{"id":"https://openalex.org/W4417508617","doi":"https://doi.org/10.1109/tnsm.2025.3644937","title":"A Sampling-Neighborhood-Regularized Latent Factorization of Tensor for Dynamic QoS Estimation","display_name":"A Sampling-Neighborhood-Regularized Latent Factorization of Tensor for Dynamic QoS Estimation","publication_year":2025,"publication_date":"2025-12-19","ids":{"openalex":"https://openalex.org/W4417508617","doi":"https://doi.org/10.1109/tnsm.2025.3644937"},"language":null,"primary_location":{"id":"doi:10.1109/tnsm.2025.3644937","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnsm.2025.3644937","pdf_url":null,"source":{"id":"https://openalex.org/S173527311","display_name":"IEEE Transactions on Network and Service Management","issn_l":"1932-4537","issn":["1932-4537","2373-7379"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Network and Service Management","raw_type":"journal-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/A5065313978","display_name":"Xiuqin Xu","orcid":"https://orcid.org/0000-0001-6639-5269"},"institutions":[{"id":"https://openalex.org/I111753288","display_name":"Fujian Normal University","ror":"https://ror.org/020azk594","country_code":"CN","type":"education","lineage":["https://openalex.org/I111753288"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiuqin Xu","raw_affiliation_strings":["College of Computer and Cyber Security, Fujian Normal University, Fuzhou, Fujian, China","College of Computer and Cyber Security, Fujian Normal University, Fujian, China"],"raw_orcid":"https://orcid.org/0000-0001-6639-5269","affiliations":[{"raw_affiliation_string":"College of Computer and Cyber Security, Fujian Normal University, Fuzhou, Fujian, China","institution_ids":["https://openalex.org/I111753288"]},{"raw_affiliation_string":"College of Computer and Cyber Security, Fujian Normal University, Fujian, China","institution_ids":["https://openalex.org/I111753288"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101851003","display_name":"Ming\u2010Wei Lin","orcid":"https://orcid.org/0000-0003-2026-7178"},"institutions":[{"id":"https://openalex.org/I111753288","display_name":"Fujian Normal University","ror":"https://ror.org/020azk594","country_code":"CN","type":"education","lineage":["https://openalex.org/I111753288"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingwei Lin","raw_affiliation_strings":["College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-2026-7178","affiliations":[{"raw_affiliation_string":"College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China","institution_ids":["https://openalex.org/I111753288"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100437308","display_name":"Zeshui Xu","orcid":"https://orcid.org/0000-0003-3547-2908"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zeshui Xu","raw_affiliation_strings":["Business School, Sichuan University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0003-3547-2908","affiliations":[{"raw_affiliation_string":"Business School, Sichuan University, Chengdu, China","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088955392","display_name":"Xin Luo","orcid":"https://orcid.org/0000-0002-1348-5305"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Luo","raw_affiliation_strings":["College of Computer and Information Science, Southwest University, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0002-1348-5305","affiliations":[{"raw_affiliation_string":"College of Computer and Information Science, Southwest University, Chongqing, China","institution_ids":["https://openalex.org/I142108993"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.018,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.91597151,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"23","issue":null,"first_page":"1707","last_page":"1722"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.5631999969482422,"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":0.5631999969482422,"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/T11478","display_name":"Caching and Content Delivery","score":0.07270000129938126,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.056699998676776886,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/quality-of-service","display_name":"Quality of service","score":0.667900025844574},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.6078000068664551},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5796999931335449},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.5726000070571899},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.5564000010490417},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5453000068664551},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.49480000138282776},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.3594000041484833}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7717000246047974},{"id":"https://openalex.org/C5119721","wikidata":"https://www.wikidata.org/wiki/Q220501","display_name":"Quality of