{"id":"https://openalex.org/W4306317301","doi":"https://doi.org/10.1145/3511808.3557390","title":"MDGCF","display_name":"MDGCF","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4306317301","doi":"https://doi.org/10.1145/3511808.3557390"},"language":"en","primary_location":{"id":"doi:10.1145/3511808.3557390","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557390","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/A5100337866","display_name":"Guohui Li","orcid":"https://orcid.org/0000-0001-6984-1914"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Guohui Li","raw_affiliation_strings":["Huazhong University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007911614","display_name":"Zhiqiang Guo","orcid":"https://orcid.org/0000-0001-9393-4854"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiqiang Guo","raw_affiliation_strings":["Huazhong University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100605911","display_name":"Jianjun Li","orcid":"https://orcid.org/0000-0002-5265-7624"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianjun Li","raw_affiliation_strings":["Huazhong University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100669681","display_name":"Chaoyang Wang","orcid":"https://orcid.org/0000-0003-4371-7514"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaoyang Wang","raw_affiliation_strings":["Huazhong University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100337866"],"corresponding_institution_ids":["https://openalex.org/I47720641"],"apc_list":null,"apc_paid":null,"fwci":0.731,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.72930073,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1094","last_page":"1103"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9639000296592712,"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":0.9639000296592712,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9624999761581421,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4946450889110565}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4946450889110565}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3511808.3557390","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557390","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":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W331119053","https://openalex.org/W1486317198","https://openalex.org/W1966553486","https://openalex.org/W2028988057","https://openalex.org/W2042281163","https://openalex.org/W2054141820","https://openalex.org/W2101409192","https://openalex.org/W2155106456","https://openalex.org/W2253995343","https://openalex.org/W2605350416","https://openalex.org/W2740920897","https://openalex.org/W2786995169","https://openalex.org/W2807021761","https://openalex.org/W2888838693","https://openalex.org/W2894039884","https://openalex.org/W2897660518","https://openalex.org/W2914721378","https://openalex.org/W2945827670","https://openalex.org/W2963085847","https://openalex.org/W2971196067","https://openalex.org/W2998431760","https://openalex.org/W3003372423","https://openalex.org/W3035135368","https://openalex.org/W3044311607","https://openalex.org/W3045200674","https://openalex.org/W3088777230","https://openalex.org/W3094605801","https://openalex.org/W3099386565","https://openalex.org/W3100278010","https://openalex.org/W3100324210","https://openalex.org/W3100848837","https://openalex.org/W3100921056","https://openalex.org/W3115386848","https://openalex.org/W3153325943","https://openalex.org/W3154113024","https://openalex.org/W3155496675","https://openalex.org/W3156622960","https://openalex.org/W3156861396","https://openalex.org/W3211009588","https://openalex.org/W3211143493","https://openalex.org/W4226368340","https://openalex.org/W6600050674","https://openalex.org/W6601215536"],"related_works":["https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2093578348","https://openalex.org/W2376932109","https://openalex.org/W2382290278","https://openalex.org/W2049775471","https://openalex.org/W2350741829","https://openalex.org/W3004735627"],"abstract_inverted_index":{"Due":[0],"to":[1,40,48,92,130,163],"the":[2,19,36,50,56,87,94,139,176,179],"success":[3],"of":[4,97,178],"graph":[5,39,68,129],"convolutional":[6],"networks":[7],"(GCNs)":[8],"in":[9,13,22,66],"effectively":[10],"extracting":[11],"features":[12,157],"non-Euclidean":[14],"spaces,":[15],"GCNs":[16],"has":[17],"become":[18],"rising":[20],"star":[21],"implicit":[23],"collaborative":[24],"filtering.":[25],"Existing":[26],"works,":[27],"while":[28],"encouraging,":[29],"typically":[30],"adopt":[31],"simple":[32],"aggregation":[33],"operation":[34],"on":[35,122,152,170],"user-item":[37],"bipartite":[38],"model":[41,187],"user":[42,196],"and":[43,60,89,111,144,161],"item":[44],"representations,":[45,166],"but":[46],"neglect":[47],"mine":[49],"sufficient":[51],"dependencies":[52,91,128,141,191],"between":[53,58,192],"nodes,":[54],"e.g.,":[55],"relationships":[57],"users/items":[59],"their":[61,165],"neighbors":[62],"(or":[63],"congeners),":[64],"resulting":[65],"inadequate":[67],"representation":[69,95],"learning.":[70],"To":[71],"address":[72],"these":[73],"problems,":[74],"we":[75,105,124,146,154],"propose":[76],"a":[77,116,126],"novel":[78],"Multi-Dependency":[79],"Graph":[80],"Collaborative":[81],"Filtering":[82],"(MDGCF)":[83],"model,":[84],"which":[85,123,153],"mines":[86],"neighborhood-":[88],"homogeneous-level":[90,140],"enhance":[93],"power":[96],"graph-based":[98],"CF":[99],"models.":[100],"Specifically,":[101],"for":[102,194],"neighborhood-level":[103,118,127],"dependencies,":[104],"explicitly":[106],"consider":[107],"both":[108],"popularity":[109],"score":[110],"preference":[112],"correlation":[113],"by":[114,136],"designing":[115],"joint":[117],"dependency":[119],"weight,":[120],"based":[121,151],"construct":[125,147],"capture":[131,189],"higher-order":[132],"interaction":[133],"features.":[134],"Besides,":[135],"adaptively":[137],"mining":[138],"among":[142],"users":[143,160],"items,":[145],"two":[148],"homogeneous":[149,159],"graphs,":[150],"further":[155],"aggregate":[156],"from":[158],"items":[162],"supplement":[164],"respectively.":[167],"Extensive":[168],"experiments":[169,183],"three":[171],"real-world":[172],"benchmark":[173],"datasets":[174],"demonstrate":[175],"effectiveness":[177],"proposed":[180],"MDGCF.":[181],"Further":[182],"reveal":[184],"that":[185],"our":[186],"can":[188],"rich":[190],"nodes":[193],"explaining":[195],"behaviors.":[197]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2022-10-16T00:00:00"}