{"id":"https://openalex.org/W2898085636","doi":"https://doi.org/10.1145/3357384.3357925","title":"AutoInt","display_name":"AutoInt","publication_year":2019,"publication_date":"2019-11-03","ids":{"openalex":"https://openalex.org/W2898085636","doi":"https://doi.org/10.1145/3357384.3357925","mag":"2898085636"},"language":"en","primary_location":{"id":"doi:10.1145/3357384.3357925","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3357384.3357925","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1810.11921","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Weiping Song","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weiping Song","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chence Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chence Shi","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhiping Xiao","orcid":null},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhiping Xiao","raw_affiliation_strings":["University of California, Los Angeles, Los Angeles, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Los Angeles, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhijian Duan","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhijian Duan","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yewen Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yewen Xu","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ming Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Zhang","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jian Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164802","display_name":"Mila - Quebec Artificial Intelligence Institute","ror":"https://ror.org/05c22rx21","country_code":"CA","type":"facility","lineage":["https://openalex.org/I4210164802"]},{"id":"https://openalex.org/I108192572","display_name":"HEC Montr\u00e9al","ror":"https://ror.org/05ww3wq27","country_code":"CA","type":"education","lineage":["https://openalex.org/I108192572"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jian Tang","raw_affiliation_strings":["Mila-Quebec AI Institute, HEC Montreal &amp; CIFAR AI Chair, Montreal, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mila-Quebec AI Institute, HEC Montreal &amp; CIFAR AI Chair, Montreal, Canada","institution_ids":["https://openalex.org/I4210164802","https://openalex.org/I108192572"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":88.8551,"has_fulltext":false,"cited_by_count":724,"citation_normalized_percentile":{"value":0.99944753,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1161","last_page":"1170"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9936000108718872,"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":0.9761999845504761,"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/categorical-variable","display_name":"Categorical variable","score":0.7742000222206116},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6051999926567078},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.501800000667572},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4643999934196472},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.42559999227523804},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.42170000076293945},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.3709999918937683},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.35989999771118164}],"concepts":[{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.7742000222206116},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7633000016212463},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6051999926567078},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5649999976158142},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.501800000667572},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4643999934196472},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4401000142097473},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4352000057697296},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.42559999227523804},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.42170000076293945},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.3709999918937683},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.35030001401901245},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32839998602867126},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3199000060558319},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.31790000200271606},{"id":"https://openalex.org/C101814296","wikidata":"https://www.wikidata.org/wiki/Q5439685","display_name":"Feature model","level":3,"score":0.2935999929904938},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C115908005","wikidata":"https://www.wikidata.org/wiki/Q2668364","display_name":"Combinatorial explosion","level":2,"score":0.25360000133514404},{"id":"https://openalex.org/C2777851325","wikidata":"https://www.wikidata.org/wiki/Q7094102","display_name":"Online