{"id":"https://openalex.org/W4367047369","doi":"https://doi.org/10.1145/3543507.3583260","title":"Recommendation with Causality enhanced Natural Language Explanations","display_name":"Recommendation with Causality enhanced Natural Language Explanations","publication_year":2023,"publication_date":"2023-04-26","ids":{"openalex":"https://openalex.org/W4367047369","doi":"https://doi.org/10.1145/3543507.3583260"},"language":"en","primary_location":{"id":"doi:10.1145/3543507.3583260","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3543507.3583260","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2023","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/A5101658564","display_name":"Jingsen Zhang","orcid":"https://orcid.org/0000-0003-2997-3386"},"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":"Jingsen Zhang","raw_affiliation_strings":["Renmin University of China, China"],"raw_orcid":"https://orcid.org/0000-0003-2997-3386","affiliations":[{"raw_affiliation_string":"Renmin University of China, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101755392","display_name":"Xu Chen","orcid":"https://orcid.org/0000-0003-0144-1775"},"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, China"],"raw_orcid":"https://orcid.org/0000-0003-0144-1775","affiliations":[{"raw_affiliation_string":"Renmin University of China, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108000448","display_name":"Jiakai Tang","orcid":"https://orcid.org/0000-0002-7769-7147"},"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":"Jiakai Tang","raw_affiliation_strings":["Renmin University of China, China"],"raw_orcid":"https://orcid.org/0000-0002-7769-7147","affiliations":[{"raw_affiliation_string":"Renmin University of China, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071811648","display_name":"Weiqi Shao","orcid":"https://orcid.org/0000-0003-1225-6997"},"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":"Weiqi Shao","raw_affiliation_strings":["Renmin University of China, China"],"raw_orcid":"https://orcid.org/0000-0003-1225-6997","affiliations":[{"raw_affiliation_string":"Renmin University of China, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048783161","display_name":"Quanyu Dai","orcid":"https://orcid.org/0000-0001-7578-2738"},"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":"Quanyu Dai","raw_affiliation_strings":["Huawei Noah's Ark Lab, China"],"raw_orcid":"https://orcid.org/0000-0001-7578-2738","affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021124418","display_name":"Zhenhua Dong","orcid":"https://orcid.org/0000-0002-2231-4663"},"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":"Zhenhua Dong","raw_affiliation_strings":["Huawei Noah's Ark Lab, China"],"raw_orcid":"https://orcid.org/0000-0002-2231-4663","affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100422092","display_name":"Rui Zhang","orcid":"https://orcid.org/0000-0002-8132-6250"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rui Zhang","raw_affiliation_strings":["www.ruizhang.info, China"],"raw_orcid":"https://orcid.org/0000-0002-8132-6250","affiliations":[{"raw_affiliation_string":"www.ruizhang.info, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5101658564"],"corresponding_institution_ids":["https://openalex.org/I78988378"],"apc_list":null,"apc_paid":null,"fwci":8.7664,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.97697133,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"876","last_page":"886"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9943000078201294,"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"}},{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.992900013923645,"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":0.7846827507019043},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.6506654024124146},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5446540713310242},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.5154502987861633},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4960688054561615},{"id":"https://openalex.org/keywords/propensity-score-matching","display_name":"Propensity score matching","score":0.46308642625808716},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.44288498163223267},{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.4387114644050598},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.42099428176879883},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.41324010491371155},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37462782859802246},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18118241429328918},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1186753511428833}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7846827507019043},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6506654024124146},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5446540713310242},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.5154502987861633},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4960688054561615},{"id":"https://openalex.org/C17923572","wikidata":"https://www.wikidata.org/wiki/Q7250160","display_name":"Propensity score