service","level":2,"score":0.667900025844574},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.6078000068664551},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5796999931335449},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.5726000070571899},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.5564000010490417},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5453000068664551},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5152000188827515},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.49480000138282776},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.36809998750686646},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3594000041484833},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.3490999937057495},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.34790000319480896},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3472999930381775},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.335999995470047},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.31709998846054077},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.3125},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3073999881744385},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.28999999165534973},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.26109999418258667},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tnsm.2025.3644937","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnsm.2025.3644937","pdf_url":null,"source":{"id":"https://openalex.org/S173527311","display_name":"IEEE Transactions on Network and Service Management","issn_l":"1932-4537","issn":["1932-4537","2373-7379"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Network and Service Management","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5310342163","display_name":null,"funder_award_id":"62272103","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8515641209","display_name":null,"funder_award_id":"62272078","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W2020999234","https://openalex.org/W2024165284","https://openalex.org/W2030239032","https://openalex.org/W2073178357","https://openalex.org/W2088950591","https://openalex.org/W2089349245","https://openalex.org/W2165702682","https://openalex.org/W2390938437","https://openalex.org/W2528907418","https://openalex.org/W2611027303","https://openalex.org/W2761830048","https://openalex.org/W2772157149","https://openalex.org/W2789590212","https://openalex.org/W2802440232","https://openalex.org/W2899291427","https://openalex.org/W2928382658","https://openalex.org/W2969519120","https://openalex.org/W2974696853","https://openalex.org/W2996967608","https://openalex.org/W2997546679","https://openalex.org/W3045200674","https://openalex.org/W3089727447","https://openalex.org/W3092642260","https://openalex.org/W3130337639","https://openalex.org/W3153812903","https://openalex.org/W4226210383","https://openalex.org/W4231517135","https://openalex.org/W4287887777","https://openalex.org/W4299965338","https://openalex.org/W4308148980","https://openalex.org/W4318823358","https://openalex.org/W4320490877","https://openalex.org/W4322730943","https://openalex.org/W4327662601","https://openalex.org/W4366590125","https://openalex.org/W4385287043","https://openalex.org/W4387623961","https://openalex.org/W4388505317","https://openalex.org/W4393631728","https://openalex.org/W4396605565","https://openalex.org/W4400412030","https://openalex.org/W4402542691","https://openalex.org/W4403722015","https://openalex.org/W4404102190","https://openalex.org/W4406614563","https://openalex.org/W4406747626","https://openalex.org/W4407507065","https://openalex.org/W4407782988","https://openalex.org/W4407922777","https://openalex.org/W4412972614"],"related_works":[],"abstract_inverted_index":{"Since":[0],"similar":[1,5,12],"users":[2],"frequently":[3],"exhibit":[4],"Quality":[6],"of":[7,27,40,84,108,135,150,154],"Service":[8],"(QoS)":[9],"when":[10],"accessing":[11],"services,":[13],"effectively":[14],"capturing":[15],"neighborhood":[16,65],"information":[17],"hidden":[18],"in":[19,48,52,63,178],"QoS":[20,30,112,169],"data":[21],"becomes":[22],"critical":[23],"for":[24],"latent":[25,82,95],"factorization":[26,83],"tensor":[28,85],"(LFT)-based":[29],"estimators.":[31],"Current":[32],"LFT":[33,143],"models":[34,177],"either":[35],"calculate":[36],"the":[37,64,70,106,116,123,128,131,147,161],"complete":[38],"set":[39,66],"neighborhoods":[41,149],"or":[42],"do":[43],"not":[44,60],"consider":[45],"neighborhoods,":[46],"resulting":[47],"a":[49,80,141],"rapid":[50],"rise":[51],"model":[53,87],"complexity":[54],"and":[55,110,152,182],"poor":[56],"estimation":[57,180],"accuracy.":[58],"Moreover,":[59],"every":[61],"neighbor":[62],"is":[67,120],"beneficial":[68],"to":[69,101,126,159],"user/service":[71],"entity.":[72],"To":[73],"address":[74],"these":[75],"limitations,":[76],"this":[77],"study":[78],"proposes":[79],"sampling-neighborhood-regularized":[81,142],"(SNLFT)":[86],"with":[88],"three":[89],"key":[90],"ideas:":[91],"1)":[92],"extracting":[93],"primal":[94,132],"factors":[96],"(LFs),":[97],"which":[98,119],"are":[99,157],"obtained":[100],"express":[102],"related":[103],"entities":[104,136,151],"on":[105,166],"basis":[107],"high-dimensional":[109],"incomplete":[111],"data;":[113],"2)":[114],"constructing":[115],"sampling-neighborhood":[117],"set,":[118],"acquired":[121],"using":[122],"Gibbs":[124],"sampling":[125,148],"reflect":[127],"similarities":[129],"between":[130],"LF":[133],"vectors":[134],"over":[137],"time;":[138],"3)":[139],"developing":[140],"model,":[144],"where":[145],"all":[146],"L2-norm":[153],"desirable":[155],"LFs":[156],"employed":[158],"regularize":[160],"objective":[162],"function.":[163],"Extensive":[164],"experiments":[165],"eight":[167],"dynamic":[168],"datasets":[170],"demonstrate":[171],"that":[172],"SNLFT":[173],"significantly":[174],"outperforms":[175],"state-of-the-art":[176],"both":[179],"accuracy":[181],"computational":[183],"efficiency.":[184]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-12-19T00:00:00"}