{"id":"https://openalex.org/W4386728968","doi":"https://doi.org/10.1145/3604915.3608833","title":"Private Matrix Factorization with Public Item Features","display_name":"Private Matrix Factorization with Public Item Features","publication_year":2023,"publication_date":"2023-09-14","ids":{"openalex":"https://openalex.org/W4386728968","doi":"https://doi.org/10.1145/3604915.3608833"},"language":"en","primary_location":{"id":"doi:10.1145/3604915.3608833","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1145/3604915.3608833","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3604915.3608833","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 17th ACM Conference on Recommender Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3604915.3608833","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025723886","display_name":"Mihaela Curmei","orcid":"https://orcid.org/0000-0002-3918-3065"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mihaela Curmei","raw_affiliation_strings":["EECS, University of California, Berkeley, USA"],"raw_orcid":"https://orcid.org/0000-0002-3918-3065","affiliations":[{"raw_affiliation_string":"EECS, University of California, Berkeley, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030908997","display_name":"Walid Krichene","orcid":"https://orcid.org/0000-0002-1066-9686"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Walid Krichene","raw_affiliation_strings":["Google Research, USA"],"raw_orcid":"https://orcid.org/0000-0002-1066-9686","affiliations":[{"raw_affiliation_string":"Google Research, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058333670","display_name":"Li Lyna Zhang","orcid":"https://orcid.org/0009-0009-4214-2174"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li Zhang","raw_affiliation_strings":["Microsoft, USA"],"raw_orcid":"https://orcid.org/0009-0009-4214-2174","affiliations":[{"raw_affiliation_string":"Microsoft, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008661612","display_name":"Mukund Sundararajan","orcid":"https://orcid.org/0000-0002-2031-7545"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mukund Sundararajan","raw_affiliation_strings":["Google, USA"],"raw_orcid":"https://orcid.org/0000-0002-2031-7545","affiliations":[{"raw_affiliation_string":"Google, USA","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5025723886"],"corresponding_institution_ids":["https://openalex.org/I95457486"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1155531,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"33","issue":null,"first_page":"805","last_page":"812"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998999834060669,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998999834060669,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9973999857902527,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9936000108718872,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/differential-privacy","display_name":"Differential privacy","score":0.8091858625411987},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7705918550491333},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.7683696746826172},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.5794036388397217},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5789729356765747},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.5653256773948669},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.5638282895088196},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5369467735290527},{"id":"https://openalex.org/keywords/matrix-algebra","display_name":"Matrix algebra","score":0.4215541481971741},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.41987884044647217},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3828699290752411},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36804118752479553},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3654719591140747},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.35980886220932007},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.22383037209510803}],"concepts":[{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.8091858625411987},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7705918550491333},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.7683696746826172},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.5794036388397217},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5789729356765747},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.5653256773948669},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.5638282895088196},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5369467735290527},{"id":"https://openalex.org/C2988995629","wikidata":"https://www.wikidata.org/wiki/Q2915729","display_name":"Matrix algebra","level":3,"score":0.4215541481971741},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.41987884044647217},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3828699290752411},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36804118752479553},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3654719591140747},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.35980886220932007},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.22383037209510803},{"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/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3604915.3608833","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1145/3604915.3608833","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3604915.3608833","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 17th ACM Conference on Recommender Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3604915.3608833","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1145/3604915.3608833","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3604915.3608833","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 17th ACM Conference on Recommender Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.5,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4386728968.pdf","grobid_xml":"https://content.openalex.org/works/W4386728968.grobid-xml"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W1979584682","https://openalex.org/W1997690112","https://openalex.org/W2008886893","https://openalex.org/W2022097286","https://openalex.org/W2082927600","https://openalex.org/W2101409192","https://openalex.org/W2102982709","https://openalex.org/W2111200183","https://openalex.org/W2117420919","https://openalex.org/W2119829020","https://openalex.org/W2123147099","https://openalex.org/W2135930857","https://openalex.org/W2166074149","https://openalex.org/W2336195973","https://openalex.org/W2473418344","https://openalex.org/W2508504774","https://openalex.org/W2605246672","https://openalex.org/W2767079719","https://openalex.org/W2916790858","https://openalex.org/W2963085847","https://openalex.org/W2997620690","https://openalex.org/W3046392445","https://openalex.org/W3211930400","https://openalex.org/W4239784985","https://openalex.org/W4312266295","https://openalex.org/W4319780902","https://openalex.org/W6657138077"],"related_works":["https://openalex.org/W3038283795","https://openalex.org/W2604501336","https://openalex.org/W2734500670","https://openalex.org/W2558166297","https://openalex.org/W2315671126","https://openalex.org/W798507144","https://openalex.org/W1973739845","https://openalex.org/W119752240","https://openalex.org/W2069570686","https://openalex.org/W4401009883"],"abstract_inverted_index":{"We":[0,32,48],"consider":[1],"the":[2,25,66],"problem":[3],"of":[4,27],"training":[5,40],"private":[6],"recommendation":[7,30],"models":[8],"with":[9,16],"access":[10],"to":[11],"public":[12,36],"item":[13,37,75,83],"features.":[14],"Training":[15],"Differential":[17],"Privacy":[18],"(DP)":[19],"offers":[20],"strong":[21],"privacy":[22],"guarantees,":[23],"at":[24],"expense":[26],"loss":[28,45],"in":[29,46],"quality.":[31,47],"show":[33],"that":[34,59,78],"incorporating":[35],"features":[38],"during":[39],"can":[41],"help":[42],"mitigate":[43],"this":[44],"propose":[49],"a":[50],"general":[51],"approach":[52],"based":[53],"on":[54],"collective":[55],"matrix":[56,69,77],"factorization":[57],"(CMF),":[58],"works":[60],"by":[61],"simultaneously":[62],"factorizing":[63],"two":[64],"matrices:":[65],"user":[67],"feedback":[68],"(representing":[70],"sensitive":[71],"data)":[72],"and":[73],"an":[74],"feature":[76],"encodes":[79],"publicly":[80],"available":[81],"(non-sensitive)":[82],"information.":[84]},"counts_by_year":[],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}