{"id":"https://openalex.org/W3208642157","doi":"https://doi.org/10.1145/3459637.3482234","title":"AutoIAS","display_name":"AutoIAS","publication_year":2021,"publication_date":"2021-10-26","ids":{"openalex":"https://openalex.org/W3208642157","doi":"https://doi.org/10.1145/3459637.3482234","mag":"3208642157"},"language":"en","primary_location":{"id":"doi:10.1145/3459637.3482234","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482234","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","raw_type":"proceedings-article"},"type":"conference-paper","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/A5058804491","display_name":"Wei Zhikun","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhikun Wei","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022927606","display_name":"Xin Wang","orcid":"https://orcid.org/0000-0002-0351-2939"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Wang","raw_affiliation_strings":["Tsinghua University & Pengcheng Laboratory, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University & Pengcheng Laboratory, Beijing, China","institution_ids":["https://openalex.org/I4210136793","https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100339293","display_name":"Wenwu Zhu","orcid":"https://orcid.org/0000-0003-2236-9290"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenwu Zhu","raw_affiliation_strings":["Tsinghua University & Pengcheng Laboratory, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University & Pengcheng Laboratory, Beijing, China","institution_ids":["https://openalex.org/I4210136793","https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.5567,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.91299767,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2101","last_page":"2110"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9994999766349792,"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":0.9994999766349792,"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/T10028","display_name":"Topic Modeling","score":0.9980000257492065,"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/T12016","display_name":"Web Data Mining and Analysis","score":0.9973000288009644,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7632368803024292},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.6374655365943909},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.5793327689170837},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.532979428768158},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5297024846076965},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5195013284683228},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.516664981842041},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.48437604308128357},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.46920815110206604},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.41028478741645813},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39878731966018677},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3524170219898224}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7632368803024292},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.6374655365943909},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.5793327689170837},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.532979428768158},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5297024846076965},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5195013284683228},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.516664981842041},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.48437604308128357},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.46920815110206604},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.41028478741645813},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39878731966018677},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3524170219898224},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","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:10.1145/3459637.3482234","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482234","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5299999713897705}],"awards":[{"id":"https://openalex.org/G5866751179","display_name":null,"funder_award_id":"62050110","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8398861722","display_name":null,"funder_award_id":"2020AAA0106301","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1985759455","https://openalex.org/W2074694452","https://openalex.org/W2090883204","https://openalex.org/W2094286023","https://openalex.org/W2295739661","https://openalex.org/W2475334473","https://openalex.org/W2509235963","https://openalex.org/W2548570154","https://openalex.org/W2602856279","https://openalex.org/W2604662567","https://openalex.org/W2793768763","https://openalex.org/W2898085636","https://openalex.org/W2913668833","https://openalex.org/W2963323306","https://openalex.org/W2963924287","https://openalex.org/W2964182926","https://openalex.org/W3012731857","https://openalex.org/W3034528892","https://openalex.org/W3034552531","https://openalex.org/W3041360407","https://openalex.org/W3081190557","https://openalex.org/W3093965394","https://openalex.org/W3101704389","https://openalex.org/W3104030692","https://openalex.org/W3104506006","https://openalex.org/W3195040486","https://openalex.org/W3195406508","https://openalex.org/W3209828932"],"related_works":["https://openalex.org/W2081900870","https://openalex.org/W3162204513","https://openalex.org/W2371138613","https://openalex.org/W2357256365","https://openalex.org/W2048963458","https://openalex.org/W2037549926","https://openalex.org/W43109613","https://openalex.org/W2359952343","https://openalex.org/W2348502264","https://openalex.org/W2239445980"],"abstract_inverted_index":{"Automating":[0],"architecture":[1,92,117,176,183],"design":[2,174],"for":[3,87,138,192],"recommendation":[4],"tasks":[5],"becomes":[6],"a":[7,84,102,106,139,146],"trending":[8],"topic":[9],"because":[10],"expert":[11],"efforts":[12],"are":[13],"saved,":[14],"and":[15,48,61,74,108,132,154,187,203,219],"better":[16],"performance":[17,151,202],"is":[18,24,82,157],"expected.":[19],"Neural":[20],"Architecture":[21,99,221],"Search":[22],"(NAS)":[23],"introduced":[25],"to":[26,76,111,148,162],"discover":[27],"powerful":[28],"CTR":[29,36,89,114,128,141],"prediction":[30,37,90,115,129,142],"model":[31,38,91,116,130],"architectures":[32,131,190],"in":[33,118],"recent":[34],"works.":[35],"usually":[39],"consists":[40],"of":[41,152,206,213],"three":[42],"components:":[43],"embedding":[44],"layer,":[45,47],"interaction":[46],"deep":[49],"neural":[50],"network.":[51],"However,":[52],"existing":[53,126],"automation":[54],"works":[55],"focus":[56],"on":[57,196],"searching":[58,68],"single":[59],"component":[60],"leaving":[62],"other":[63],"components":[64,73,186],"hand-crafted.":[65],"The":[66],"isolated":[67],"will":[69],"cause":[70],"incompatibility":[71],"among":[72,165,185],"lead":[75],"weak":[77],"generalization":[78,204],"ability.":[79],"Moreover,":[80],"there":[81],"not":[83],"unified":[85],"framework":[86,103],"integrated":[88,135],"searching.":[93],"This":[94],"paper":[95],"presents":[96],"Automatic":[97],"Integrated":[98],"Searcher":[100],"(AutoIAS),":[101],"that":[104,179],"provides":[105],"practical":[107],"general":[109],"method":[110,218],"find":[112],"optimal":[113],"an":[119,134,175],"automatic":[120],"manner.":[121],"In":[122],"AutoIAS,":[123],"we":[124,173],"unify":[125],"interaction-based":[127],"propose":[133],"search":[136,171],"space":[137],"complete":[140],"model.":[143],"We":[144],"utilize":[145],"supernet":[147,156,216],"predict":[149],"the":[150,155,170,182,200,211,214,220],"sub-architectures,":[153],"trained":[158],"with":[159],"Knowledge":[160],"Distillation(KD)":[161],"enhance":[163],"consistency":[164],"sub-architectures.":[166],"To":[167],"efficiently":[168],"explore":[169],"space,":[172],"generator":[177],"network":[178],"explicitly":[180],"models":[181],"dependencies":[184],"generates":[188],"conditioned":[189],"distribution":[191],"each":[193],"component.":[194],"Experiments":[195],"public":[197],"datasets":[198],"show":[199],"outstanding":[201],"ability":[205],"AutoIAS.":[207],"Ablation":[208],"study":[209],"shows":[210],"effectiveness":[212],"KD-based":[215],"training":[217],"Generator":[222],"Network.":[223]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2021-11-08T00:00:00"}