model","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3357384.3357925","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3357384.3357925","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1810.11921","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1810.11921","pdf_url":"https://arxiv.org/pdf/1810.11921","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:1810.11921","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1810.11921","pdf_url":"https://arxiv.org/pdf/1810.11921","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1964509623","https://openalex.org/W1971014294","https://openalex.org/W2002834872","https://openalex.org/W2055079831","https://openalex.org/W2074694452","https://openalex.org/W2076618162","https://openalex.org/W2090883204","https://openalex.org/W2171279286","https://openalex.org/W2194775991","https://openalex.org/W2255110919","https://openalex.org/W2295739661","https://openalex.org/W2443960221","https://openalex.org/W2475334473","https://openalex.org/W2509235963","https://openalex.org/W2512971201","https://openalex.org/W2517540742","https://openalex.org/W2548570154","https://openalex.org/W2604242010","https://openalex.org/W2604662567","https://openalex.org/W2605225344","https://openalex.org/W2723293840","https://openalex.org/W2773640334","https://openalex.org/W2788730650","https://openalex.org/W2793768763","https://openalex.org/W2802187397","https://openalex.org/W2908404712","https://openalex.org/W2962965405","https://openalex.org/W2963323306","https://openalex.org/W2963448850","https://openalex.org/W6713134421"],"related_works":[],"abstract_inverted_index":{"Click-through":[0],"rate":[1],"(CTR)":[2],"prediction,":[3],"which":[4,71,135],"aims":[5],"to":[6,22,75,83,119,139,171],"predict":[7],"the":[8,40,44,96,117,122,150,156,174,178,185],"probability":[9],"of":[10,95,126,184,192,195],"a":[11,161],"user":[12,45,47],"clicking":[13],"on":[14,64,208,218],"an":[15,18,60,111,213],"ad":[16],"or":[17],"item,":[19],"is":[20,35,132,169,241],"critical":[21],"many":[23],"online":[24,28],"applications":[25],"such":[26],"as":[27],"advertising":[29],"and":[30,56,58,80,98,102,113,142,152],"recommender":[31],"systems.":[32],"The":[33,201],"problem":[34],"very":[36,73,133],"challenging":[37],"since":[38],"(1)":[39],"input":[41,127,144,196],"features":[42,67,101,154,197],"(e.g.,":[43],"id,":[46,50],"age,":[48],"item":[49,51],"category)":[52],"are":[53,72,81],"usually":[54],"sparse":[55,97],"high-dimensional,":[57],"(2)":[59],"effective":[61,112],"prediction":[62,234],"relies":[63],"high-order":[65,123],"combinatorial":[66],"(a.k.a.":[68],"cross":[69],"features),":[70],"time-consuming":[74],"hand-craft":[76],"by":[77],"domain":[78],"experts":[79],"impossible":[82],"be":[84,137,199,205],"enumerated.":[85],"Therefore,":[86],"there":[87],"have":[88],"been":[89],"efforts":[90],"in":[91,177,212],"finding":[92],"low-dimensional":[93,158,179],"representations":[94],"high-dimensional":[99],"raw":[100,210],"their":[103],"meaningful":[104],"combinations.":[105],"In":[106],"this":[107],"paper,":[108],"we":[109,147],"propose":[110],"efficient":[114],"method":[115],"called":[116],"AutoInt":[118],"automatically":[120],"learn":[121],"feature":[124,175,193],"interactions":[125,176],"features.":[128,145],"Our":[129],"proposed":[130,170,225],"algorithm":[131],"general,":[134],"can":[136,198,204],"applied":[138],"both":[140,149],"numerical":[141,151],"categorical":[143,153],"Specifically,":[146],"map":[148],"into":[155],"same":[157],"space.":[159,180],"Afterwards,":[160],"multi-head":[162,186],"self-attentive":[163,187],"neural":[164,188],"network":[165],"with":[166],"residual":[167],"connections":[168],"explicitly":[172],"model":[173,203],"With":[181],"different":[182,190],"layers":[183],"networks,":[189],"orders":[191],"combinations":[194],"modeled.":[200],"whole":[202],"efficiently":[206],"fit":[207],"large-scale":[209],"data":[211],"end-to-end":[214],"fashion.":[215],"Experimental":[216],"results":[217],"four":[219],"real-world":[220],"datasets":[221],"show":[222],"that":[223],"our":[224],"approach":[226],"not":[227],"only":[228],"outperforms":[229],"existing":[230],"state-of-the-art":[231],"approaches":[232],"for":[233],"but":[235],"also":[236],"offers":[237],"good":[238],"explainability.":[239],"Code":[240],"available":[242],"at:":[243],"\\urlhttps://github.com/DeepGraphLearning/RecommenderSystems.":[244]},"counts_by_year":[{"year":2026,"cited_by_count":24},{"year":2025,"cited_by_count":151},{"year":2024,"cited_by_count":147},{"year":2023,"cited_by_count":150},{"year":2022,"cited_by_count":120},{"year":2021,"cited_by_count":88},{"year":2020,"cited_by_count":42},{"year":2019,"cited_by_count":2}],"updated_date":"2026-05-24T08:33:08.758527","created_date":"2018-11-02T00:00:00"}
