{"id":"https://openalex.org/W4205333464","doi":"https://doi.org/10.1109/bigdata52589.2021.9671870","title":"NEAT: A Label Noise-resistant Complementary Item Recommender System with Trustworthy Evaluation","display_name":"NEAT: A Label Noise-resistant Complementary Item Recommender System with Trustworthy Evaluation","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4205333464","doi":"https://doi.org/10.1109/bigdata52589.2021.9671870"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671870","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671870","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2202.05456","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013776504","display_name":"Luyi Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I1330693074","display_name":"Walmart (United States)","ror":"https://ror.org/04j0gge90","country_code":"US","type":"company","lineage":["https://openalex.org/I1330693074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Luyi Ma","raw_affiliation_strings":["Walmart Global Tech, Sunnyvale, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Walmart Global Tech, Sunnyvale, CA","institution_ids":["https://openalex.org/I1330693074"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034805091","display_name":"Jianpeng Xu","orcid":"https://orcid.org/0000-0002-9500-3700"},"institutions":[{"id":"https://openalex.org/I1330693074","display_name":"Walmart (United States)","ror":"https://ror.org/04j0gge90","country_code":"US","type":"company","lineage":["https://openalex.org/I1330693074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianpeng Xu","raw_affiliation_strings":["Walmart Global Tech, Sunnyvale, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Walmart Global Tech, Sunnyvale, CA","institution_ids":["https://openalex.org/I1330693074"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019756064","display_name":"Jason H. D. Cho","orcid":null},"institutions":[{"id":"https://openalex.org/I1330693074","display_name":"Walmart (United States)","ror":"https://ror.org/04j0gge90","country_code":"US","type":"company","lineage":["https://openalex.org/I1330693074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jason H.D. Cho","raw_affiliation_strings":["Walmart Global Tech, Sunnyvale, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Walmart Global Tech, Sunnyvale, CA","institution_ids":["https://openalex.org/I1330693074"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025613254","display_name":"Evren K\u00f6rpeo\u011flu","orcid":null},"institutions":[{"id":"https://openalex.org/I1330693074","display_name":"Walmart (United States)","ror":"https://ror.org/04j0gge90","country_code":"US","type":"company","lineage":["https://openalex.org/I1330693074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Evren Korpeoglu","raw_affiliation_strings":["Walmart Global Tech, Sunnyvale, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Walmart Global Tech, Sunnyvale, CA","institution_ids":["https://openalex.org/I1330693074"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103326324","display_name":"Sushant Kumar","orcid":"https://orcid.org/0009-0000-5643-5263"},"institutions":[{"id":"https://openalex.org/I1330693074","display_name":"Walmart (United States)","ror":"https://ror.org/04j0gge90","country_code":"US","type":"company","lineage":["https://openalex.org/I1330693074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sushant Kumar","raw_affiliation_strings":["Walmart Global Tech, Sunnyvale, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Walmart Global Tech, Sunnyvale, CA","institution_ids":["https://openalex.org/I1330693074"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079669827","display_name":"Kannan Achan","orcid":"https://orcid.org/0009-0000-9186-3175"},"institutions":[{"id":"https://openalex.org/I1330693074","display_name":"Walmart (United States)","ror":"https://ror.org/04j0gge90","country_code":"US","type":"company","lineage":["https://openalex.org/I1330693074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kannan Achan","raw_affiliation_strings":["Walmart Global Tech, Sunnyvale, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Walmart Global Tech, Sunnyvale, CA","institution_ids":["https://openalex.org/I1330693074"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1330693074"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9929999709129333,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9824000000953674,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.7578766345977783},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.7281455397605896},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.549023449420929},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5453769564628601},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4822145700454712},{"id":"https://openalex.org/keywords/multiset","display_name":"Multiset","score":0.44443243741989136},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.43857526779174805},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4339512586593628},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4285636246204376},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.42491084337234497},{"id":"https://openalex.org/keywords/database-transaction","display_name":"Database