{"id":"https://openalex.org/W4306316940","doi":"https://doi.org/10.1145/3511808.3557368","title":"ITSM-GCN","display_name":"ITSM-GCN","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4306316940","doi":"https://doi.org/10.1145/3511808.3557368"},"language":"en","primary_location":{"id":"doi:10.1145/3511808.3557368","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557368","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","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":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","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/A5013290772","display_name":"Kaiqi Gong","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kaiqi Gong","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101743034","display_name":"Xiao Song","orcid":"https://orcid.org/0000-0002-3565-2231"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Song","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035708362","display_name":"Senzhang Wang","orcid":"https://orcid.org/0000-0002-3615-4859"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Senzhang Wang","raw_affiliation_strings":["Central South University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102789179","display_name":"Songsong Liu","orcid":"https://orcid.org/0009-0005-2589-7488"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Songsong Liu","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100355277","display_name":"Yong Li","orcid":"https://orcid.org/0000-0001-5617-1659"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Li","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5013290772"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.437,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.61716937,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"614","last_page":"623"},"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/T11478","display_name":"Caching and Content Delivery","score":0.984000027179718,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.9706000089645386,"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/computer-science","display_name":"Computer science","score":0.754612922668457},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5274629592895508},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5095048546791077},{"id":"https://openalex.org/keywords/performance-improvement","display_name":"Performance improvement","score":0.4971597492694855},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46482565999031067},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.44833311438560486},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3645888864994049},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3431054949760437},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.20840629935264587},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.12644585967063904},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10678768157958984}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.754612922668457},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5274629592895508},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5095048546791077},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.4971597492694855},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46482565999031067},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.44833311438560486},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3645888864994049},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3431054949760437},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.20840629935264587},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.12644585967063904},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10678768157958984},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3511808.3557368","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557368","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","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":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4994113833","display_name":null,"funder_award_id":"2020YFB1712203","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1888005072","https://openalex.org/W1967507014","https://openalex.org/W2042281163","https://openalex.org/W2054141820","https://openalex.org/W2102035799","https://openalex.org/W2154851992","https://openalex.org/W2512971201","https://openalex.org/W2591957553","https://openalex.org/W2604433096","https://openalex.org/W2605350416","https://openalex.org/W2904156528","https://openalex.org/W2945827670","https://openalex.org/W2962756421","https://openalex.org/W2962763064","https://openalex.org/W2964051675","https://openalex.org/W2966799427","https://openalex.org/W2998431760","https://openalex.org/W2999649805","https://openalex.org/W3044311607","https://openalex.org/W3045200674","https://openalex.org/W3080456792","https://openalex.org/W3094605801","https://openalex.org/W3096507354","https://openalex.org/W3100278010","https://openalex.org/W3100324210","https://openalex.org/W3104097132","https://openalex.org/W3130062726","https://openalex.org/W3153325943","https://openalex.org/W3156861396","https://openalex.org/W3172854437","https://openalex.org/W3209428314","https://openalex.org/W3211143493","https://openalex.org/W4214875743","https://openalex.org/W4220909642"],"related_works":["https://openalex.org/W1185300216","https://openalex.org/W2961085424","https://openalex.org/W2954163146","https://openalex.org/W4224009465","https://openalex.org/W4306674287","https://openalex.org/W2899086345","https://openalex.org/W4286629047","https://openalex.org/W2896057011","https://openalex.org/W4205958290","https://openalex.org/W2042102171"],"abstract_inverted_index":{"Recently,":[0],"graph":[1],"convolutional":[2],"network":[3],"(GCN)":[4],"has":[5],"become":[6],"one":[7],"of":[8,87],"the":[9,85,97],"most":[10],"popular":[11],"and":[12,26,33,58,95,111,129,151,162,170],"state-of-the-art":[13,144],"collaborative":[14],"filtering":[15],"(CF)":[16],"methods.":[17],"Existing":[18],"GCN-based":[19,88,135,145],"CF":[20,89,146],"studies":[21],"have":[22,45],"made":[23],"many":[24],"meaningful":[25],"excellent":[27],"efforts":[28],"at":[29],"loss":[30],"function":[31],"design":[32,117],"embedding":[34],"propagation":[35],"improvement.":[36],"Despite":[37],"their":[38],"successes,":[39],"we":[40,92,116],"argue":[41],"that":[42,140],"existing":[43],"methods":[44],"not":[46],"yet":[47],"properly":[48],"explored":[49],"more":[50],"effective":[51],"sampling":[52,57,82,100],"strategy,":[53,102],"including":[54,148],"both":[55,108],"positive":[56,120],"negative":[59,99],"sampling.":[60],"To":[61],"tackle":[62],"this":[63],"limitation,":[64],"a":[65,126],"novel":[66],"framework":[67],"named":[68],"ITSM-GCN":[69,141,155],"is":[70],"proposed":[71],"to":[72,132],"carry":[73],"out":[74],"our":[75],"designed":[76],"Informative":[77],"Training":[78],"Sample":[79],"Mining":[80],"(ITSM)":[81],"strategy":[83],"for":[84,167],"learning":[86],"models.":[90],"Specifically,":[91],"first":[93],"adopt":[94],"improve":[96],"dynamic":[98],"(DNS)":[101],"which":[103],"achieves":[104],"considerable":[105],"improvements":[106],"in":[107],"training":[109,121],"efficiency":[110],"recommendation":[112],"performance.":[113],"More":[114],"importantly,":[115],"two":[118],"potentially":[119],"sample":[122],"mining":[123],"strategies,":[124],"namely":[125],"similarity-based":[127],"sampler":[128],"score-based":[130],"sampler,":[131],"further":[133],"enhance":[134],"CF.":[136],"Extensive":[137],"experiments":[138],"show":[139],"significantly":[142],"outperforms":[143],"models,":[147],"LightGCN,":[149],"SGL-ED":[150],"SimpleX.":[152],"For":[153],"example,":[154],"improves":[156],"on":[157,164],"SimpleX":[158],"by":[159],"12.0%,":[160],"3.0%,":[161],"1.2%":[163],"[email":[165],"protected]":[166],"Amazon-Books,":[168],"Yelp2018":[169],"Gowalla,":[171],"respectively.":[172]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2022-10-16T00:00:00"}