{"id":"https://openalex.org/W4414272395","doi":"https://doi.org/10.1109/ichms65439.2025.11154161","title":"Temporal Pattern-Aware QoS Prediction by Self-Attending Neural Tucker Factorization","display_name":"Temporal Pattern-Aware QoS Prediction by Self-Attending Neural Tucker Factorization","publication_year":2025,"publication_date":"2025-05-26","ids":{"openalex":"https://openalex.org/W4414272395","doi":"https://doi.org/10.1109/ichms65439.2025.11154161"},"language":"en","primary_location":{"id":"doi:10.1109/ichms65439.2025.11154161","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ichms65439.2025.11154161","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 5th International Conference on Human-Machine Systems (ICHMS)","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":null,"display_name":"Yikai Hou","orcid":null},"institutions":[{"id":"https://openalex.org/I1313079697","display_name":"Southwest University of Political Science & Law","ror":"https://ror.org/02pz16m08","country_code":"CN","type":"education","lineage":["https://openalex.org/I1313079697"]},{"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":true,"raw_author_name":"Yikai Hou","raw_affiliation_strings":["College of Computer and Information Science, School of Software, Southwest University,Chongqing,China"],"affiliations":[{"raw_affiliation_string":"College of Computer and Information Science, School of Software, Southwest University,Chongqing,China","institution_ids":["https://openalex.org/I142108993","https://openalex.org/I1313079697"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101903307","display_name":"Peng Tang","orcid":"https://orcid.org/0000-0003-3580-7667"},"institutions":[{"id":"https://openalex.org/I1313079697","display_name":"Southwest University of Political Science & Law","ror":"https://ror.org/02pz16m08","country_code":"CN","type":"education","lineage":["https://openalex.org/I1313079697"]},{"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":"Peng Tang","raw_affiliation_strings":["College of Computer and Information Science, School of Software, Southwest University,Chongqing,China"],"affiliations":[{"raw_affiliation_string":"College of Computer and Information Science, School of Software, Southwest University,Chongqing,China","institution_ids":["https://openalex.org/I142108993","https://openalex.org/I1313079697"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1313079697","https://openalex.org/I142108993"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22913292,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"211","last_page":"216"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11932","display_name":"Wireless Body Area Networks","score":0.9136999845504761,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11932","display_name":"Wireless Body Area Networks","score":0.9136999845504761,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9043999910354614,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/quality-of-service","display_name":"Quality of service","score":0.6897000074386597},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.595300018787384},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5134000182151794},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5034999847412109},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4228000044822693},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.4162999987602234},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.400299996137619},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.3853999972343445},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.3391000032424927}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8098999857902527},{"id":"https://openalex.org/C5119721","wikidata":"https://www.wikidata.org/wiki/Q220501","display_name":"Quality of service","level":2,"score":0.6897000074386597},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6061999797821045},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.595300018787384},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5509999990463257},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5134000182151794},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5034999847412109},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44839999079704285},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4228000044822693},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.4162999987602234},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.400299996137619},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.3853999972343445},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.3391000032424927},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3301999866962433},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.3273000121116638},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.31850001215934753},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30300000309944153},{"id":"https://openalex.org/C3019022308","wikidata":"https://www.wikidata.org/wiki/Q1418353","display_name":"Multidimensional data","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ichms65439.2025.11154161","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ichms65439.2025.11154161","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 5th International Conference on Human-Machine Systems (ICHMS)","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":30,"referenced_works":["https://openalex.org/W1753817908","https://openalex.org/W2056398894","https://openalex.org/W2119264846","https://openalex.org/W2119523409","https://openalex.org/W2162220364","https://openalex.org/W2294256350","https://openalex.org/W2325979489","https://openalex.org/W2517030840","https://openalex.org/W2571654287","https://openalex.org/W2605350416","https://openalex.org/W2606637053","https://openalex.org/W2901144993","https://openalex.org/W2928382658","https://openalex.org/W2980537499","https://openalex.org/W2996967608","https://openalex.org/W3019396605","https://openalex.org/W3156564332","https://openalex.org/W4200597037","https://openalex.org/W4205678748","https://openalex.org/W4205774517","https://openalex.org/W4291972732","https://openalex.org/W4293107381","https://openalex.org/W4315777759","https://openalex.org/W4385245566","https://openalex.org/W4386952034","https://openalex.org/W4389776655","https://openalex.org/W4390534276","https://openalex.org/W4392647365","https://openalex.org/W4403422439","https://openalex.org/W4406610818"],"related_works":[],"abstract_inverted_index":{"Quality-of-service":[0],"(QoS)":[1],"data":[2,36,124],"exhibit":[3],"dynamic":[4,66,161],"temporal":[5,31],"patterns":[6,16,73,119],"that":[7],"are":[8],"crucial":[9],"for":[10,37,62,91,112],"accurately":[11],"predicting":[12],"missing":[13,182],"values.":[14],"These":[15],"arise":[17],"from":[18,164,194],"the":[19,30,44,60,71,138,167,181],"interactions":[20],"between":[21],"users":[22],"and":[23,40,46,65,97,144],"services,":[24],"making":[25],"it":[26],"essential":[27],"to":[28,55,68,136,150,175],"capture":[29,70],"dynamics":[32],"inherent":[33],"in":[34,74,121,179,189],"such":[35],"precise":[38],"decision-making":[39],"service":[41],"selection.":[42],"As":[43],"size":[45],"complexity":[47],"of":[48,95,141],"QoS":[49,76,99,162,183,195],"datasets":[50,163],"increase,":[51],"existing":[52],"models":[53,178],"struggle":[54],"provide":[56],"accurate":[57],"predictions,":[58],"highlighting":[59],"need":[61],"more":[63],"flexible":[64],"methods":[67],"better":[69],"underlying":[72],"large-scale":[75],"data.":[77],"To":[78],"address":[79],"this":[80],"issue,":[81],"we":[82],"introduce":[83],"a":[84,126,132,147],"neural":[85,133],"network-based":[86],"tensor":[87],"factorization":[88,143],"approach":[89],"tailored":[90],"learning":[92,190],"spatiotemporal":[93,116,192],"representations":[94,193],"high-dimensional":[96],"incomplete":[98],"tensors,":[100],"namely":[101],"Self-Attending":[102],"Neural":[103],"Tucker":[104,142],"Factorization":[105],"(SANTucF).":[106],"The":[107],"model":[108,170],"is":[109],"elaborately":[110],"designed":[111],"modeling":[113],"intricate":[114],"nonlinear":[115,152],"feature":[117],"interaction":[118,154],"hidden":[120],"real":[122,165],"world":[123],"with":[125],"two-fold":[127],"idea.":[128],"It":[129],"first":[130],"employs":[131],"network":[134],"structure":[135],"generalize":[137],"traditional":[139],"framework":[140],"then":[145],"leverages":[146],"self-attending":[148],"module":[149],"enforce":[151],"latent":[153],"learning.":[155],"In":[156],"empirical":[157],"studies":[158],"on":[159],"two":[160],"applications,":[166],"proposed":[168],"SANTucF":[169],"demonstrates":[171,186],"superior":[172],"performance":[173],"compared":[174],"state-of-the-art":[176],"benchmark":[177],"estimating":[180],"observations.":[184],"This":[185],"its":[187],"effectiveness":[188],"non-linear":[191],"tensors.":[196]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
