{"id":"https://openalex.org/W2997617192","doi":"https://doi.org/10.1145/3336191.3371831","title":"RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback","display_name":"RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback","publication_year":2020,"publication_date":"2020-01-20","ids":{"openalex":"https://openalex.org/W2997617192","doi":"https://doi.org/10.1145/3336191.3371831","mag":"2997617192"},"language":"en","primary_location":{"id":"doi:10.1145/3336191.3371831","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3336191.3371831","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1912.11160","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Ilya Shenbin","orcid":null},"institutions":[{"id":"https://openalex.org/I2800906181","display_name":"St. Petersburg Department of Steklov Institute of Mathematics","ror":"https://ror.org/030h9s280","country_code":"RU","type":"facility","lineage":["https://openalex.org/I1313323035","https://openalex.org/I2800906181","https://openalex.org/I4210124601"]}],"countries":["RU"],"is_corresponding":true,"raw_author_name":"Ilya Shenbin","raw_affiliation_strings":["Steklov Institute of Mathematics at St. Petersburg, Saint Petersburg, Russian Fed"],"affiliations":[{"raw_affiliation_string":"Steklov Institute of Mathematics at St. Petersburg, Saint Petersburg, Russian Fed","institution_ids":["https://openalex.org/I2800906181"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Anton Alekseev","orcid":null},"institutions":[{"id":"https://openalex.org/I2800906181","display_name":"St. Petersburg Department of Steklov Institute of Mathematics","ror":"https://ror.org/030h9s280","country_code":"RU","type":"facility","lineage":["https://openalex.org/I1313323035","https://openalex.org/I2800906181","https://openalex.org/I4210124601"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Anton Alekseev","raw_affiliation_strings":["Steklov Institute of Mathematics at St. Petersburg, Saint Petersburg, Russian Fed"],"affiliations":[{"raw_affiliation_string":"Steklov Institute of Mathematics at St. Petersburg, Saint Petersburg, Russian Fed","institution_ids":["https://openalex.org/I2800906181"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Elena Tutubalina","orcid":null},"institutions":[{"id":"https://openalex.org/I2800906181","display_name":"St. Petersburg Department of Steklov Institute of Mathematics","ror":"https://ror.org/030h9s280","country_code":"RU","type":"facility","lineage":["https://openalex.org/I1313323035","https://openalex.org/I2800906181","https://openalex.org/I4210124601"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Elena Tutubalina","raw_affiliation_strings":["Steklov Institute of Mathematics at St. Petersburg, Saint Petersburg, Russian Fed"],"affiliations":[{"raw_affiliation_string":"Steklov Institute of Mathematics at St. Petersburg, Saint Petersburg, Russian Fed","institution_ids":["https://openalex.org/I2800906181"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Valentin Malykh","orcid":null},"institutions":[{"id":"https://openalex.org/I153845743","display_name":"Moscow Institute of Physics and Technology","ror":"https://ror.org/00v0z9322","country_code":"RU","type":"education","lineage":["https://openalex.org/I153845743"]},{"id":"https://openalex.org/I4210089467","display_name":"Institute of Physics and Technology","ror":"https://ror.org/005n3yy14","country_code":"RU","type":"facility","lineage":["https://openalex.org/I1313323035","https://openalex.org/I4210089467"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Valentin Malykh","raw_affiliation_strings":["Moscow Institute of Physics and Technology, Moscow, Russian Fed"],"affiliations":[{"raw_affiliation_string":"Moscow Institute of Physics and Technology, Moscow, Russian Fed","institution_ids":["https://openalex.org/I4210089467","https://openalex.org/I153845743"]}]},{"author_position":"last","author":{"id":null,"display_name":"Sergey I. Nikolenko","orcid":null},"institutions":[{"id":"https://openalex.org/I2801160905","display_name":"Steklov Mathematical Institute","ror":"https://ror.org/03zeg8w71","country_code":"RU","type":"facility","lineage":["https://openalex.org/I1313323035","https://openalex.org/I2801160905","https://openalex.org/I4210124601"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Sergey I. Nikolenko","raw_affiliation_strings":["Steklov Institute of Mathematics at St. Petersburg, Steklov Institute of Mathemati, Russian Fed"],"affiliations":[{"raw_affiliation_string":"Steklov Institute of Mathematics at St. Petersburg, Steklov Institute of Mathemati, Russian Fed","institution_ids":["https://openalex.org/I2801160905"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2800906181"],"apc_list":null,"apc_paid":null,"fwci":23.6418,"has_fulltext":false,"cited_by_count":172,"citation_normalized_percentile":{"value":0.99480477,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"528","last_page":"536"},"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9355000257492065,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.9162999987602234,"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/autoencoder","display_name":"Autoencoder","score":0.765999972820282},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.7242000102996826},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.6800000071525574},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5407000184059143},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.4359999895095825},{"id":"https://openalex.org/keywords/multinomial-distribution","display_name":"Multinomial