{"id":"https://openalex.org/W4377865018","doi":"https://doi.org/10.1145/3539618.3591704","title":"It's Enough: Relaxing Diagonal Constraints in Linear Autoencoders for Recommendation","display_name":"It's Enough: Relaxing Diagonal Constraints in Linear Autoencoders for Recommendation","publication_year":2023,"publication_date":"2023-07-18","ids":{"openalex":"https://openalex.org/W4377865018","doi":"https://doi.org/10.1145/3539618.3591704"},"language":"en","primary_location":{"id":"doi:10.1145/3539618.3591704","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539618.3591704","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2305.12922","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034736590","display_name":"Jaewan Moon","orcid":"https://orcid.org/0009-0002-5438-1884"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Jaewan Moon","raw_affiliation_strings":["Sungkyunkwan University, Suwon, Republic of Korea"],"raw_orcid":"https://orcid.org/0009-0002-5438-1884","affiliations":[{"raw_affiliation_string":"Sungkyunkwan University, Suwon, Republic of Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013148146","display_name":"H. J. Kim","orcid":"https://orcid.org/0009-0003-2247-3482"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hye-young Kim","raw_affiliation_strings":["Sungkyunkwan University, Suwon, Republic of Korea"],"raw_orcid":"https://orcid.org/0009-0003-2247-3482","affiliations":[{"raw_affiliation_string":"Sungkyunkwan University, Suwon, Republic of Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065423554","display_name":"Jongwuk Lee","orcid":"https://orcid.org/0000-0001-9213-7706"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jongwuk Lee","raw_affiliation_strings":["Sungkyunkwan University, Suwon, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0001-9213-7706","affiliations":[{"raw_affiliation_string":"Sungkyunkwan University, Suwon, Republic of Korea","institution_ids":["https://openalex.org/I848706"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5034736590"],"corresponding_institution_ids":["https://openalex.org/I848706"],"apc_list":null,"apc_paid":null,"fwci":0.8968,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.78002247,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1639","last_page":"1648"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9643999934196472,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9634000062942505,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8473629951477051},{"id":"https://openalex.org/keywords/diagonal","display_name":"Diagonal","score":0.785412073135376},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.6495985984802246},{"id":"https://openalex.org/keywords/singular-value-decomposition","display_name":"Singular value decomposition","score":0.5998727083206177},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5070734024047852},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4337451159954071},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.43296998739242554},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.43046507239341736},{"id":"https://openalex.org/keywords/linear-model","display_name":"Linear model","score":0.4265793561935425},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4111882150173187},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39665526151657104},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3918074369430542},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3458781838417053},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.25182464718818665},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.21215903759002686}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8473629951477051},{"id":"https://openalex.org/C130367717","wikidata":"https://www.wikidata.org/wiki/Q189791","display_name":"Diagonal","level":2,"score":0.785412073135376},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6495985984802246},{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.5998727083206177},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5070734024047852},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4337451159954071},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.43296998739242554},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43046507239341736},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.4265793561935425},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4111882150173187},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39665526151657104},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3918074369430542},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3458781838417053},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25182464718818665},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.21215903759002686},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3539618.3591704","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539618.3591704","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2305.12922","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.12922","pdf_url":"https://arxiv.org/pdf/2305.12922","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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2305.12922","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.12922","pdf_url":"https://arxiv.org/pdf/2305.12922","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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1478288009","display_name":null,"funder_award_id":