{"id":"https://openalex.org/W2963753203","doi":"https://doi.org/10.1145/3298689.3347006","title":"A deep learning system for predicting size and fit in fashion e-commerce","display_name":"A deep learning system for predicting size and fit in fashion e-commerce","publication_year":2019,"publication_date":"2019-09-10","ids":{"openalex":"https://openalex.org/W2963753203","doi":"https://doi.org/10.1145/3298689.3347006","mag":"2963753203"},"language":"en","primary_location":{"id":"doi:10.1145/3298689.3347006","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3298689.3347006","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM Conference on Recommender Systems","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/1907.09844","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Abdul-Saboor Sheikh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abdul-Saboor Sheikh","raw_affiliation_strings":["Zalando Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zalando Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Romain Guigour\u00e8s","orcid":null},"institutions":[{"id":"https://openalex.org/I44067592","display_name":"Business in the Community","ror":"https://ror.org/04kfpsj04","country_code":"GB","type":"nonprofit","lineage":["https://openalex.org/I44067592"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Romain Guigour\u00e8s","raw_affiliation_strings":["Shopify Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shopify Inc","institution_ids":["https://openalex.org/I44067592"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Evgenii Koriagin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Evgenii Koriagin","raw_affiliation_strings":["Zalando SE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zalando SE","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yuen King Ho","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuen King Ho","raw_affiliation_strings":["OLX and Zalando SE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"OLX and Zalando SE","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Reza Shirvany","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Reza Shirvany","raw_affiliation_strings":["Zalando SE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zalando SE","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Roland Vollgraf","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roland Vollgraf","raw_affiliation_strings":["Zalando Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zalando Research","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Urs Bergmann","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Urs Bergmann","raw_affiliation_strings":["Zalando Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zalando Research","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":40,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"110","last_page":"118"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10609","display_name":"Digital Marketing and Social Media","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11536","display_name":"Consumer Retail Behavior Studies","score":0.9868000149726868,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5976999998092651},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5648000240325928},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5415999889373779},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.5109000205993652},{"id":"https://openalex.org/keywords/customer-satisfaction","display_name":"Customer satisfaction","score":0.44029998779296875},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4219000041484833},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.34619998931884766},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.3156999945640564}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7609999775886536},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6129999756813049},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5976999998092651},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5648000240325928},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5415999889373779},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5403000116348267},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.5109000205993652},{"id":"https://openalex.org/C191511416","wikidata":"https://www.wikidata.org/wiki/Q999278","display_name":"Customer satisfaction","level":2,"score":0.44029998779296875},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4219000041484833},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37790000438690186},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.34619998931884766},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3140999972820282},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.31150001287460327},{"id":"https://openalex.org/C98825075","wikidata":"https://www.wikidata.org/wiki/Q485643","display_name":"Customer relationship management","level":2,"score":0.29420000314712524},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2703000009059906},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2680000066757202},{"id":"https://openalex.org/C23213687","wikidata":"https://www.wikidata.org/wiki/Q301468","display_name":"Consumer behaviour","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C2778956030","wikidata":"https://www.wikidata.org/wiki/Q5142477","display_name":"Cold start (automotive)","level":2,"score":0.2549999952316284},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.2547000050544739}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3298689.3347006","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3298689.3347006","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM Conference on Recommender Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1907.09844","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1907.09844","pdf_url":"https://arxiv.org/pdf/1907.09844","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:1907.09844","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1907.09844","pdf_url":"https://arxiv.org/pdf/1907.09844","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1990836268","https://openalex.org/W2000529749","https://openalex.org/W2008886893","https://openalex.org/W2029948740","https://openalex.org/W2114079787","https://openalex.org/W2136189984","https://openalex.org/W2194775991","https://openalex.org/W2605350416","https://openalex.org/W2749155890","https://openalex.org/W2750004028","https://openalex.org/W2788493241","https://openalex.org/W2893160345","https://openalex.org/W2894397262"],"related_works":[],"abstract_inverted_index":{"Personalized":[0],"size":[1,73,111,199],"and":[2,20,74,84,87,112,124,143,182],"fit":[3,16,75,113,177],"recommendations":[4],"bear":[5],"crucial":[6],"significance":[7],"for":[8,49,71,198],"any":[9],"fashion":[10,180],"e-commerce":[11],"platform.":[12],"Predicting":[13],"the":[14,22,138,167,183],"correct":[15],"drives":[17],"customer":[18,39,83,123,142,186],"satisfaction":[19],"benefits":[21],"business":[23],"by":[24],"reducing":[25],"costs":[26],"incurred":[27],"due":[28],"to":[29,37,106,129,150,158],"size-related":[30],"returns.":[31],"Traditional":[32],"collaborative":[33],"filtering":[34],"algorithms":[35],"seek":[36],"model":[38,89],"preferences":[40],"based":[41,68],"on":[42],"their":[43,131],"previous":[44],"orders.":[45,58],"A":[46],"typical":[47],"challenge":[48],"such":[50],"methods":[51],"stems":[52],"from":[53,116,179],"extreme":[54],"sparsity":[55],"of":[56,104,110,155],"customer-article":[57,118],"To":[59],"alleviate":[60],"this":[61],"problem,":[62],"we":[63,188],"propose":[64],"a":[65,95,101,147,194],"deep":[66],"learning":[67],"content-collaborative":[69],"methodology":[70],"personalized":[72,152],"recommendation.":[76,200],"Our":[77],"proposed":[78],"method":[79,99,139,157],"can":[80,88],"ingest":[81],"arbitrary":[82],"article":[85,125,144],"data":[86],"multiple":[90],"individuals":[91],"or":[92],"intents":[93],"behind":[94],"single":[96],"account.":[97],"The":[98],"optimizes":[100],"global":[102],"set":[103],"parameters":[105],"learn":[107,130],"population-level":[108],"abstractions":[109],"relevant":[114],"information":[115],"observed":[117],"interactions.":[119],"It":[120],"further":[121,189],"employs":[122],"specific":[126],"embedding":[127],"variables":[128],"properties.":[132],"Together":[133],"with":[134],"learned":[135],"entity":[136],"embeddings,":[137],"maps":[140],"additional":[141],"attributes":[145],"into":[146],"latent":[148],"space":[149],"derive":[151],"recommendations.":[153],"Application":[154],"our":[156],"two":[159,172],"publicly":[160],"available":[161],"datasets":[162],"demonstrate":[163],"an":[164],"improvement":[165],"over":[166],"state-of-the-art":[168],"published":[169],"results.":[170],"On":[171],"proprietary":[173],"datasets,":[174],"one":[175],"containing":[176],"feedback":[178],"experts":[181],"other":[184],"involving":[185],"purchases,":[187],"outperform":[190],"comparable":[191],"methodologies,":[192],"including":[193],"recent":[195],"Bayesian":[196],"approach":[197]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2019-07-30T00:00:00"}