distribution","score":0.4323999881744385},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3711000084877014},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.3411000072956085}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.765999972820282},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7242000102996826},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.6800000071525574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6572999954223633},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5943999886512756},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5795000195503235},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5407000184059143},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.4359999895095825},{"id":"https://openalex.org/C192065140","wikidata":"https://www.wikidata.org/wiki/Q1147928","display_name":"Multinomial distribution","level":2,"score":0.4323999881744385},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3711000084877014},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3411000072956085},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3206999897956848},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3142000138759613},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31139999628067017},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2937000095844269},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2689000070095062},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.26269999146461487},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.2574999928474426}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3336191.3371831","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3336191.3371831","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1912.11160","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1912.11160","pdf_url":"https://arxiv.org/pdf/1912.11160","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":"pmh:oai:arXiv.org:1912.11160","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1912.11160","pdf_url":"https://arxiv.org/pdf/1912.11160","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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1987063155","https://openalex.org/W1987431925","https://openalex.org/W2031634675","https://openalex.org/W2085040216","https://openalex.org/W2101409192","https://openalex.org/W2128877075","https://openalex.org/W2170551306","https://openalex.org/W2171960770","https://openalex.org/W2219888463","https://openalex.org/W2253995343","https://openalex.org/W2963085847","https://openalex.org/W2963223306","https://openalex.org/W2997574889","https://openalex.org/W6703949738"],"related_works":[],"abstract_inverted_index":{"Recent":[0],"research":[1,52],"has":[2,31],"shown":[3,32],"the":[4,19,26,43,74,82,86],"advantages":[5],"of":[6],"using":[7],"autoencoders":[8],"based":[9,95],"on":[10,53,96],"deep":[11],"neural":[12],"networks":[13],"for":[14,35,56,73,85],"collaborative":[15,118],"filtering.":[16],"In":[17,38,99],"particular,":[18],"recently":[20],"proposed":[21,109],"Mult-VAE":[22,113],"model,":[23],"which":[24],"used":[25],"multinomial":[27],"likelihood":[28],"variational":[29,57],"autoencoders,":[30],"excellent":[33],"results":[34],"top-N":[36],"recommendations.":[37],"this":[39],"work,":[40],"we":[41,102],"propose":[42],"Recommender":[44],"VAE":[45],"(RecVAE)":[46],"model":[47],"that":[48,104],"originates":[49],"from":[50],"our":[51,129],"regularization":[54],"techniques":[55],"autoencoders.":[58],"RecVAE":[59,105],"introduces":[60],"several":[61],"novel":[62,69],"ideas":[63],"to":[64,80,93,127],"improve":[65],"Mult-VAE,":[66],"including":[67,112],"a":[68,77,90,123],"composite":[70],"prior":[71],"distribution":[72],"latent":[75],"codes,":[76],"new":[78,91,130],"approach":[79,92],"setting":[81],"beta":[83],"hyperparameter":[84],"beta-VAE":[87],"framework,":[88],"and":[89,114,121,133],"training":[94],"alternating":[97],"updates.":[98],"experimental":[100],"evaluation,":[101],"show":[103],"significantly":[106],"outperforms":[107],"previously":[108],"autoencoder-based":[110],"models,":[111],"RaCT,":[115],"across":[116],"classical":[117],"filtering":[119],"datasets,":[120],"present":[122],"detailed":[124],"ablation":[125],"study":[126],"assess":[128],"developments.":[131],"Code":[132],"models":[134],"are":[135],"available":[136],"at":[137],"https://github.com/ilya-shenbin/RecVAE.":[138]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":46},{"year":2024,"cited_by_count":37},{"year":2023,"cited_by_count":23},{"year":2022,"cited_by_count":34},{"year":2021,"cited_by_count":24},{"year":2020,"cited_by_count":4}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2020-01-10T00:00:00"}