"2021-0-02068","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"},{"id":"https://openalex.org/G2191473729","display_name":null,"funder_award_id":"2019-0-00421","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"},{"id":"https://openalex.org/G3265002533","display_name":null,"funder_award_id":"IITP-2023-2020-0-01821","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"},{"id":"https://openalex.org/G4296643599","display_name":null,"funder_award_id":"2022-0-01045","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G4643994530","display_name":null,"funder_award_id":"2021-0-02068","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G4700831490","display_name":null,"funder_award_id":"2022-","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G5217505250","display_name":null,"funder_award_id":"2020-0-01821","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"},{"id":"https://openalex.org/G6326437388","display_name":null,"funder_award_id":"2022-0-00680","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G8463697320","display_name":null,"funder_award_id":"IITP-2023-2020-0-01821","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G8874964841","display_name":null,"funder_award_id":"2019-0-00421","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"}],"funders":[{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4377865018.pdf","grobid_xml":"https://content.openalex.org/works/W4377865018.grobid-xml"},"referenced_works_count":53,"referenced_works":["https://openalex.org/W1511814458","https://openalex.org/W1587374567","https://openalex.org/W1966553486","https://openalex.org/W1987431925","https://openalex.org/W1994389483","https://openalex.org/W2042281163","https://openalex.org/W2101409192","https://openalex.org/W2101950184","https://openalex.org/W2124187902","https://openalex.org/W2140310134","https://openalex.org/W2186878252","https://openalex.org/W2196920274","https://openalex.org/W2253995343","https://openalex.org/W2605350416","https://openalex.org/W2892888989","https://openalex.org/W2912745432","https://openalex.org/W2937556626","https://openalex.org/W2944441143","https://openalex.org/W2945827670","https://openalex.org/W2957191877","https://openalex.org/W2963085847","https://openalex.org/W2963367478","https://openalex.org/W2964044287","https://openalex.org/W2984100107","https://openalex.org/W2987577970","https://openalex.org/W2998089494","https://openalex.org/W2998431760","https://openalex.org/W3004578093","https://openalex.org/W3045200674","https://openalex.org/W3088444111","https://openalex.org/W3088744629","https://openalex.org/W3098638686","https://openalex.org/W3100278010","https://openalex.org/W3101708421","https://openalex.org/W3105114834","https://openalex.org/W3106082234","https://openalex.org/W3115198250","https://openalex.org/W3125645198","https://openalex.org/W3164446158","https://openalex.org/W3172974245","https://openalex.org/W3194908853","https://openalex.org/W3201591198","https://openalex.org/W3208747044","https://openalex.org/W3211009588","https://openalex.org/W4212843742","https://openalex.org/W4212986650","https://openalex.org/W4229075504","https://openalex.org/W4232980324","https://openalex.org/W4234698323","https://openalex.org/W4284689794","https://openalex.org/W4296591816","https://openalex.org/W4299286960","https://openalex.org/W4300029251"],"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/W2066043610"],"abstract_inverted_index":{"Linear":[0,101,107],"autoencoder":[1,93],"models":[2,94,129,139],"learn":[3],"an":[4],"item-to-item":[5],"weight":[6],"matrix":[7],"via":[8],"convex":[9],"optimization":[10],"with":[11],"L2":[12,61],"regularization":[13,62,160],"and":[14,52,104,137,161],"zero-diagonal":[15,70],"constraints.":[16,123],"Despite":[17],"their":[18],"simplicity,":[19],"they":[20,113,144],"have":[21],"shown":[22],"remarkable":[23],"performance":[24,80],"compared":[25],"to":[26,33,79,134],"sophisticated":[27],"non-linear":[28,138],"models.":[29],"This":[30],"paper":[31],"aims":[32],"theoretically":[34],"understand":[35],"the":[36,45,64,73,119,147],"properties":[37],"of":[38,47,66,75,121,149],"two":[39],"terms":[40],"in":[41,164],"linear":[42,92,115,136,165],"autoencoders.":[43,166],"Through":[44],"lens":[46],"singular":[48],"value":[49],"decomposition":[50],"(SVD)":[51],"principal":[53],"component":[54],"analysis":[55],"(PCA),":[56],"it":[57],"is":[58],"revealed":[59],"that":[60,112,127],"enhances":[63],"impact":[65,74],"high-ranked":[67],"PCs.":[68],"Meanwhile,":[69],"constraints":[71,163],"reduce":[72],"low-ranked":[76],"PCs,":[77],"leading":[78],"degradation":[81],"for":[82],"unpopular":[83],"items.":[84,151],"Inspired":[85],"by":[86,117],"this":[87],"analysis,":[88],"we":[89],"propose":[90],"simple-yet-effective":[91],"using":[95],"diagonal":[96,122,162],"inequality":[97],"constraints,":[98],"called":[99],"Relaxed":[100,105],"AutoEncoder":[102,108],"(RLAE)":[103],"Denoising":[106],"(RDLAE).":[109],"We":[110],"prove":[111],"generalize":[114],"autoencoders":[116],"adjusting":[118],"degree":[120],"Experimental":[124],"results":[125,153],"demonstrate":[126],"our":[128,156],"are":[130],"comparable":[131],"or":[132],"superior":[133],"state-of-the-art":[135],"on":[140,159],"six":[141],"benchmark":[142],"datasets;":[143],"significantly":[145],"improve":[146],"accuracy":[148],"long-tail":[150],"These":[152],"also":[154],"support":[155],"theoretical":[157],"insights":[158]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2023-05-24T00:00:00"}
