{"id":"https://openalex.org/W2517540742","doi":"https://doi.org/10.1145/2939672.2939704","title":"Deep Crossing","display_name":"Deep Crossing","publication_year":2016,"publication_date":"2016-08-08","ids":{"openalex":"https://openalex.org/W2517540742","doi":"https://doi.org/10.1145/2939672.2939704","mag":"2517540742"},"language":"en","primary_location":{"id":"doi:10.1145/2939672.2939704","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2939672.2939704","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","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/A5102004349","display_name":"Ying Shan","orcid":"https://orcid.org/0000-0001-7673-8325"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ying Shan","raw_affiliation_strings":["Microsoft Corporation, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Corporation, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053059276","display_name":"T. Ryan Hoens","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"T. Ryan Hoens","raw_affiliation_strings":["Microsoft Corporation, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Corporation, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074199611","display_name":"Jian Jiao","orcid":"https://orcid.org/0000-0003-4779-9588"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jian Jiao","raw_affiliation_strings":["Microsoft Corporation, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Corporation, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101939308","display_name":"Haijing Wang","orcid":"https://orcid.org/0000-0002-3753-5709"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haijing Wang","raw_affiliation_strings":["Microsoft Corporation, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Corporation, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034476404","display_name":"Dong Yu","orcid":"https://orcid.org/0000-0003-0520-6844"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dong Yu","raw_affiliation_strings":["Microsoft Corporation, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Corporation, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112346466","display_name":"JC Mao","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"JC Mao","raw_affiliation_strings":["Microsoft Corporation, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Corporation, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5102004349"],"corresponding_institution_ids":["https://openalex.org/I1290206253"],"apc_list":null,"apc_paid":null,"fwci":15.7305,"has_fulltext":false,"cited_by_count":414,"citation_normalized_percentile":{"value":0.99355245,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"255","last_page":"262"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.996999979019165,"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"}},"topics":[{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.996999979019165,"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"}},{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T11181","display_name":"Advanced Data Storage Technologies","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.8353321552276611},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7324350476264954},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6088840961456299},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4918506145477295},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.47961461544036865},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45738476514816284},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.43809816241264343},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4287565350532532},{"id":"https://openalex.org/keywords/scratch","display_name":"Scratch","score":0.42729657888412476},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.41607239842414856},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33443766832351685},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32693904638290405},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1467665433883667}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8353321552276611},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7324350476264954},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6088840961456299},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4918506145477295},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.47961461544036865},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45738476514816284},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.43809816241264343},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4287565350532532},{"id":"https://openalex.org/C2781235140","wikidata":"https://www.wikidata.org/wiki/Q275131","display_name":"Scratch","level":2,"score":0.42729657888412476},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.41607239842414856},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33443766832351685},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32693904638290405},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1467665433883667},{"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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2939672.2939704","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2939672.2939704","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6299999952316284}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1975392791","https://openalex.org/W2014506068","https://openalex.org/W2076063813","https://openalex.org/W2093866254","https://openalex.org/W2101926813","https://openalex.org/W2112796928","https://openalex.org/W2124386111","https://openalex.org/W2136189984","https://openalex.org/W2136922672","https://openalex.org/W2147768505","https://openalex.org/W2158899491","https://openalex.org/W2160306971","https://openalex.org/W2160815625","https://openalex.org/W2161742217","https://openalex.org/W2163605009","https://openalex.org/W2181607856","https://openalex.org/W2186845332","https://openalex.org/W2194775991","https://openalex.org/W2295739661","https://openalex.org/W2394932179","https://openalex.org/W2405883473","https://openalex.org/W2604272474","https://openalex.org/W2950133940","https://openalex.org/W2997617958","https://openalex.org/W3124857404"],"related_works":["https://openalex.org/W2475116013","https://openalex.org/W2770018148","https://openalex.org/W2358308169","https://openalex.org/W2066741154","https://openalex.org/W2385135707","https://openalex.org/W2082556335","https://openalex.org/W4377865163","https://openalex.org/W3193857078","https://openalex.org/W3208304128","https://openalex.org/W3000197790"],"abstract_inverted_index":{"Manually":[0],"crafted":[1,26],"combinatorial":[2],"features":[3,22,27,50,65,76,146],"have":[4],"been":[5],"the":[6,17,37,81,109,145,149,154,175,184],"\"secret":[7],"sauce\"":[8],"behind":[9],"many":[10],"successful":[11],"models.":[12,54,151],"For":[13],"web-scale":[14,128],"applications,":[15],"however,":[16],"variety":[18],"and":[19,32,89,136,189],"volume":[20],"of":[21,57,63,86,97,144,156,177,183,191],"make":[23],"these":[24],"manually":[25],"expensive":[28],"to":[29,51,123,165,172],"create,":[30],"maintain,":[31],"deploy.":[33],"This":[34,152],"paper":[35],"proposes":[36],"Deep":[38,58,100,158],"Crossing":[39,59,101,159],"model":[40],"which":[41,83],"is":[42,60,102],"a":[43,61,95,105,117,131,142,161,181],"deep":[44,192],"neural":[45],"network":[46],"that":[47,66],"automatically":[48],"combines":[49],"produce":[52],"superior":[53,138],"The":[55,73],"input":[56],"set":[62],"individual":[64],"can":[67],"be":[68],"either":[69],"dense":[70],"or":[71],"sparse.":[72],"important":[74],"crossing":[75],"are":[77,84],"discovered":[78],"implicitly":[79],"by":[80,116],"networks,":[82],"comprised":[85],"an":[87],"embedding":[88],"stacking":[90],"layer,":[91],"as":[92,94,160,169,171],"well":[93,170],"cascade":[96],"Residual":[98],"Units.":[99],"implemented":[103],"with":[104,140,180],"modeling":[106,163],"tool":[107],"called":[108],"Computational":[110],"Network":[111],"Tool":[112],"Kit":[113],"(CNTK),":[114],"powered":[115],"multi-GPU":[118],"platform.":[119],"It":[120],"was":[121],"able":[122],"build,":[124],"from":[125],"scratch,":[126],"two":[127],"models":[129,179],"for":[130],"major":[132],"paid":[133],"search":[134],"engine,":[135],"achieve":[137],"results":[139],"only":[141],"sub-set":[143],"used":[147],"in":[148,186],"production":[150],"demonstrates":[153],"potential":[155],"using":[157],"general":[162],"paradigm":[164],"improve":[166],"existing":[167],"products,":[168],"speed":[173],"up":[174],"development":[176],"new":[178],"fraction":[182],"investment":[185],"feature":[187],"engineering":[188],"acquisition":[190],"domain":[193],"knowledge.":[194]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":30},{"year":2024,"cited_by_count":40},{"year":2023,"cited_by_count":55},{"year":2022,"cited_by_count":44},{"year":2021,"cited_by_count":86},{"year":2020,"cited_by_count":61},{"year":2019,"cited_by_count":52},{"year":2018,"cited_by_count":31},{"year":2017,"cited_by_count":10}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2016-09-16T00:00:00"}
