{"id":"https://openalex.org/W2987999300","doi":"https://doi.org/10.1145/3356250.3360038","title":"DeepAPP","display_name":"DeepAPP","publication_year":2019,"publication_date":"2019-11-05","ids":{"openalex":"https://openalex.org/W2987999300","doi":"https://doi.org/10.1145/3356250.3360038","mag":"2987999300"},"language":"en","primary_location":{"id":"doi:10.1145/3356250.3360038","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3356250.3360038","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th Conference on Embedded Networked Sensor Systems","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/A5020917990","display_name":"Zhihao Shen","orcid":"https://orcid.org/0000-0002-8389-3988"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhihao Shen","raw_affiliation_strings":["Xi'an Jiaotong University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087322305","display_name":"Kang Yang","orcid":"https://orcid.org/0000-0001-8248-4894"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kang Yang","raw_affiliation_strings":["Xi'an Jiaotong University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101915919","display_name":"Wan Du","orcid":"https://orcid.org/0000-0003-2057-2885"},"institutions":[{"id":"https://openalex.org/I2803209242","display_name":"University of California System","ror":"https://ror.org/00pjdza24","country_code":"US","type":"education","lineage":["https://openalex.org/I2803209242"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wan Du","raw_affiliation_strings":["University of California"],"affiliations":[{"raw_affiliation_string":"University of California","institution_ids":["https://openalex.org/I2803209242"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044233783","display_name":"Xi Zhao","orcid":"https://orcid.org/0000-0001-9983-6366"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xi Zhao","raw_affiliation_strings":["Xi'an Jiaotong University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103277719","display_name":"Jianhua Zou","orcid":"https://orcid.org/0000-0003-1632-4758"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianhua Zou","raw_affiliation_strings":["Xi'an Jiaotong University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5020917990"],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":2.623,"has_fulltext":false,"cited_by_count":41,"citation_normalized_percentile":{"value":0.90454666,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"153","last_page":"165"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12238","display_name":"Green IT and Sustainability","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T12238","display_name":"Green IT and Sustainability","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11478","display_name":"Caching and Content Delivery","score":0.9955999851226807,"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/T13553","display_name":"Age of Information Optimization","score":0.986299991607666,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8666597008705139},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8604300022125244},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5372791886329651},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5276297926902771},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5058083534240723},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48858770728111267},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4750426113605499},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.46118906140327454},{"id":"https://openalex.org/keywords/mobile-apps","display_name":"Mobile apps","score":0.444207102060318},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.414158433675766},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.14909866452217102}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8666597008705139},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8604300022125244},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5372791886329651},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5276297926902771},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5058083534240723},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48858770728111267},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4750426113605499},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.46118906140327454},{"id":"https://openalex.org/C2988145974","wikidata":"https://www.wikidata.org/wiki/Q620615","display_name":"Mobile