{"id":"https://openalex.org/W2725279637","doi":"https://doi.org/10.1109/siu.2017.7960695","title":"Comparison of collaborative deep learning and nonnegative matrix factorization for recommender systems","display_name":"Comparison of collaborative deep learning and nonnegative matrix factorization for recommender systems","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2725279637","doi":"https://doi.org/10.1109/siu.2017.7960695","mag":"2725279637"},"language":"en","primary_location":{"id":"doi:10.1109/siu.2017.7960695","is_oa":false,"landing_page_url":"https://doi.org/10.1109/siu.2017.7960695","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","raw_type":"proceedings-article"},"type":"article","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/A5063051452","display_name":"Mine Oggretir","orcid":null},"institutions":[{"id":"https://openalex.org/I4405392","display_name":"Bo\u011fazi\u00e7i University","ror":"https://ror.org/03z9tma90","country_code":"TR","type":"education","lineage":["https://openalex.org/I4405392"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Mine Oggretir","raw_affiliation_strings":["Bogazici Universitesi, Istanbul, TR"],"affiliations":[{"raw_affiliation_string":"Bogazici Universitesi, Istanbul, TR","institution_ids":["https://openalex.org/I4405392"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064633247","display_name":"Ali Taylan Cemgil","orcid":"https://orcid.org/0000-0003-4463-8455"},"institutions":[{"id":"https://openalex.org/I4405392","display_name":"Bo\u011fazi\u00e7i University","ror":"https://ror.org/03z9tma90","country_code":"TR","type":"education","lineage":["https://openalex.org/I4405392"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Ali Taylan Cemgil","raw_affiliation_strings":["Bilgisayar M\u00fchendisli\u011fi B\u00f6l\u00fcm\u00fc, Bo\u011fazi\u00e7i \u00dcniversitesi, \u0130stanbul, T\u00fcrkiye"],"affiliations":[{"raw_affiliation_string":"Bilgisayar M\u00fchendisli\u011fi B\u00f6l\u00fcm\u00fc, Bo\u011fazi\u00e7i \u00dcniversitesi, \u0130stanbul, T\u00fcrkiye","institution_ids":["https://openalex.org/I4405392"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5063051452"],"corresponding_institution_ids":["https://openalex.org/I4405392"],"apc_list":null,"apc_paid":null,"fwci":2.4181,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.91163777,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9994000196456909,"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":0.9994000196456909,"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":0.9961000084877014,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9886999726295471,"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/collaborative-filtering","display_name":"Collaborative filtering","score":0.9428125619888306},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.9027540683746338},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7646478414535522},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.7185760736465454},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5885000228881836},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4971599876880646},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4907890260219574},{"id":"https://openalex.org/keywords/hybrid-learning","display_name":"Hybrid learning","score":0.422471821308136},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.4202798306941986},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41434332728385925},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32664260268211365},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.06181091070175171}],"concepts":[{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.9428125619888306},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.9027540683746338},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7646478414535522},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.7185760736465454},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5885000228881836},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4971599876880646},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4907890260219574},{"id":"https://openalex.org/C3018790387","wikidata":"https://www.wikidata.org/wiki/Q869010","display_name":"Hybrid learning","level":2,"score":0.422471821308136},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.4202798306941986},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41434332728385925},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32664260268211365},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.06181091070175171},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","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":1,"locations":[{"id":"doi:10.1109/siu.2017.7960695","is_oa":false,"landing_page_url":"https://doi.org/10.1109/siu.2017.7960695","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.550000011920929,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W189086490","https://openalex.org/W1493526108","https://openalex.org/W1690919088","https://openalex.org/W2025605741","https://openalex.org/W2031696998","https://openalex.org/W2048657872","https://openalex.org/W2099866409","https://openalex.org/W2101409192","https://openalex.org/W2116802659","https://openalex.org/W2135790056","https://openalex.org/W2137028279","https://openalex.org/W2137245235","https://openalex.org/W2140310134","https://openalex.org/W2145094598","https://openalex.org/W2153579005","https://openalex.org/W2157881433","https://openalex.org/W2262817822","https://openalex.org/W2548339725","https://openalex.org/W2997574889","https://openalex.org/W4294170691","https://openalex.org/W4299286960","https://openalex.org/W6607802380","https://openalex.org/W6677446147","https://openalex.org/W6680830989","https://openalex.org/W6681096077","https://openalex.org/W6682691769","https://openalex.org/W6692935382","https://openalex.org/W6729413687","https://openalex.org/W7070499600"],"related_works":["https://openalex.org/W2772628444","https://openalex.org/W1484355083","https://openalex.org/W4220714703","https://openalex.org/W2098758514","https://openalex.org/W2735929803","https://openalex.org/W3008845055","https://openalex.org/W2170391450","https://openalex.org/W4376854386","https://openalex.org/W2202724490","https://openalex.org/W2508671622"],"abstract_inverted_index":{"Collaborative":[0,57],"filtering":[1,42,67],"and":[2,12,39],"content-based":[3],"methods":[4],"are":[5],"two":[6],"main":[7],"approaches":[8],"for":[9,36],"recommender":[10],"systems,":[11],"hybrid":[13,28,55],"models":[14],"use":[15],"advantages":[16],"of":[17,26],"both.":[18],"In":[19],"this":[20],"paper,":[21],"we":[22],"made":[23],"a":[24,27,40],"comparison":[25],"model,":[29,56],"which":[30],"uses":[31],"Bayesian":[32,44],"Staked":[33],"Denoising":[34],"Autoencoders":[35],"content":[37],"learning,":[38],"collaborative":[41,66],"method,":[43],"Nonnegative":[45],"Matrix":[46],"Factorisation.":[47],"It":[48],"is":[49],"shown":[50],"that":[51],"the":[52],"tightly":[53],"coupled":[54],"Deep":[58],"Learning,":[59],"gave":[60],"more":[61],"successful":[62],"results":[63],"comparing":[64],"to":[65],"methods.":[68]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}