{"id":"https://openalex.org/W3041034658","doi":"https://doi.org/10.1109/sam48682.2020.9104354","title":"Learning Latent Features with Pairwise Penalties in Low-Rank Matrix Completion","display_name":"Learning Latent Features with Pairwise Penalties in Low-Rank Matrix Completion","publication_year":2020,"publication_date":"2020-06-01","ids":{"openalex":"https://openalex.org/W3041034658","doi":"https://doi.org/10.1109/sam48682.2020.9104354","mag":"3041034658"},"language":"en","primary_location":{"id":"doi:10.1109/sam48682.2020.9104354","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sam48682.2020.9104354","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071973105","display_name":"Kaiyi Ji","orcid":"https://orcid.org/0000-0002-9533-0058"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kaiyi Ji","raw_affiliation_strings":["Department of Electrical and Computer Engineering, The Ohio State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, The Ohio State University","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101812238","display_name":"Jian Tan","orcid":"https://orcid.org/0000-0002-1080-9300"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jian Tan","raw_affiliation_strings":["Department of Electrical and Computer Engineering, The Ohio State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, The Ohio State University","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031118213","display_name":"Jinfeng Xu","orcid":"https://orcid.org/0000-0002-3165-2015"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Jinfeng Xu","raw_affiliation_strings":["Department of Statistics and Actuarial Science, The University of Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Actuarial Science, The University of Hong Kong","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053809095","display_name":"Yuejie Chi","orcid":"https://orcid.org/0000-0002-6766-5459"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuejie Chi","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Carnegie Mellon University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9921000003814697,"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/T10057","display_name":"Face and Expression Recognition","score":0.9921000003814697,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9889000058174133,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9842000007629395,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.9061485528945923},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6561895608901978},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.6527134776115417},{"id":"https://openalex.org/keywords/matrix-completion","display_name":"Matrix completion","score":0.6410191059112549},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.522616446018219},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5167802572250366},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47449991106987},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46439629793167114},{"id":"https://openalex.org/keywords/convex-optimization","display_name":"Convex optimization","score":0.42403462529182434},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.4146530032157898},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3067896366119385},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.2752864956855774},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2179303765296936},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.06818887591362}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.9061485528945923},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6561895608901978},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.6527134776115417},{"id":"https://openalex.org/C2778459887","wikidata":"https://www.wikidata.org/wiki/Q6787865","display_name":"Matrix completion","level":3,"score":0.6410191059112549},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.522616446018219},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5167802572250366},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47449991106987},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46439629793167114},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.42403462529182434},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.4146530032157898},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3067896366119385},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.2752864956855774},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2179303765296936},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.06818887591362},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sam48682.2020.9104354","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sam48682.2020.9104354","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.75}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W57543762","https://openalex.org/W1530218127","https://openalex.org/W1774304772","https://openalex.org/W1903133717","https://openalex.org/W1965125844","https://openalex.org/W2020925091","https://openalex.org/W2026670441","https://openalex.org/W2027982384","https://openalex.org/W2054141820","https://openalex.org/W2074682976","https://openalex.org/W2078784669","https://openalex.org/W2117354486","https://openalex.org/W2125261539","https://openalex.org/W2144487656","https://openalex.org/W2150695437","https://openalex.org/W2164278908","https://openalex.org/W2167445298","https://openalex.org/W2168227362","https://openalex.org/W2184497579","https://openalex.org/W2186878252","https://openalex.org/W2210533708","https://openalex.org/W2219888463","https://openalex.org/W2277973662","https://openalex.org/W2295652899","https://openalex.org/W2341535507","https://openalex.org/W2394550695","https://openalex.org/W2405409114","https://openalex.org/W2566181539","https://openalex.org/W2602608495","https://openalex.org/W2610153490","https://openalex.org/W2611328865","https://openalex.org/W2788545655","https://openalex.org/W2810541051","https://openalex.org/W2962769133","https://openalex.org/W2962853966","https://openalex.org/W2963043672","https://openalex.org/W2963178962","https://openalex.org/W2963354044","https://openalex.org/W2964210434","https://openalex.org/W2969215180","https://openalex.org/W2997583404","https://openalex.org/W3098306969","https://openalex.org/W3101553404","https://openalex.org/W4292363360","https://openalex.org/W6631990664","https://openalex.org/W6678484073","https://openalex.org/W6684515591","https://openalex.org/W6686515677","https://openalex.org/W6686968995","https://openalex.org/W6703949738","https://openalex.org/W6737558694","https://openalex.org/W6748722399"],"related_works":["https://openalex.org/W3144354057","https://openalex.org/W2788210652","https://openalex.org/W3103289951","https://openalex.org/W2950359809","https://openalex.org/W2972830027","https://openalex.org/W4288107728","https://openalex.org/W4301205717","https://openalex.org/W3213035342","https://openalex.org/W3041034658","https://openalex.org/W3093503721"],"abstract_inverted_index":{"Low-rank":[0],"matrix":[1,13,43,80],"completion":[2],"has":[3],"achieved":[4],"great":[5],"success":[6],"in":[7],"many":[8],"real-world":[9],"data":[10],"applications.":[11],"A":[12,99],"factorization":[14,44],"model":[15],"that":[16,88],"learns":[17],"latent":[18,31],"features":[19],"is":[20,104],"usually":[21],"employed":[22],"and,":[23],"to":[24,56,71,106,120],"improve":[25],"prediction":[26],"performance,":[27],"the":[28,40,53,58,108,122],"similarities":[29],"between":[30],"variables":[32],"can":[33],"be":[34,63],"exploited":[35],"by":[36,66],"pairwise":[37,59,77,96],"learning":[38,78],"using":[39],"graph":[41],"regularized":[42],"(GRMF)":[45],"method.":[46],"However,":[47],"existing":[48],"GRMF":[49],"approaches":[50],"often":[51],"use":[52],"squared":[54],"loss":[55],"measure":[57],"differences,":[60],"which":[61],"may":[62],"overly":[64],"influenced":[65],"dissimilar":[67],"pairs":[68],"and":[69,101],"lead":[70],"inferior":[72],"prediction.":[73],"To":[74],"fully":[75],"empower":[76],"for":[79],"completion,":[81],"we":[82],"propose":[83],"a":[84,90],"general":[85,127],"optimization":[86,110],"framework":[87],"allows":[89],"rich":[91],"class":[92],"of":[93,125],"(non-":[94],")convex":[95],"penalty":[97],"functions.":[98],"new":[100],"efficient":[102],"algorithm":[103],"developed":[105],"solve":[107],"proposed":[109],"problem.":[111],"We":[112],"conduct":[113],"extensive":[114],"experiments":[115],"on":[116],"real":[117],"recommender":[118],"datasets":[119],"demonstrate":[121],"superior":[123],"performance":[124],"this":[126],"framework.":[128]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
