7580012083053589},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7322582006454468},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.5891664028167725},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5258391499519348},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4462081491947174},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.4367407560348511},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39756423234939575},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3457232713699341},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32911670207977295},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.23454752564430237},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1532708704471588},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1412247121334076}],"concepts":[{"id":"https://openalex.org/C17923572","wikidata":"https://www.wikidata.org/wiki/Q7250160","display_name":"Propensity score matching","level":2,"score":0.7580012083053589},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7322582006454468},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.5891664028167725},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5258391499519348},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4462081491947174},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.4367407560348511},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39756423234939575},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3457232713699341},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32911670207977295},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23454752564430237},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1532708704471588},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1412247121334076},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3511808.3557299","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557299","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1045197148","display_name":null,"funder_award_id":"61872338, 62102420,61832017","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":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W2077927809","https://openalex.org/W2101409192","https://openalex.org/W2157331557","https://openalex.org/W2171279286","https://openalex.org/W2413794162","https://openalex.org/W2469952266","https://openalex.org/W2507134384","https://openalex.org/W2723293840","https://openalex.org/W2740920897","https://openalex.org/W2809307135","https://openalex.org/W2897674092","https://openalex.org/W2899259597","https://openalex.org/W2905569957","https://openalex.org/W2912967843","https://openalex.org/W2962745591","https://openalex.org/W2984100107","https://openalex.org/W2994850640","https://openalex.org/W2998534896","https://openalex.org/W2998673651","https://openalex.org/W3034348890","https://openalex.org/W3035404611","https://openalex.org/W3047934539","https://openalex.org/W3065542300","https://openalex.org/W3083159507","https://openalex.org/W3099096815","https://openalex.org/W3100260481","https://openalex.org/W3153754021","https://openalex.org/W3154419237","https://openalex.org/W3156622960","https://openalex.org/W3156844209","https://openalex.org/W3209048663","https://openalex.org/W4224313077","https://openalex.org/W4224322431"],"related_works":["https://openalex.org/W2026576563","https://openalex.org/W3196761963","https://openalex.org/W2036193982","https://openalex.org/W213628847","https://openalex.org/W2065417422","https://openalex.org/W101468167","https://openalex.org/W4232168831","https://openalex.org/W4253956144","https://openalex.org/W3023923059","https://openalex.org/W2105161754"],"abstract_inverted_index":{"Sequential":[0],"recommender":[1],"systems":[2],"train":[3],"their":[4],"models":[5],"based":[6,32,191],"on":[7,33,192,230],"a":[8,44,148,163,236],"large":[9],"amount":[10],"of":[11,42,70,80,98,103,135,186,196,208,226],"implicit":[12],"user":[13,136,157,197],"feedback":[14,76,89,198],"data":[15,77,90],"and":[16,137,152,156,188,213,224,235],"may":[17],"be":[18,93],"subject":[19],"to":[20,27,54,67,127,167,174,205],"biases":[21],"when":[22],"users":[23,104],"are":[24,181,203],"systematically":[25],"under/over-exposed":[26],"certain":[28],"items.":[29,109],"Unbiased":[30],"learning":[31],"inverse":[34],"propensity":[35,61,118,130,179,211],"scores":[36,131,180,212],"(IPS),":[37],"which":[38],"estimate":[39,128],"the":[40,48,56,68,75,85,88,96,101,108,111,117,123,129,133,153,169,175,184,193,209,215,222,246],"probability":[41],"observing":[43],"user-item":[45,150,170],"pair":[46,151],"given":[47,147],"historical":[49],"information,":[50],"has":[51],"been":[52],"proposed":[53],"address":[55],"issue.":[57],"In":[58],"these":[59],"methods,":[60],"score":[62,119],"estimation":[63],"is":[64],"usually":[65],"limited":[66],"view":[69,97],"item,":[71,138],"that":[72,82,105,241],"is,":[73],"treating":[74],"as":[78,100],"sequences":[79,102],"items":[81],"interacted":[83],"with":[84,107],"users.":[86],"However,":[87],"can":[91,114,243],"also":[92],"treated":[94],"from":[95,132,183],"user,":[99,189],"interact":[106],"Moreover,":[110],"two":[112,177,201,210],"views":[113,134,185],"jointly":[115],"enhance":[116],"estimation.":[120],"Inspired":[121],"by":[122],"observation,":[124],"we":[125],"propose":[126],"called":[139],"Dually":[140],"Enhanced":[141],"Propensity":[142],"Score":[143],"Estimation":[144],"(DEPS).":[145],"Specifically,":[146],"target":[149],"corresponding":[154],"item":[155,187],"interaction":[158],"sequences,":[159],"DEPS":[160,242],"first":[161],"constructs":[162],"time-aware":[164],"causal":[165],"graph":[166],"represent":[168],"observational":[171],"probability.":[172],"According":[173],"graph,":[176],"complementary":[178],"estimated":[182],"respectively,":[190],"same":[194],"set":[195],"data.":[199],"Finally,":[200],"transformers":[202],"designed":[204],"make":[206,214],"use":[207],"final":[216],"preference":[217],"prediction.":[218],"Theoretical":[219],"analysis":[220],"showed":[221],"unbiasedness":[223],"variance":[225],"DEPS.":[227],"Experimental":[228],"results":[229],"three":[231],"publicly":[232],"available":[233],"benchmarks":[234],"proprietary":[237],"industrial":[238],"dataset":[239],"demonstrated":[240],"significantly":[244],"outperform":[245],"state-of-the-art":[247],"baselines.":[248]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-10-10T00:00:00"}