{"id":"https://openalex.org/W3094609756","doi":"https://doi.org/10.1145/3340531.3417445","title":"Large Scale Long-tailed Product Recognition System at Alibaba","display_name":"Large Scale Long-tailed Product Recognition System at Alibaba","publication_year":2020,"publication_date":"2020-10-19","ids":{"openalex":"https://openalex.org/W3094609756","doi":"https://doi.org/10.1145/3340531.3417445","mag":"3094609756"},"language":"en","primary_location":{"id":"doi:10.1145/3340531.3417445","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3340531.3417445","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Information &amp; Knowledge Management","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/2102.04652","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xiangzeng Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangzeng Zhou","raw_affiliation_strings":["Damo Academy, Alibaba Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Damo Academy, Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Pan Pan","orcid":null},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pan Pan","raw_affiliation_strings":["Damo Academy, Alibaba Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Damo Academy, Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yun Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yun Zheng","raw_affiliation_strings":["Damo Academy, Alibaba Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Damo Academy, Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yinghui Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinghui Xu","raw_affiliation_strings":["Damo Academy, Alibaba Group, Hanzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Damo Academy, Alibaba Group, Hanzhou, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"last","author":{"id":null,"display_name":"Rong Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rong Jin","raw_affiliation_strings":["Damo Academy, Alibaba Group, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Damo Academy, Alibaba Group, Hangzhou, China","institution_ids":["https://openalex.org/I45928872"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I45928872"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3353","last_page":"3356"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9965000152587891,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9965000152587891,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9945999979972839,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.989799976348877,"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/embedding","display_name":"Embedding","score":0.6029999852180481},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5758000016212463},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5385000109672546},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49459999799728394},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.48739999532699585},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.48100000619888306},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4325999915599823},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.41119998693466187},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4016000032424927}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6782000064849854},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6029999852180481},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5863999724388123},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5758000016212463},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5385000109672546},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49459999799728394},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.48739999532699585},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48570001125335693},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.48100000619888306},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4325999915599823},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.41119998693466187},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4016000032424927},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3790999948978424},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3573000133037567},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.3562999963760376},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.3465999960899353},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.34619998931884766},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31130000948905945},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.30379998683929443},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.288100004196167},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.27889999747276306},{"id":"https://openalex.org/C65236422","wikidata":"https://www.wikidata.org/wiki/Q173740","display_name":"Cartesian product","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.25600001215934753}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3340531.3417445","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3340531.3417445","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2102.04652","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2102.04652","pdf_url":"https://arxiv.org/pdf/2102.04652","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:2102.04652","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2102.04652","pdf_url":"https://arxiv.org/pdf/2102.04652","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":3,"referenced_works":["https://openalex.org/W2087787741","https://openalex.org/W2964050365","https://openalex.org/W6649990292"],"related_works":[],"abstract_inverted_index":{"A":[0,119],"practical":[1,198],"large":[2,75,158,199],"scale":[3,76,159,200],"product":[4,23,201],"recognition":[5,78,202],"system":[6,80,180],"suffers":[7],"from":[8,133,166],"the":[9,17,48,62,84,90,96,115,173,177,183,188,206],"phenomenon":[10],"of":[11,28,58,60,163,176,215],"long-tailed":[12],"imbalanced":[13],"training":[14,137,145],"data":[15,138,146],"under":[16],"E-commercial":[18],"circumstance":[19],"at":[20,25,193],"Alibaba.":[21],"Besides":[22],"images":[24],"Alibaba,":[26,194],"plenty":[27],"image":[29,91],"related":[30,92],"side":[31,63,72,93,117],"information":[32,39,73],"(e.g.":[33],"title,":[34],"tags)":[35],"reveal":[36],"rich":[37],"semantic":[38,112,123],"about":[40],"images.":[41],"Prior":[42],"works":[43],"mainly":[44],"focus":[45],"on":[46,155],"addressing":[47],"long":[49,85,184],"tail":[50,86,185],"problem":[51,87],"in":[52,149,181],"visual":[53,77,120,189],"perspective":[54],"only,":[55],"but":[56],"lack":[57],"consideration":[59],"leveraging":[61,89],"information.":[64,94,118],"In":[65,95,187],"this":[66],"paper,":[67],"we":[68,100,195],"present":[69],"a":[70,103,111,197,212],"novel":[71],"based":[74],"co-training~(SICoT)":[79],"to":[81,109,130,141,169],"deal":[82],"with":[83,135,143],"by":[88,205],"proposed":[97,178,207],"co-training":[98,125],"system,":[99,209],"firstly":[101],"introduce":[102],"bilinear":[104],"word":[105],"attention":[106],"module":[107],"aiming":[108],"construct":[110],"embedding":[113,124],"over":[114],"noisy":[116],"feature":[121],"and":[122,210],"scheme":[126],"is":[127],"then":[128],"designed":[129],"transfer":[131],"knowledge":[132],"classes":[134,142,164],"abundant":[136],"(head":[139],"classes)":[140,148],"few":[144],"(tail":[147],"an":[150],"end-to-end":[151],"fashion.":[152],"Extensive":[153],"experiments":[154],"four":[156],"challenging":[157],"datasets,":[160],"whose":[161],"numbers":[162],"range":[165],"one":[167,170],"thousand":[168],"million,":[171],"demonstrate":[172],"scalable":[174],"effectiveness":[175],"SICoT":[179,208],"alleviating":[182],"problem.":[186],"search":[190],"platform":[191],"Pailitao\\footnote{http://www.pailitao.com}":[192],"settle":[196],"application":[203],"driven":[204],"achieve":[211],"significant":[213],"gain":[214],"unique":[216],"visitor~(UV)":[217],"conversion":[218],"rate.":[219]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2020-10-29T00:00:00"}
