{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/100947"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/100947","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Learning embeddings for fashion recommendation","abstract":"In this work, we present a novel methodology to recommend items that are compatible with a given item of clothing. Compatibility is a hard notion to capture because of its diversity and subjectivity. We propose an embedding based approach to solve this problem, and perform recommendation based on product-closeness to the given clothing item. We perform this by first decomposing the notion of product-closeness into two inter-related notions of product similarity and product compatibility. Then, we incorporate product type into our embedding mechanism, and learn different embedding networks for different product types. We evaluate our proposed strategy extensively, and demonstrate that it performs better than the baseline, and is an effective method for performing few-shot transfer to compatibility prediction tasks.","abstract_html":"In this work, we present a novel methodology to recommend items that are compatible with a given item of clothing. Compatibility is a hard notion to capture because of its diversity and subjectivity. We propose an embedding based approach to solve this problem, and perform recommendation based on product-closeness to the given clothing item. We perform this by first decomposing the notion of product-closeness into two inter-related notions of product similarity and product compatibility. Then, we incorporate product type into our embedding mechanism, and learn different embedding networks for different product types. We evaluate our proposed strategy extensively, and demonstrate that it performs better than the baseline, and is an effective method for performing few-shot transfer to compatibility prediction tasks.","abstract_has_math":false,"creators":["Rajpal, Shreya"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Forsyth, David A."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-04T20:26:57Z","date_published":"2018-09-04T20:26:57Z","updated_at":"2026-07-22T22:24:38Z","subjects":["embeddings","fashion","fashion recommendation","computer vision"],"languages":["en"],"rights":["Copyright 2018 Shreya Rajpal"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/100947","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Forsyth, David A."]},{"key":"dc:creator","label":"Author","values":["Rajpal, Shreya"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-04T20:26:57Z","2018-04-25","2018-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["embeddings","fashion","fashion recommendation","computer vision"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Shreya Rajpal"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/100947"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In this work, we present a novel methodology to recommend items that are compatible with a given item of clothing. Compatibility is a hard notion to capture because of its diversity and subjectivity. We propose an embedding based approach to solve this problem, and perform recommendation based on product-closeness to the given clothing item. We perform this by first decomposing the notion of product-closeness into two inter-related notions of product similarity and product compatibility. Then, we incorporate product type into our embedding mechanism, and learn different embedding networks for different product types. We evaluate our proposed strategy extensively, and demonstrate that it performs better than the baseline, and is an effective method for performing few-shot transfer to compatibility prediction tasks.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo terms","The student, Shreya Rajpal, accepted the attached license on 2018-04-25 at 12:35.","The student, Shreya Rajpal, submitted this Thesis for approval on 2018-04-25 at 13:01.","This Thesis was approved for publication on 2018-04-25 at 15:25.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12161 on 2018-08-31 at 17:11:16","Made available in DSpace on 2018-09-04T20:26:57Z (GMT). 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We perform this by first decomposing the notion of product-closeness into two inter-related notions of product similarity and product compatibility. Then, we incorporate product type into our embedding mechanism, and learn different embedding networks for different product types. We evaluate our proposed strategy extensively, and demonstrate that it performs better than the baseline, and is an effective method for performing few-shot transfer to compatibility prediction tasks.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo terms","The student, Shreya Rajpal, accepted the attached license on 2018-04-25 at 12:35.","The student, Shreya Rajpal, submitted this Thesis for approval on 2018-04-25 at 13:01.","This Thesis was approved for publication on 2018-04-25 at 15:25.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12161 on 2018-08-31 at 17:11:16","Made available in DSpace on 2018-09-04T20:26:57Z (GMT). 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