{"id":"https://openalex.org/W2788663865","doi":"https://doi.org/10.1145/3178876.3186146","title":"Aesthetic-based Clothing Recommendation","display_name":"Aesthetic-based Clothing Recommendation","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2788663865","doi":"https://doi.org/10.1145/3178876.3186146","mag":"2788663865"},"language":"en","primary_location":{"id":"doi:10.1145/3178876.3186146","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3178876.3186146","pdf_url":null,"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 2018 World Wide Web Conference on World Wide Web - WWW '18","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3178876.3186146","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Wenhui Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenhui Yu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Huidi Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huidi Zhang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiangnan He","orcid":null},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Xiangnan He","raw_affiliation_strings":["National University of Singapore, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"National University of Singapore, Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xu Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Chen","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Li Xiong","orcid":null},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li Xiong","raw_affiliation_strings":["Emory University, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"Emory University, Atlanta, GA, USA","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"last","author":{"id":null,"display_name":"Zheng Qin","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Qin","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":10.6366,"has_fulltext":false,"cited_by_count":169,"citation_normalized_percentile":{"value":0.98662441,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"649","last_page":"658"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9932000041007996,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9932000041007996,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9745000004768372,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9739999771118164,"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/clothing","display_name":"Clothing","score":0.8802000284194946},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6690000295639038},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.6686999797821045},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.6065000295639038},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.5669999718666077},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4496999979019165},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.415800005197525}],"concepts":[{"id":"https://openalex.org/C530175646","wikidata":"https://www.wikidata.org/wiki/Q11460","display_name":"Clothing","level":2,"score":0.8802000284194946},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6690000295639038},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.6686999797821045},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6539999842643738},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.6065000295639038},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.5669999718666077},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5128999948501587},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4496999979019165},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.415800005197525},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.353300005197525},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3481000065803528},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3346000015735626},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.31060001254081726},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C147101817","wikidata":"https://www.wikidata.org/wiki/Q13443840","display_name":"Product category","level":3,"score":0.25429999828338623},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2517000138759613},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.25110000371932983}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3178876.3186146","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3178876.3186146","pdf_url":null,"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 2018 World Wide Web Conference on World Wide Web - WWW '18","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1809.05822","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1809.05822","pdf_url":"https://arxiv.org/pdf/1809.05822","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":"doi:10.1145/3178876.3186146","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3178876.3186146","pdf_url":null,"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 2018 World Wide Web Conference on World Wide Web - WWW '18","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1500188831","https://openalex.org/W1511924373","https://openalex.org/W1814521481","https://openalex.org/W1949942966","https://openalex.org/W2009678853","https://openalex.org/W2024165284","https://openalex.org/W2027731328","https://openalex.org/W2048835603","https://openalex.org/W2054141820","https://openalex.org/W2056380823","https://openalex.org/W2078807908","https://openalex.org/W2089349245","https://openalex.org/W2102937240","https://openalex.org/W2125326641","https://openalex.org/W2126762950","https://openalex.org/W2144685566","https://openalex.org/W2146851707","https://openalex.org/W2337403844","https://openalex.org/W2340502990","https://openalex.org/W2462264630","https://openalex.org/W2469230926","https://openalex.org/W2497911325","https://openalex.org/W2512965516","https://openalex.org/W2527464456","https://openalex.org/W2565948352","https://openalex.org/W2604528050","https://openalex.org/W2605350416","https://openalex.org/W2740409734","https://openalex.org/W2741249238","https://openalex.org/W2741359394","https://openalex.org/W2788730650","https://openalex.org/W2963323306","https://openalex.org/W4231109964","https://openalex.org/W4301312111"],"related_works":[],"abstract_inverted_index":{"Recently,":[0],"product":[1],"images":[2],"have":[3],"gained":[4],"increasing":[5],"attention":[6],"in":[7,77,91,181],"clothing":[8,15,78,89,124],"recommendation":[9,79,209],"since":[10,80],"the":[11,37,48,66,88,97,113,133,150,156,178,199],"visual":[12,38],"appearance":[13],"of":[14,64,202],"products":[16],"has":[17],"a":[18,74,81,138,144,171,182],"significant":[19],"impact":[20],"on":[21,28,86,189],"consumers\u00bb":[22],"decision.":[23],"Most":[24],"existing":[25],"methods":[26],"rely":[27],"conventional":[29,98],"features":[30,39,100,135,180],"to":[31,111,163,176],"represent":[32],"an":[33],"image,":[34],"such":[35],"as":[36],"extracted":[40,136],"by":[41,137,166],"convolutional":[42],"neural":[43,140],"networks":[44],"(CNN":[45],"features)":[46],"and":[47,57,165,204],"scale-invariant":[49],"feature":[50],"transform":[51],"algorithm":[52],"(SIFT":[53],"features),":[54],"color":[55],"histograms,":[56],"so":[58],"on.":[59],"Nevertheless,":[60],"one":[61],"important":[62],"type":[63],"features,":[65,68],"aesthetic":[67,114,134,151,157,179,200],"is":[69,90,117,143],"seldom":[70],"considered.":[71],"It":[72],"plays":[73],"vital":[75],"role":[76],"users\u00bb":[82],"decision":[83],"depends":[84],"largely":[85],"whether":[87],"line":[92],"with":[93,120],"her":[94],"aesthetics,":[95],"however":[96],"image":[99],"cannot":[101],"portray":[102],"this":[103,107],"directly.":[104],"To":[105,127],"bridge":[106],"gap,":[108],"we":[109,130,168],"propose":[110,170],"introduce":[112],"information,":[115],"which":[116,142,192],"highly":[118],"relevant":[119],"user":[121,162,164],"preference,":[122],"into":[123],"recommender":[125],"systems.":[126],"achieve":[128],"this,":[129],"first":[131],"present":[132],"pre-trained":[139],"network,":[141],"brain-inspired":[145],"deep":[146],"structure":[147],"trained":[148],"for":[149],"assessment":[152],"task.":[153],"Considering":[154],"that":[155,194],"preference":[158,201],"varies":[159],"significantly":[160,205],"from":[161],"time,":[167],"then":[169],"new":[172],"tensor":[173],"factorization":[174],"model":[175],"incorporate":[177],"personalized":[183],"manner.":[184],"We":[185],"conduct":[186],"extensive":[187],"experiments":[188],"real-world":[190],"datasets,":[191],"demonstrate":[193],"our":[195],"approach":[196],"can":[197],"capture":[198],"users":[203],"outperform":[206],"several":[207],"state-of-the-art":[208],"methods.":[210]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":23},{"year":2022,"cited_by_count":26},{"year":2021,"cited_by_count":32},{"year":2020,"cited_by_count":26},{"year":2019,"cited_by_count":36},{"year":2018,"cited_by_count":6}],"updated_date":"2026-03-23T07:41:27.035349","created_date":"2018-03-06T00:00:00"}
