{"id":"https://openalex.org/W3036935926","doi":"https://doi.org/10.1145/3394486.3403382","title":"Bootstrapping Complete The Look at Pinterest","display_name":"Bootstrapping Complete The Look at Pinterest","publication_year":2020,"publication_date":"2020-08-20","ids":{"openalex":"https://openalex.org/W3036935926","doi":"https://doi.org/10.1145/3394486.3403382","mag":"3036935926"},"language":"en","primary_location":{"id":"doi:10.1145/3394486.3403382","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3394486.3403382","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403382","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403382","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033598859","display_name":"Eileen Li","orcid":"https://orcid.org/0000-0001-8251-4942"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eileen Li","raw_affiliation_strings":["Pinterest, San Francisco, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pinterest, San Francisco, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100779045","display_name":"Eric Kim","orcid":"https://orcid.org/0000-0001-5242-0291"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eric Kim","raw_affiliation_strings":["Pinterest, San Francisco, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pinterest, San Francisco, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068607680","display_name":"Andrew Zhai","orcid":"https://orcid.org/0009-0007-6081-8727"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andrew Zhai","raw_affiliation_strings":["Pinterest, San Francisco, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pinterest, San Francisco, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016268540","display_name":"Josh Beal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Josh Beal","raw_affiliation_strings":["Pinterest, San Francisco, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pinterest, San Francisco, CA, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069517998","display_name":"Kunlong Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kunlong Gu","raw_affiliation_strings":["Pinterest, San Francisco, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pinterest, San Francisco, CA, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.598,"has_fulltext":true,"cited_by_count":11,"citation_normalized_percentile":{"value":0.74783291,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3299","last_page":"3307"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9987999796867371,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9987999796867371,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9822999835014343,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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":0.803271472454071},{"id":"https://openalex.org/keywords/intuition","display_name":"Intuition","score":0.4854293763637543},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4667634665966034},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.44708189368247986},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.38287195563316345},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.347719669342041}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.803271472454071},{"id":"https://openalex.org/C132010649","wikidata":"https://www.wikidata.org/wiki/Q189222","display_name":"Intuition","level":2,"score":0.4854293763637543},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4667634665966034},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.44708189368247986},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.38287195563316345},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.347719669342041},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3394486.3403382","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3394486.3403382","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403382","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2006.10792","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2006.10792","pdf_url":"https://arxiv.org/pdf/2006.10792","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"doi:10.1145/3394486.3403382","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3394486.3403382","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403382","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.46000000834465027,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3036935926.pdf","grobid_xml":"https://content.openalex.org/works/W3036935926.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W181871703","https://openalex.org/W639708223","https://openalex.org/W2021354639","https://openalex.org/W2027731328","https://openalex.org/W2033365921","https://openalex.org/W2051926867","https://openalex.org/W2100142570","https://openalex.org/W2146851707","https://openalex.org/W2170881581","https://openalex.org/W2200092826","https://openalex.org/W2463470988","https://openalex.org/W2471768434","https://openalex.org/W2549139847","https://openalex.org/W2565639579","https://openalex.org/W2735425839","https://openalex.org/W2748310231","https://openalex.org/W2752782242","https://openalex.org/W2767109396","https://openalex.org/W2772328249","https://openalex.org/W2914911817","https://openalex.org/W2946617802","https://openalex.org/W2949291161","https://openalex.org/W2950628404","https://openalex.org/W2951555918","https://openalex.org/W2951781701","https://openalex.org/W2963775347","https://openalex.org/W2963785012","https://openalex.org/W2963857660","https://openalex.org/W2970971581","https://openalex.org/W3099462466","https://openalex.org/W3100153382","https://openalex.org/W3104731853","https://openalex.org/W4240782810","https://openalex.org/W4241307704","https://openalex.org/W4249615887","https://openalex.org/W4255556797","https://openalex.org/W4290992857","https://openalex.org/W4297085396","https://openalex.org/W4298070890"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2364252372","https://openalex.org/W4234066492","https://openalex.org/W1998063895","https://openalex.org/W1967044713","https://openalex.org/W2133470120","https://openalex.org/W1994286895","https://openalex.org/W2747625183","https://openalex.org/W2080622457","https://openalex.org/W1970592395"],"abstract_inverted_index":{"Putting":[0],"together":[1],"an":[2,77,93,197],"ideal":[3],"outfit":[4,114],"is":[5,56],"a":[6,17,31,57,125],"process":[7],"that":[8,59,75,89,103],"involves":[9],"creativity":[10],"and":[11,30,121,137,144,158,178],"style":[12],"intuition.":[13],"This":[14,55],"makes":[15],"it":[16],"particularly":[18],"difficult":[19],"task":[20,65,157],"to":[21,61,71,83,133,142,175,180],"automate.":[22],"Existing":[23],"styling":[24],"products":[25],"generally":[26],"involve":[27],"human":[28],"specialists":[29],"highly":[32],"curated":[33],"set":[34],"of":[35,66,95,116,127,192,196],"fashion":[36,202],"items.":[37],"In":[38,79],"this":[39,107,146,155],"paper,":[40],"we":[41,45,81,129,149,153,167],"will":[42,111,130,150,168],"describe":[43,138,151],"how":[44,152,179],"bootstrapped":[46],"the":[47,63,134,139,185,193],"Complete":[48],"The":[49],"Look":[50],"(CTL)":[51],"system":[52],"at":[53],"Pinterest.":[54],"technology":[58],"aims":[60],"learn":[62],"subjective":[64,156],"\"style":[67],"compatibility\"":[68],"in":[69,184],"order":[70],"recommend":[72],"complementary":[73],"items":[74],"complete":[76],"outfit.":[78],"particular,":[80],"want":[82],"show":[84],"recommendations":[85],"from":[86,173],"other":[87],"categories":[88],"are":[90,100],"compatible":[91],"with":[92,106],"item":[94],"interest.":[96],"For":[97],"example,":[98],"what":[99],"some":[101],"heels":[102],"go":[104],"well":[105],"cocktail":[108],"dress?":[109],"We":[110],"introduce":[112],"our":[113,170],"dataset":[115],"over":[117],"1":[118],"million":[119,123],"outfits":[120],"4":[122],"objects,":[124],"subset":[126],"which":[128],"make":[131],"available":[132],"research":[135],"community,":[136],"pipeline":[140],"used":[141],"obtain":[143],"refresh":[145],"dataset.":[147],"Furthermore,":[148],"evaluate":[154],"compare":[159],"model":[160],"performance":[161],"across":[162],"multiple":[163],"training":[164],"methods.":[165],"Lastly,":[166],"share":[169],"lessons":[171],"going":[172],"experimentation":[174],"working":[176],"prototype,":[177],"mitigate":[181],"failure":[182],"modes":[183],"production":[186],"environment.":[187],"Our":[188],"work":[189],"represents":[190],"one":[191],"first":[194],"examples":[195],"industrial-scale":[198],"solution":[199],"for":[200],"compatibility-based":[201],"recommendation.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-10T00:00:00"}