{"id":"https://openalex.org/W3094242471","doi":"https://doi.org/10.1145/3340531.3412695","title":"Decoupled Graph Convolution Network for Inferring Substitutable and Complementary Items","display_name":"Decoupled Graph Convolution Network for Inferring Substitutable and Complementary Items","publication_year":2020,"publication_date":"2020-10-19","ids":{"openalex":"https://openalex.org/W3094242471","doi":"https://doi.org/10.1145/3340531.3412695","mag":"3094242471"},"language":"en","primary_location":{"id":"doi:10.1145/3340531.3412695","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3340531.3412695","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th 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/A5101677601","display_name":"Yiding Liu","orcid":"https://orcid.org/0000-0001-6857-261X"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yiding Liu","raw_affiliation_strings":["Baidu Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102274943","display_name":"Yulong Gu","orcid":"https://orcid.org/0009-0007-3969-5734"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yulong Gu","raw_affiliation_strings":["JD.com, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD.com, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008043408","display_name":"Zhuoye Ding","orcid":"https://orcid.org/0000-0001-7430-5980"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuoye Ding","raw_affiliation_strings":["JD.com, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD.com, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084255802","display_name":"Junchao Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junchao Gao","raw_affiliation_strings":["JD.com, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD.com, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084559992","display_name":"Ziyi Guo","orcid":"https://orcid.org/0000-0002-0811-5086"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziyi Guo","raw_affiliation_strings":["JD.com, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD.com, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089195488","display_name":"Yongjun Bao","orcid":"https://orcid.org/0000-0002-4957-9813"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongjun Bao","raw_affiliation_strings":["JD.com, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD.com, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103185227","display_name":"Weipeng Yan","orcid":"https://orcid.org/0000-0001-5112-2655"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weipeng Yan","raw_affiliation_strings":["JD.com, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD.com, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5101677601"],"corresponding_institution_ids":["https://openalex.org/I98301712"],"apc_list":null,"apc_paid":null,"fwci":6.309,"has_fulltext":false,"cited_by_count":55,"citation_normalized_percentile":{"value":0.97152666,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2621","last_page":"2628"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9994000196456909,"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/T10028","display_name":"Topic Modeling","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.7603029012680054},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.6137385368347168},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5960871577262878},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4956322908401489},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4812852740287781},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4721940755844116},{"id":"https://openalex.org/keywords/complementarity","display_name":"Complementarity (molecular biology)","score":0.4298895299434662},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38311105966567993},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.25916796922683716},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.15513256192207336}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7603029012680054},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.6137385368347168},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5960871577262878},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4956322908401489},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4812852740287781},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4721940755844116},{"id":"https://openalex.org/C202269582","wikidata":"https://www.wikidata.org/wiki/Q2644277","display_name":"Complementarity (molecular biology)","level":2,"score":0.4298895299434662},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38311105966567993},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25916796922683716},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.15513256192207336},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","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},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3340531.3412695","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3340531.3412695","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5099999904632568,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1991418309","https://openalex.org/W2027731328","https://openalex.org/W2546938941","https://openalex.org/W2552027021","https://openalex.org/W2734820071","https://openalex.org/W2743104969","https://openalex.org/W2775589622","https://openalex.org/W2782696945","https://openalex.org/W2787612455","https://openalex.org/W2807021761","https://openalex.org/W2809418595","https://openalex.org/W2892485145","https://openalex.org/W2907492528","https://openalex.org/W2907496897","https://openalex.org/W2911286998","https://openalex.org/W2943373497","https://openalex.org/W2945266622","https://openalex.org/W2955081857","https://openalex.org/W2962793481","https://openalex.org/W2963601856","https://openalex.org/W2963707260","https://openalex.org/W2963911286","https://openalex.org/W2964965220","https://openalex.org/W2965316489","https://openalex.org/W2965857891","https://openalex.org/W2970066309","https://openalex.org/W2971998163","https://openalex.org/W3007094061","https://openalex.org/W3100848837","https://openalex.org/W3105136071"],"related_works":["https://openalex.org/W2521519254","https://openalex.org/W3139833644","https://openalex.org/W3123110765","https://openalex.org/W4383553409","https://openalex.org/W2104948296","https://openalex.org/W1735800226","https://openalex.org/W4285172739","https://openalex.org/W3123208392","https://openalex.org/W2212953222","https://openalex.org/W4372316851"],"abstract_inverted_index":{"Inferring":[0],"substitutable":[1,93],"and":[2,8,60,94,104,110,125,157],"complementary":[3,95],"items":[4],"is":[5,44,98,111,141,171],"an":[6],"important":[7],"fundamental":[9],"concern":[10],"for":[11,40,55,148],"recommendation":[12],"in":[13,20,106,176],"e-commerce":[14],"websites.":[15],"However,":[16],"the":[17,62,89,145,149,166,181],"item":[18,53,58,102,128],"relationships":[19],"real-world":[21,136,177],"are":[22,130],"usually":[23],"heterogeneous,":[24],"posing":[25],"great":[26],"challenges":[27],"to":[28,87,100,173],"conventional":[29],"methods":[30],"that":[31,139,169],"can":[32],"only":[33],"deal":[34],"with":[35,113],"homogeneous":[36],"relationships.":[37,71],"More":[38],"specifically,":[39],"this":[41,74],"problem,":[42],"there":[43],"a":[45,78,114],"lack":[46],"of":[47,91],"in-depth":[48],"investigation":[49],"on":[50,134,161],"1)":[51,121],"decoupling":[52],"semantics":[54,129],"modeling":[56],"heterogeneous":[57],"relationships,":[59],"at":[61,151,183],"same":[63],"time,":[64],"2)":[65,126],"incorporating":[66],"mutual":[67],"influence":[68],"between":[69,120],"different":[70,122,127],"To":[72],"fill":[73],"gap,":[75],"we":[76],"propose":[77],"novel":[79],"solution,":[80],"namely":[81],"Decoupled":[82],"Graph":[83],"Convolutional":[84],"Network":[85],"(DecGCN),":[86],"solve":[88],"problem":[90,150],"inferring":[92],"items.":[96],"DecGCN":[97,140,170],"designed":[99],"model":[101],"substitutability":[103],"complementarity":[105],"separated":[107],"embedding":[108],"spaces,":[109],"equipped":[112],"two-step":[115],"integration":[116],"scheme,where":[117],"inherent":[118],"influences":[119],"graph":[123],"structures":[124],"captured.":[131],"Our":[132],"experiments":[133],"three":[135],"datasets":[137],"demonstrate":[138],"more":[142],"effective":[143,172],"than":[144],"state-of-the-art":[146],"baselines":[147],"hand.":[152],"We":[153,179],"also":[154],"conduct":[155],"offline":[156],"online":[158],"A/B":[159],"tests":[160],"large-scale":[162],"industrial":[163],"data,":[164],"where":[165],"results":[167],"show":[168],"be":[174],"deployed":[175],"applications.":[178],"release":[180],"codes":[182],"https://github.com/liuyiding1993/CIKM2020_DecGCN.":[184]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}