{"id":"https://openalex.org/W2160853492","doi":"https://doi.org/10.1145/2554850.2555195","title":"Semi-supervised context-aware matrix factorization","display_name":"Semi-supervised context-aware matrix factorization","publication_year":2014,"publication_date":"2014-03-24","ids":{"openalex":"https://openalex.org/W2160853492","doi":"https://doi.org/10.1145/2554850.2555195","mag":"2160853492"},"language":"en","primary_location":{"id":"doi:10.1145/2554850.2555195","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2554850.2555195","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th Annual ACM Symposium on Applied Computing","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/A5051900701","display_name":"Yong Zheng","orcid":"https://orcid.org/0000-0003-4990-4580"},"institutions":[{"id":"https://openalex.org/I118353179","display_name":"DePaul University","ror":"https://ror.org/04xtx5t16","country_code":"US","type":"education","lineage":["https://openalex.org/I118353179"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yong Zheng","raw_affiliation_strings":["DePaul University, Chicago, Illinois"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DePaul University, Chicago, Illinois","institution_ids":["https://openalex.org/I118353179"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5051900701"],"corresponding_institution_ids":["https://openalex.org/I118353179"],"apc_list":null,"apc_paid":null,"fwci":0.2659,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.53779155,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"292","last_page":"293"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9664000272750854,"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":0.9351000189781189,"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/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.823878288269043},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.8010875582695007},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7720237970352173},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.6814444661140442},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.620361328125},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5276314616203308},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5227111577987671},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5069199204444885},{"id":"https://openalex.org/keywords/probabilistic-latent-semantic-analysis","display_name":"Probabilistic latent semantic analysis","score":0.4828494191169739},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.38384562730789185},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3260936737060547},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3229689598083496}],"concepts":[{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.823878288269043},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.8010875582695007},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7720237970352173},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.6814444661140442},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.620361328125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5276314616203308},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5227111577987671},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5069199204444885},{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.4828494191169739},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.38384562730789185},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3260936737060547},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3229689598083496},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2554850.2555195","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2554850.2555195","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th Annual ACM Symposium on Applied Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W56073274","https://openalex.org/W1546447935","https://openalex.org/W1560147776","https://openalex.org/W1576240472","https://openalex.org/W1994389483","https://openalex.org/W1998889130","https://openalex.org/W2016506327","https://openalex.org/W2102937240","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W1551384396","https://openalex.org/W2096865229","https://openalex.org/W2888805565","https://openalex.org/W2921491680","https://openalex.org/W2251863249","https://openalex.org/W4291700620","https://openalex.org/W2132052677","https://openalex.org/W3159709618","https://openalex.org/W4396666968","https://openalex.org/W2110027950"],"abstract_inverted_index":{"Context-aware":[0],"recommender":[1,23],"systems":[2,24],"(CARS)":[3],"additionally":[4],"take":[5],"contexts":[6,42,92,105,125],"into":[7],"consideration":[8],"and":[9,33,73,120,134,145],"try":[10],"to":[11,16,90,102],"adapt":[12],"users'":[13],"preferences":[14],"according":[15],"their":[17,39],"contextual":[18,66],"situations.":[19],"In":[20,112],"the":[21,81,94,104,115,142],"traditional":[22],"(RS),":[25],"latent":[26,34,47,82],"factor":[27],"models,":[28],"such":[29],"as":[30,80,136,154],"matrix":[31,149],"factorization":[32,150],"dirichlet":[35],"allocation,":[36],"have":[37,56,64],"demonstrated":[38],"efficiencies.":[40],"Apparently,":[41],"could":[43],"be":[44],"those":[45],"possible":[46],"factors":[48,131],"--":[49],"they":[50],"are":[51,132],"\"latent\"":[52,110,130],"ones":[53],"if":[54],"we":[55,63],"no":[57],"pre-knowledge":[58],"of":[59,99,109,129],"them,":[60],"but":[61,97],"currently":[62],"explicit":[65],"information":[67],"at":[68],"hand,":[69],"why":[70],"not":[71],"treat":[72],"use":[74,103],"them":[75,100],"in":[76,86,93,106,126,141],"a":[77,107,127,146,155],"similar":[78],"way":[79,108,128],"factors?":[83],"Most":[84],"research":[85,116,139],"CARS":[87,143],"seeks":[88],"ways":[89],"incorporate":[91],"recommendation":[95],"process,":[96],"none":[98],"continue":[101],"factors.":[111],"this":[113],"work,":[114],"ideas,":[117],"relevant":[118],"challenges":[119],"expected":[121],"outcomes":[122],"about":[123],"using":[124],"introduced":[133],"discussed":[135],"one":[137],"novel":[138],"direction":[140],"domain,":[144],"semi-supervised":[147],"context-aware":[148],"approach":[151],"is":[152],"proposed":[153],"result.":[156]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}