transaction","score":0.4162985682487488},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.404114693403244},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35982492566108704},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19377505779266357},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1829579770565033},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.1427496373653412}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7578766345977783},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.7281455397605896},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.549023449420929},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5453769564628601},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4822145700454712},{"id":"https://openalex.org/C2779623528","wikidata":"https://www.wikidata.org/wiki/Q864377","display_name":"Multiset","level":2,"score":0.44443243741989136},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.43857526779174805},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4339512586593628},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4285636246204376},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.42491084337234497},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.4162985682487488},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.404114693403244},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35982492566108704},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19377505779266357},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1829579770565033},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.1427496373653412},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671870","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671870","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2202.05456","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2202.05456","pdf_url":"https://arxiv.org/pdf/2202.05456","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2202.05456","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2202.05456","pdf_url":"https://arxiv.org/pdf/2202.05456","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1991418309","https://openalex.org/W2031648200","https://openalex.org/W2101409192","https://openalex.org/W2127265454","https://openalex.org/W2140310134","https://openalex.org/W2605350416","https://openalex.org/W2739749877","https://openalex.org/W2782696945","https://openalex.org/W2783272285","https://openalex.org/W2892485145","https://openalex.org/W2896962583","https://openalex.org/W2963367478","https://openalex.org/W2964341035","https://openalex.org/W2964691483","https://openalex.org/W2965316489","https://openalex.org/W2984100107","https://openalex.org/W3003413895","https://openalex.org/W3031029974","https://openalex.org/W3034844787","https://openalex.org/W3093133157","https://openalex.org/W3094242471","https://openalex.org/W3101157305","https://openalex.org/W3139159537","https://openalex.org/W4294170691","https://openalex.org/W6678984984","https://openalex.org/W6680830989","https://openalex.org/W6682443497","https://openalex.org/W6682691769","https://openalex.org/W6785459916"],"related_works":["https://openalex.org/W4300455649","https://openalex.org/W2296588302","https://openalex.org/W4328119206","https://openalex.org/W2033245919","https://openalex.org/W4252409532","https://openalex.org/W2006067547","https://openalex.org/W3143753676","https://openalex.org/W2954483618","https://openalex.org/W9228044","https://openalex.org/W2770810599"],"abstract_inverted_index":{"The":[0],"complementary":[1,8,28,48,54,72,129,162,246],"item":[2,20,178,196,247],"recommender":[3],"system":[4],"(CIRS)":[5],"recommends":[6],"the":[7,19,27,32,38,53,61,87,106,110,120,127,133,140,145,155,158,161,167,170,185,191,201,204,229,234,239,251],"items":[9,45,64,68,93,149],"for":[10,122],"a":[11,24,47,57,151,180,217],"given":[12],"query":[13],"item.":[14],"Existing":[15],"CIRS":[16],"models":[17],"consider":[18],"co-purchase":[21,99,141],"signal":[22],"as":[23,56,102,150,179],"proxy":[25],"of":[26,34,60,139,147,188,203,241],"relationship,":[29],"due":[30],"to":[31,73,215],"lack":[33],"human-curated":[35],"labels":[36,103,121,206],"from":[37,136,160,169,195],"huge":[39],"transaction":[40],"records.":[41],"These":[42],"methods":[43],"represent":[44,176],"in":[46,245],"embedding":[49,182],"space":[50],"and":[51,83,164,183,193,233],"model":[52,107,113,144,244],"relationship":[55],"point":[58],"estimation":[59],"similarity":[62],"between":[63],"vectors.":[65],"However,":[66],"co-purchased":[67],"are":[69,94,124],"not":[70,95,116,125],"necessarily":[71],"each":[74,177],"other.":[75],"For":[76],"example,":[77],"customers":[78],"may":[79],"frequently":[80],"purchase":[81],"bananas":[82],"bottle":[84],"water":[85],"within":[86],"same":[88],"transaction,":[89],"but":[90],"these":[91],"two":[92,148],"complementary.":[96],"Hence,":[97],"using":[98],"signals":[100],"directly":[101],"will":[104,115],"aggravate":[105],"performance.":[108],"On":[109],"other":[111],"hand,":[112],"evaluation":[114,123],"be":[117],"trustworthy":[118,218],"if":[119],"reflecting":[126],"true":[128],"relatedness.":[130],"To":[131,172,199],"address":[132],"above":[134],"challenges":[135],"noisy":[137,205],"labeling":[138],"data,":[142],"we":[143,175,209],"co-purchases":[146,159,168,189],"Gaussian":[152,181,186,197],"distribution,":[153],"where":[154],"mean":[156],"denotes":[157,166],"relatedness,":[163],"covariance":[165],"noise.":[171],"do":[173],"so,":[174],"parameterize":[184],"distribution":[187],"by":[190],"means":[192],"covariances":[194],"embedding.":[198],"reduce":[200],"impact":[202],"during":[207],"evaluation,":[208],"propose":[210],"an":[211],"independence":[212],"test-based":[213],"method":[214],"generate":[216],"label":[219],"set":[220],"with":[221,250],"certain":[222],"confidence.":[223],"Our":[224],"extensive":[225],"experiments":[226],"on":[227],"both":[228],"publicly":[230],"available":[231],"dataset":[232,237],"large-scale":[235],"real-world":[236],"justify":[238],"effectiveness":[240],"our":[242],"proposed":[243],"recommendations":[248],"compared":[249],"state-of-the-art":[252],"models.":[253]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