apps","level":2,"score":0.444207102060318},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.414158433675766},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.14909866452217102},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3356250.3360038","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3356250.3360038","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th Conference on Embedded Networked Sensor Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7200000286102295,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1577404640","https://openalex.org/W1809720746","https://openalex.org/W1972598631","https://openalex.org/W1997666564","https://openalex.org/W2002658930","https://openalex.org/W2005567524","https://openalex.org/W2015057666","https://openalex.org/W2015218663","https://openalex.org/W2031745992","https://openalex.org/W2032654855","https://openalex.org/W2039370351","https://openalex.org/W2042169692","https://openalex.org/W2070412788","https://openalex.org/W2088394455","https://openalex.org/W2102346651","https://openalex.org/W2114979172","https://openalex.org/W2133253683","https://openalex.org/W2141350820","https://openalex.org/W2145339207","https://openalex.org/W2161628678","https://openalex.org/W2171837816","https://openalex.org/W2173248099","https://openalex.org/W2201581102","https://openalex.org/W2215378786","https://openalex.org/W2260756217","https://openalex.org/W2328234520","https://openalex.org/W2509009161","https://openalex.org/W2517712916","https://openalex.org/W2575891491","https://openalex.org/W2576937703","https://openalex.org/W2767256145","https://openalex.org/W2769747407","https://openalex.org/W2782675250","https://openalex.org/W2807939108","https://openalex.org/W2860338957","https://openalex.org/W2885291434","https://openalex.org/W2963864421","https://openalex.org/W2988035443","https://openalex.org/W2997842691","https://openalex.org/W3138125150","https://openalex.org/W4394672593","https://openalex.org/W6668954134","https://openalex.org/W6684205842","https://openalex.org/W6687681856","https://openalex.org/W6701936466","https://openalex.org/W6713134421"],"related_works":["https://openalex.org/W4306904969","https://openalex.org/W2138720691","https://openalex.org/W4362501864","https://openalex.org/W4380318855","https://openalex.org/W3084456289","https://openalex.org/W2024136090","https://openalex.org/W4391331176","https://openalex.org/W2031695474","https://openalex.org/W2586732548","https://openalex.org/W2964765435"],"abstract_inverted_index":{"This":[0,59],"paper":[1,60],"aims":[2],"to":[3,31,39,89,94],"predict":[4],"the":[5,47,91,95,138,164,168],"apps":[6],"a":[7,62,72,104,116,124,132,144],"user":[8],"will":[9],"open":[10],"on":[11,143],"her":[12],"mobile":[13,33],"device":[14],"next.":[15],"Such":[16],"an":[17,41,83],"information":[18],"is":[19,37,87],"essential":[20],"for":[21,120,127,136],"many":[22],"smartphone":[23],"operations,":[24],"e.g.,":[25],"app":[26,56,79,97,147],"pre-loading":[27],"and":[28,50,131,158,162],"content":[29],"pre-caching,":[30],"save":[32],"energy.":[34],"However,":[35],"it":[36],"hard":[38],"build":[40],"explicit":[42],"model":[43],"that":[44,151],"accurately":[45],"depicts":[46],"affecting":[48,52],"factors":[49],"their":[51],"mechanism":[53],"of":[54,160,167,175,185],"time-varying":[55,96],"usage":[57,80,98,148],"behavior.":[58,99],"presents":[61],"deep":[63,106],"reinforcement":[64,107],"learning":[65,108],"framework,":[66],"named":[67],"as":[68],"DeepAPP,":[69],"which":[70],"learns":[71],"model-free":[73],"predictive":[74,92],"neural":[75],"network":[76,93],"from":[77],"historical":[78],"data.":[81],"Meanwhile,":[82],"online":[84],"updating":[85],"strategy":[86],"designed":[88],"adapt":[90],"To":[100],"transform":[101],"DeepAPP":[102,152,180],"into":[103],"practical":[105],"system,":[109],"several":[110],"challenges":[111],"are":[112],"addressed":[113],"by":[114,170],"developing":[115],"context":[117],"representation":[118],"method":[119],"complex":[121],"contextual":[122],"environment,":[123],"general":[125],"agent":[126,135],"overcoming":[128],"data":[129],"sparsity":[130],"lightweight":[133],"personalized":[134],"minimizing":[137],"prediction":[139,165],"time.":[140],"Extensive":[141],"experiments":[142],"large-scale":[145],"anonymized":[146],"dataset":[149],"reveal":[150],"provides":[153],"high":[154],"accuracy":[155],"(precision":[156],"70.6%":[157],"recall":[159],"62.4%)":[161],"reduces":[163],"time":[166,184],"state-of-the-art":[169],"6.58\u00d7.":[171],"A":[172],"field":[173],"experiment":[174],"29":[176],"participants":[177],"also":[178],"demonstrates":[179],"can":[181],"effectively":[182],"reduce":[183],"loading":[186],"apps.":[187]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2019-11-22T00:00:00"}