{"id":"https://openalex.org/W4211225178","doi":"https://doi.org/10.1109/icnsc52481.2021.9702156","title":"Creator2Vec: Creator Feature Embedding for Deep Learning Recommender System","display_name":"Creator2Vec: Creator Feature Embedding for Deep Learning Recommender System","publication_year":2021,"publication_date":"2021-12-03","ids":{"openalex":"https://openalex.org/W4211225178","doi":"https://doi.org/10.1109/icnsc52481.2021.9702156"},"language":"en","primary_location":{"id":"doi:10.1109/icnsc52481.2021.9702156","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnsc52481.2021.9702156","pdf_url":null,"source":{"id":"https://openalex.org/S4363608459","display_name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","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/A5052502839","display_name":"Zhengrong Wen","orcid":null},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengrong Wen","raw_affiliation_strings":["Department of Automation, Xiamen University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061054286","display_name":"Kehua Miao","orcid":"https://orcid.org/0000-0003-2267-480X"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kehua Miao","raw_affiliation_strings":["Department of Automation, Xiamen University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028752845","display_name":"Yuling Fan","orcid":"https://orcid.org/0000-0003-1312-8655"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuling Fan","raw_affiliation_strings":["Department of Automation, Xiamen University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068333641","display_name":"Yuxin Shang","orcid":null},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxin Shang","raw_affiliation_strings":["Department of Automation, Xiamen University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29711628,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"1","last_page":"6"},"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/T10028","display_name":"Topic Modeling","score":0.9983999729156494,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9950000047683716,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.7535631656646729},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7485307455062866},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.7389804124832153},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6267623901367188},{"id":"https://openalex.org/keywords/word2vec","display_name":"Word2vec","score":0.5868512392044067},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.581636369228363},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.5509827733039856},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5004522800445557},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46206626296043396},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.42848679423332214},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4051632583141327},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3416271209716797}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7535631656646729},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7485307455062866},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.7389804124832153},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6267623901367188},{"id":"https://openalex.org/C2776461190","wikidata":"https://www.wikidata.org/wiki/Q22673982","display_name":"Word2vec","level":3,"score":0.5868512392044067},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.581636369228363},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.5509827733039856},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5004522800445557},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46206626296043396},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.42848679423332214},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4051632583141327},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3416271209716797},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"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/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icnsc52481.2021.9702156","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnsc52481.2021.9702156","pdf_url":null,"source":{"id":"https://openalex.org/S4363608459","display_name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.5199999809265137}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W36903255","https://openalex.org/W1522301498","https://openalex.org/W1614298861","https://openalex.org/W1966553486","https://openalex.org/W1976526581","https://openalex.org/W2042281163","https://openalex.org/W2054141820","https://openalex.org/W2090883204","https://openalex.org/W2120861206","https://openalex.org/W2133564696","https://openalex.org/W2138204974","https://openalex.org/W2140190241","https://openalex.org/W2156387975","https://openalex.org/W2158698691","https://openalex.org/W2159094788","https://openalex.org/W2295739661","https://openalex.org/W2475334473","https://openalex.org/W2517540742","https://openalex.org/W2604662567","https://openalex.org/W2723293840","https://openalex.org/W2882319491","https://openalex.org/W2964341035","https://openalex.org/W3080642298","https://openalex.org/W3127524795","https://openalex.org/W3169936356","https://openalex.org/W4294170691","https://openalex.org/W6601546654","https://openalex.org/W6631190155","https://openalex.org/W6636510571","https://openalex.org/W6678040779","https://openalex.org/W6679434410","https://openalex.org/W6680106237","https://openalex.org/W6680704940","https://openalex.org/W6682691769","https://openalex.org/W6682889407"],"related_works":["https://openalex.org/W2980729574","https://openalex.org/W1560851690","https://openalex.org/W3092047717","https://openalex.org/W3110772647","https://openalex.org/W2770162183","https://openalex.org/W2894231409","https://openalex.org/W2947721150","https://openalex.org/W2995297654","https://openalex.org/W2969545244","https://openalex.org/W2781739396"],"abstract_inverted_index":{"The":[0,181],"current":[1],"embedding":[2,50,59,95],"method":[3,60,116],"neglects":[4],"to":[5,45,76,85,105,118,174],"deeply":[6],"explore":[7],"the":[8,11,15,22,29,35,70,74,78,91,98,106,120,132,151,164,176,198],"associations":[9],"between":[10],"items":[12,71],"created":[13,96],"by":[14,73,97],"same":[16,152],"item":[17,87,94,109,123,128,139,148,169,179,194],"creator,":[18],"and":[19,48,89,125,154,191],"directly":[20],"puts":[21],"long-tail":[23],"distributed":[24],"categorical":[25],"creator":[26,75,99,101],"feature":[27,114],"into":[28],"model":[30],"for":[31,63],"end-to-end":[32],"training.":[33],"Because":[34],"tail":[36],"data":[37],"provides":[38],"too":[39],"little":[40],"information,":[41],"it":[42],"is":[43,67],"difficult":[44],"train":[46],"stable":[47],"meaningful":[49],"vectors.":[51],"To":[52],"this":[53],"end,":[54],"we":[55,81,111,135,162],"propose":[56],"an":[57],"improved":[58],"Creator2Vec":[61,190],"used":[62],"recommender":[64],"systems,":[65],"which":[66],"based":[68],"on":[69,127,184,209],"produced":[72],"characterize":[77],"creator.":[79],"Specifically,":[80],"first":[82],"use":[83,138],"Word2Vec":[84],"generate":[86],"embedding,":[88],"take":[90],"average":[92],"of":[93,108,122,166,168,178,201],"as":[100,141,171,197],"embedding.":[102],"Then,":[103],"according":[104],"quality":[107,177],"comments,":[110],"design":[112],"a":[113,172,185],"extraction":[115],"weighted":[117,192],"mine":[119],"information":[121],"comments":[124,126,149],"comments.":[129,180],"Benefit":[130],"from":[131],"proposed":[133],"method,":[134],"not":[136],"only":[137],"ratings":[140],"features,":[142,196],"but":[143],"are":[144],"also":[145],"concern":[146],"that":[147,189],"with":[150],"rating":[153,195],"different":[155,158],"qualities":[156],"have":[157,206],"recommendation":[159,212],"effects.":[160],"Finally,":[161],"uses":[163],"number":[165],"likes":[167],"reviews":[170],"standard":[173],"measure":[175],"empirical":[182],"results":[183],"practical":[186],"dataset":[187],"show":[188],"cumulative":[193],"input":[199],"layer":[200],"common":[202],"deep":[203],"learning":[204],"models,":[205],"good":[207],"effects":[208],"binary":[210],"classification":[211],"tasks.":[213]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
