{"id":"https://openalex.org/W3197577391","doi":"https://doi.org/10.1145/3471158.3472241","title":"Extracting per Query Valid Explanations for Blackbox Learning-to-Rank Models","display_name":"Extracting per Query Valid Explanations for Blackbox Learning-to-Rank Models","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3197577391","doi":"https://doi.org/10.1145/3471158.3472241","mag":"3197577391"},"language":"en","primary_location":{"id":"doi:10.1145/3471158.3472241","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3471158.3472241","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval","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/A5100714939","display_name":"Jaspreet Singh","orcid":"https://orcid.org/0000-0003-4364-2487"},"institutions":[{"id":"https://openalex.org/I4210089985","display_name":"Amazon (Germany)","ror":"https://ror.org/00b9ktm87","country_code":"DE","type":"company","lineage":["https://openalex.org/I1311688040","https://openalex.org/I4210089985"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jaspreet Singh","raw_affiliation_strings":["Amazon, Berlin, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon, Berlin, Germany","institution_ids":["https://openalex.org/I4210089985"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027689420","display_name":"Megha Khosla","orcid":"https://orcid.org/0000-0002-0319-3181"},"institutions":[{"id":"https://openalex.org/I4210136150","display_name":"L3S Research Center","ror":"https://ror.org/039t4wk02","country_code":"DE","type":"facility","lineage":["https://openalex.org/I114112103","https://openalex.org/I4210136150","https://openalex.org/I94509681"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Megha Khosla","raw_affiliation_strings":["L3S Research Center, Hannover, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"L3S Research Center, Hannover, Germany","institution_ids":["https://openalex.org/I4210136150"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070316143","display_name":"Zhenye Wang","orcid":"https://orcid.org/0009-0003-2242-455X"},"institutions":[{"id":"https://openalex.org/I4210136150","display_name":"L3S Research Center","ror":"https://ror.org/039t4wk02","country_code":"DE","type":"facility","lineage":["https://openalex.org/I114112103","https://openalex.org/I4210136150","https://openalex.org/I94509681"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Wang Zhenye","raw_affiliation_strings":["L3S Research Center, Hannover, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"L3S Research Center, Hannover, Germany","institution_ids":["https://openalex.org/I4210136150"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075681290","display_name":"Avishek Anand","orcid":"https://orcid.org/0000-0002-0163-0739"},"institutions":[{"id":"https://openalex.org/I4210136150","display_name":"L3S Research Center","ror":"https://ror.org/039t4wk02","country_code":"DE","type":"facility","lineage":["https://openalex.org/I114112103","https://openalex.org/I4210136150","https://openalex.org/I94509681"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Avishek Anand","raw_affiliation_strings":["L3S Research Center, Hannover, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"L3S Research Center, Hannover, Germany","institution_ids":["https://openalex.org/I4210136150"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3993,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.85079913,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"203","last_page":"210"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.994700014591217,"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"}},"topics":[{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.994700014591217,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9905999898910522,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9872000217437744,"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/computer-science","display_name":"Computer science","score":0.7536590099334717},{"id":"https://openalex.org/keywords/pointwise","display_name":"Pointwise","score":0.7172333598136902},{"id":"https://openalex.org/keywords/completeness","display_name":"Completeness (order theory)","score":0.6674864292144775},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.6242977976799011},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.6242456436157227},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5782216787338257},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.5774169564247131},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5622602701187134},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.5167993307113647},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49982452392578125},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.36428403854370117},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1634785532951355}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7536590099334717},{"id":"https://openalex.org/C2777984123","wikidata":"https://www.wikidata.org/wiki/Q9248237","display_name":"Pointwise","level":2,"score":0.7172333598136902},{"id":"https://openalex.org/C17231256","wikidata":"https://www.wikidata.org/wiki/Q5156540","display_name":"Completeness (order theory)","level":2,"score":0.6674864292144775},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6242977976799011},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6242456436157227},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5782216787338257},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.5774169564247131},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5622602701187134},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.5167993307113647},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49982452392578125},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.36428403854370117},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1634785532951355},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3471158.3472241","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3471158.3472241","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W27255796","https://openalex.org/W1530210183","https://openalex.org/W1875482710","https://openalex.org/W2118022153","https://openalex.org/W2143331230","https://openalex.org/W2516809705","https://openalex.org/W2618851150","https://openalex.org/W2891482011","https://openalex.org/W2955573083","https://openalex.org/W2962807820","https://openalex.org/W2963259708","https://openalex.org/W2970222187","https://openalex.org/W2994120362","https://openalex.org/W2997560917","https://openalex.org/W3013870856","https://openalex.org/W3124792046","https://openalex.org/W4226251537"],"related_works":["https://openalex.org/W2767338541","https://openalex.org/W2112815677","https://openalex.org/W2606864227","https://openalex.org/W2429267751","https://openalex.org/W2011472225","https://openalex.org/W3000057026","https://openalex.org/W3048565508","https://openalex.org/W2069165070","https://openalex.org/W3163984363","https://openalex.org/W3127142483"],"abstract_inverted_index":{"Learning-to-rank":[0],"(LTR)":[1],"is":[2],"a":[3,16,33,71,81,94,120,127],"class":[4],"of":[5,19,26,35,44,60,84,101,105,116,122],"supervised":[6],"learning":[7,48,61],"techniques":[8],"that":[9,54,63,77,147],"apply":[10],"to":[11,79,89,131],"ranking":[12],"problems":[13],"dealing":[14],"with":[15,56],"large":[17],"number":[18],"features.":[20],"The":[21],"popularity":[22],"and":[23,46,103,135,159],"widespread":[24],"application":[25],"LTR":[27,161],"models":[28,162],"in":[29,32,41,163],"prioritizing":[30],"information":[31],"variety":[34],"domains":[36],"makes":[37],"their":[38],"scrutability":[39],"vital":[40],"today's":[42],"landscape":[43],"fair":[45],"transparent":[47],"systems.":[49],"However,":[50],"limited":[51],"work":[52],"exists":[53],"deals":[55],"interpreting":[57],"the":[58,90,112],"decisions":[59],"systems":[62],"output":[64,92],"rankings.":[65],"In":[66,141],"this":[67],"paper":[68],"we":[69,145],"propose":[70,136],"model":[72,152],"agnostic":[73,153],"local":[74],"explanation":[75,88,154],"method":[76],"seeks":[78],"identify":[80],"small":[82],"subset":[83],"input":[85],"features":[86],"as":[87,119,139],"ranked":[91],"for":[93,108],"given":[95],"query.":[96],"We":[97,125],"introduce":[98],"new":[99],"notions":[100],"validity":[102,133,164],"completeness":[104],"explanations":[106],"specifically":[107],"rankings,":[109],"based":[110],"on":[111,168],"presence":[113],"or":[114],"absence":[115],"selected":[117],"features,":[118],"way":[121],"measuring":[123],"goodness.":[124],"devise":[126],"novel":[128],"optimization":[129],"problem":[130],"maximize":[132],"directly":[134],"greedy":[137],"algorithms":[138],"solutions.":[140],"extensive":[142],"quantitative":[143],"experiments":[144],"show":[146],"our":[148],"approach":[149],"outperforms":[150],"other":[151],"approaches":[155],"across":[156],"pointwise,":[157],"pairwise":[158],"listwise":[160],"while":[165],"not":[166],"compromising":[167],"completeness.":[169]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":5}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}