matching","level":2,"score":0.46308642625808716},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.44288498163223267},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.4387114644050598},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.42099428176879883},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.41324010491371155},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37462782859802246},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18118241429328918},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1186753511428833},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"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:10.1145/3543507.3583260","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3543507.3583260","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2023","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G639102501","display_name":null,"funder_award_id":"62102420","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":37,"referenced_works":["https://openalex.org/W1593271688","https://openalex.org/W1994389483","https://openalex.org/W2054141820","https://openalex.org/W2126159342","https://openalex.org/W2152184085","https://openalex.org/W2629213068","https://openalex.org/W2733769132","https://openalex.org/W2739992143","https://openalex.org/W2740167620","https://openalex.org/W2798331900","https://openalex.org/W2798435682","https://openalex.org/W2798713837","https://openalex.org/W2808925008","https://openalex.org/W2947612811","https://openalex.org/W2950275995","https://openalex.org/W2962986764","https://openalex.org/W2964961855","https://openalex.org/W2966349618","https://openalex.org/W2998534896","https://openalex.org/W3034348890","https://openalex.org/W3035523484","https://openalex.org/W3094497946","https://openalex.org/W3097679710","https://openalex.org/W3101366597","https://openalex.org/W3101422495","https://openalex.org/W3103310105","https://openalex.org/W3115643957","https://openalex.org/W3153682915","https://openalex.org/W3153906321","https://openalex.org/W3154587251","https://openalex.org/W3156622960","https://openalex.org/W3156939347","https://openalex.org/W3164238513","https://openalex.org/W3164548985","https://openalex.org/W3168738558","https://openalex.org/W3170713142","https://openalex.org/W4224950663"],"related_works":["https://openalex.org/W2026576563","https://openalex.org/W3196761963","https://openalex.org/W2180954594","https://openalex.org/W2052835778","https://openalex.org/W2036193982","https://openalex.org/W213628847","https://openalex.org/W2065417422","https://openalex.org/W101468167","https://openalex.org/W1641372354","https://openalex.org/W2434094746"],"abstract_inverted_index":{"Explainable":[0],"recommendation":[1,76,145],"has":[2],"recently":[3],"attracted":[4],"increasing":[5],"attention":[6],"from":[7,140],"both":[8],"academic":[9],"and":[10,31,81,101,130,143,176,186],"industry":[11],"communities.":[12],"Among":[13],"different":[14],"explainable":[15,75],"strategies,":[16],"generating":[17],"natural":[18],"language":[19],"explanations":[20,33],"is":[21,121],"an":[22,96],"important":[23],"method,":[24],"which":[25,52],"can":[26,53,147],"deliver":[27],"more":[28,170],"informative,":[29],"flexible":[30],"readable":[32],"to":[34,57,87,205],"facilitate":[35],"better":[36],"user":[37,188],"decisions.":[38],"Despite":[39],"the":[40,49,58,72,108,113,119,128,137,141,158,163,167,178,184,197,207],"effectiveness,":[41],"existing":[42],"models":[43],"are":[44],"mostly":[45],"optimized":[46],"based":[47,111,200],"on":[48,112,201],"observed":[50],"datasets,":[51],"be":[54,148],"skewed":[55],"due":[56],"selection":[59],"or":[60],"exposure":[61],"bias.":[62],"To":[63],"alleviate":[64],"this":[65,68],"problem,":[66],"in":[67,133],"paper,":[69],"we":[70,93,150,173],"formulate":[71],"task":[73],"of":[74,209],"with":[77,196],"a":[78,83,104,122,152],"causal":[79],"graph,":[80],"design":[82],"causality":[84],"enhanced":[85],"framework":[86],"generate":[88],"unbiased":[89,98],"explanations.":[90],"More":[91],"specifically,":[92],"firstly":[94],"define":[95],"ideal":[97,131],"learning":[99],"objective,":[100],"then":[102],"derive":[103],"tractable":[105],"loss":[106,129,160],"for":[107,126],"observational":[109],"data":[110],"inverse":[114],"propensity":[115],"score":[116],"(IPS),":[117],"where":[118],"key":[120],"sample":[123,164],"re-weighting":[124],"strategy":[125],"equalizing":[127],"objective":[132],"expectation.":[134],"Considering":[135],"that":[136],"IPS":[138],"estimated":[139],"sparse":[142],"noisy":[144],"datasets":[146,204],"inaccurate,":[149],"introduce":[151],"fault":[153],"tolerant":[154],"mechanism":[155],"by":[156,162,183,194],"minimizing":[157],"maximum":[159],"induced":[161,182],"weights":[165],"near":[166],"IPS.":[168],"For":[169],"comprehensive":[171],"modeling,":[172],"further":[174],"analyze":[175],"infer":[177],"potential":[179],"latent":[180],"confounders":[181],"complex":[185],"diverse":[187],"personalities.":[189],"We":[190],"conduct":[191],"extensive":[192],"experiments":[193],"comparing":[195],"state-of-the-art":[198],"methods":[199],"three":[202],"real-world":[203],"demonstrate":[206],"effectiveness":[208],"our":[210],"method.":[211]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":2}],"updated_date":"2026-05-21T09:19:25.381259","created_date":"2025-10-10T00:00:00"}