{"id":"https://openalex.org/W4221163653","doi":"https://doi.org/10.1145/3487553.3524254","title":"Supervised Contrastive Learning for Product Matching","display_name":"Supervised Contrastive Learning for Product Matching","publication_year":2022,"publication_date":"2022-04-25","ids":{"openalex":"https://openalex.org/W4221163653","doi":"https://doi.org/10.1145/3487553.3524254"},"language":"en","primary_location":{"id":"doi:10.1145/3487553.3524254","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3487553.3524254","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3487553.3524254","source":{"id":"https://openalex.org/S4363608846","display_name":"Companion Proceedings of the Web Conference 2022","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the Web Conference 2022","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3487553.3524254","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082603069","display_name":"Ralph Peeters","orcid":"https://orcid.org/0000-0003-3174-2616"},"institutions":[{"id":"https://openalex.org/I177802217","display_name":"University of Mannheim","ror":"https://ror.org/031bsb921","country_code":"DE","type":"education","lineage":["https://openalex.org/I177802217"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ralph Peeters","raw_affiliation_strings":["Data and Web Science Group, University of Mannheim, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data and Web Science Group, University of Mannheim, Germany","institution_ids":["https://openalex.org/I177802217"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076876024","display_name":"Christian Bizer","orcid":"https://orcid.org/0000-0003-2367-0237"},"institutions":[{"id":"https://openalex.org/I177802217","display_name":"University of Mannheim","ror":"https://ror.org/031bsb921","country_code":"DE","type":"education","lineage":["https://openalex.org/I177802217"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christian Bizer","raw_affiliation_strings":["Data and Web Science Group, University of Mannheim, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data and Web Science Group, University of Mannheim, Germany","institution_ids":["https://openalex.org/I177802217"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I177802217"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":34,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"248","last_page":"251"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9987999796867371,"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":0.9987999796867371,"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":0.9983000159263611,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9962000250816345,"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/computer-science","display_name":"Computer science","score":0.7767375707626343},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.606406033039093},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5120685696601868},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4972739517688751},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48325854539871216},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4471905827522278},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.41326022148132324},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.412728488445282},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3330293893814087},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.28911644220352173},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.16738244891166687},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10185658931732178},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09610772132873535}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7767375707626343},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.606406033039093},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5120685696601868},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4972739517688751},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48325854539871216},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4471905827522278},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.41326022148132324},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.412728488445282},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3330293893814087},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28911644220352173},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.16738244891166687},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10185658931732178},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09610772132873535},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3487553.3524254","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3487553.3524254","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3487553.3524254","source":{"id":"https://openalex.org/S4363608846","display_name":"Companion Proceedings of the Web Conference 2022","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the Web Conference 2022","raw_type":"proceedings-article"},{"id":"pmh:oai:ub-madoc.bib.uni-mannheim.de:62661","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4377196315","display_name":"MADOC (University of Mannheim)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I177802217","host_organization_name":"University of Mannheim","host_organization_lineage":["https://openalex.org/I177802217"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"Konferenzver\u00f6ffentlichung"},{"id":"pmh:oai:arXiv.org:2202.02098","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2202.02098","pdf_url":"https://arxiv.org/pdf/2202.02098","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:10.1145/3487553.3524254","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3487553.3524254","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3487553.3524254","source":{"id":"https://openalex.org/S4363608846","display_name":"Companion Proceedings of the Web Conference 2022","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the Web Conference 2022","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.49000000953674316,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4221163653.pdf","grobid_xml":"https://content.openalex.org/works/W4221163653.grobid-xml"},"referenced_works_count":10,"referenced_works":["https://openalex.org/W1583837637","https://openalex.org/W2798649495","https://openalex.org/W2945883855","https://openalex.org/W2965373594","https://openalex.org/W3005680577","https://openalex.org/W3014705052","https://openalex.org/W3123375411","https://openalex.org/W3156636935","https://openalex.org/W3174836606","https://openalex.org/W4287812705"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2145836866","https://openalex.org/W2803255133","https://openalex.org/W2909431601"],"abstract_inverted_index":{"Contrastive":[0],"learning":[1,31,53,82],"has":[2,200],"moved":[3],"the":[4,7,24,33,65,91,113,132,155,170,180],"state":[5],"of":[6,35,127],"art":[8],"for":[9,64,86,135,175,204],"many":[10],"tasks":[11],"in":[12,18,38,89,106,151,187,209],"computer":[13],"vision":[14],"and":[15,149,164],"information":[16],"retrieval":[17],"recent":[19],"years.":[20],"This":[21],"poster":[22],"is":[23,61,213],"first":[25],"work":[26],"that":[27,79,101,169,197],"applies":[28],"supervised":[29,51,103],"contrastive":[30,52,81,104,166,198],"to":[32,55,83,131,183,190],"task":[34,67],"product":[36,41,97,205],"matching":[37,66,206],"e-commerce":[39],"using":[40,68],"offers":[42],"from":[43],"different":[44],"e-shops.":[45],"More":[46],"specifically,":[47],"we":[48,123,144],"employ":[49],"a":[50,57,75,184,201],"technique":[54],"pre-train":[56],"Transformer":[58],"encoder":[59],"which":[60,90,210],"afterward":[62],"fine-tuned":[63],"pair-wise":[69],"training":[70,92,156,177],"data.":[71],"We":[72,99,194],"further":[73],"propose":[74],"source-aware":[76,109],"sampling":[77,110],"strategy":[78],"enables":[80],"be":[84,173],"applied":[85],"use":[87,207],"cases":[88,208],"data":[93,162],"does":[94],"not":[95],"contain":[96],"identifiers.":[98],"show":[100,168],"applying":[102],"pre-training":[105,167,199],"combination":[107],"with":[108,161],"significantly":[111],"improves":[112],"state-of-the-art":[114],"performance":[115,188],"on":[116,154],"several":[117],"widely":[118],"used":[119],"benchmarks:":[120],"For":[121,140],"Abt-Buy,":[122],"reach":[124,145],"an":[125],"F1-score":[126,152],"94.29":[128],"(+3.24":[129],"compared":[130],"previous":[133],"state-of-the-art),":[134],"Amazon-Google":[136],"79.28":[137],"(+":[138],"3.7).":[139],"WDC":[141],"Computers":[142],"datasets,":[143],"improvements":[146],"between":[147],"+0.8":[148],"+8.84":[150],"depending":[153],"set":[157],"size.":[158],"Further":[159],"experiments":[160],"augmentation":[163],"self-supervised":[165],"former":[171],"can":[172],"helpful":[174],"smaller":[176],"sets":[178],"while":[179],"latter":[181],"leads":[182],"significant":[185],"decline":[186],"due":[189],"inherent":[191],"label":[192],"noise.":[193],"thus":[195],"conclude":[196],"high":[202],"potential":[203],"explicit":[211],"supervision":[212],"available.":[214]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}