{"id":"https://openalex.org/W2979057167","doi":"https://doi.org/10.1145/3357384.3357924","title":"Relation-Aware Graph Convolutional Networks for Agent-Initiated Social E-Commerce Recommendation","display_name":"Relation-Aware Graph Convolutional Networks for Agent-Initiated Social E-Commerce Recommendation","publication_year":2019,"publication_date":"2019-11-03","ids":{"openalex":"https://openalex.org/W2979057167","doi":"https://doi.org/10.1145/3357384.3357924","mag":"2979057167"},"language":"en","primary_location":{"id":"doi:10.1145/3357384.3357924","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3357384.3357924","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Information and 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/A5062365263","display_name":"Fengli Xu","orcid":"https://orcid.org/0000-0002-5720-4026"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fengli Xu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087106517","display_name":"Jianxun Lian","orcid":"https://orcid.org/0000-0003-3108-5601"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianxun Lian","raw_affiliation_strings":["Miscrosoft Research Asia, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Miscrosoft Research Asia, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101854528","display_name":"Zhenyu Han","orcid":"https://orcid.org/0000-0001-9634-7962"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenyu Han","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100355277","display_name":"Yong Li","orcid":"https://orcid.org/0000-0001-5617-1659"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Li","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085088189","display_name":"Yujian Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yujian Xu","raw_affiliation_strings":["Beibei Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beibei Group, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044651577","display_name":"Xing Xie","orcid":"https://orcid.org/0000-0002-8608-8482"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xing Xie","raw_affiliation_strings":["Microsoft Research Asia, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5062365263"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":19.7106,"has_fulltext":false,"cited_by_count":95,"citation_normalized_percentile":{"value":0.99271908,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"529","last_page":"538"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9987000226974487,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.988099992275238,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8167099356651306},{"id":"https://openalex.org/keywords/news-aggregator","display_name":"News aggregator","score":0.6064071655273438},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5884517431259155},{"id":"https://openalex.org/keywords/social-graph","display_name":"Social graph","score":0.5123323202133179},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.4915517270565033},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.49005353450775146},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.47710829973220825},{"id":"https://openalex.org/keywords/social-network","display_name":"Social network (sociolinguistics)","score":0.4512641727924347},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.4330940544605255},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.41043734550476074},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3860873579978943},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3653239905834198},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3290434181690216},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.25728607177734375},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.24448683857917786},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.15955254435539246}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8167099356651306},{"id":"https://openalex.org/C180505990","wikidata":"https://www.wikidata.org/wiki/Q498267","display_name":"News aggregator","level":2,"score":0.6064071655273438},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5884517431259155},{"id":"https://openalex.org/C2777522414","wikidata":"https://www.wikidata.org/wiki/Q648457","display_name":"Social graph","level":3,"score":0.5123323202133179},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.4915517270565033},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.49005353450775146},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.47710829973220825},{"id":"https://openalex.org/C4727928","wikidata":"https://www.wikidata.org/wiki/Q17164759","display_name":"Social network (sociolinguistics)","level":3,"score":0.4512641727924347},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.4330940544605255},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.41043734550476074},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3860873579978943},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3653239905834198},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3290434181690216},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25728607177734375},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.24448683857917786},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.15955254435539246},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3357384.3357924","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3357384.3357924","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Information and 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":28,"referenced_works":["https://openalex.org/W103340358","https://openalex.org/W1991055526","https://openalex.org/W1994213885","https://openalex.org/W2009280107","https://openalex.org/W2039613841","https://openalex.org/W2054141820","https://openalex.org/W2130354913","https://openalex.org/W2144487656","https://openalex.org/W2146950091","https://openalex.org/W2154851992","https://openalex.org/W2187089797","https://openalex.org/W2244405900","https://openalex.org/W2604314403","https://openalex.org/W2605350416","https://openalex.org/W2624431344","https://openalex.org/W2743104969","https://openalex.org/W2792485195","https://openalex.org/W2807955117","https://openalex.org/W2808787330","https://openalex.org/W2884134047","https://openalex.org/W2911286998","https://openalex.org/W2962756421","https://openalex.org/W2962767366","https://openalex.org/W2963146368","https://openalex.org/W2982348041","https://openalex.org/W2986673834","https://openalex.org/W3037502709","https://openalex.org/W3104097132"],"related_works":["https://openalex.org/W2759391675","https://openalex.org/W1983063916","https://openalex.org/W4362644772","https://openalex.org/W2158052187","https://openalex.org/W2948091038","https://openalex.org/W2034279876","https://openalex.org/W2116533290","https://openalex.org/W2132828661","https://openalex.org/W3004249040","https://openalex.org/W2167351964"],"abstract_inverted_index":{"Recent":[0],"years":[1],"have":[2,75,91],"witnessed":[3],"a":[4,118,129,142,173,198],"phenomenal":[5],"success":[6],"of":[7,32,48,53,67],"agent-initiated":[8],"social":[9,24,33],"e-commerce":[10,34],"models,":[11],"which":[12,115],"encourage":[13],"users":[14],"to":[15,19,123,150,176,181,205],"become":[16],"selling":[17,57,191],"agents":[18,58,192],"promote":[20],"items":[21],"through":[22],"their":[23],"connections.":[25],"The":[26],"complex":[27],"interactions":[28,188],"in":[29,84,94,128,137,210,217],"this":[30],"type":[31],"can":[35],"be":[36],"formulated":[37],"as":[38,79],"Heterogeneous":[39],"Information":[40],"Networks":[41,73],"(HIN),":[42],"where":[43],"there":[44],"are":[45],"numerous":[46],"types":[47,52],"relations":[49,98,140],"between":[50],"three":[51],"nodes,":[54],"i.e.,":[55],"users,":[56,190],"and":[59,70,99,145,154,193,212],"items.":[60,194],"Learning":[61],"high":[62],"quality":[63],"node":[64,208],"embeddings":[65,165,209],"is":[66,203],"key":[68],"interest,":[69],"Graph":[71],"Convolutional":[72],"(GCNs)":[74],"recently":[76],"been":[77],"established":[78],"the":[80,147,164,186],"latest":[81],"state-of-the-art":[82],"methods":[83,216],"representation":[85],"learning.":[86],"However,":[87],"prior":[88],"GCN":[89,121],"models":[90],"fundamental":[92],"limitations":[93],"both":[95],"modeling":[96],"heterogeneous":[97,126,139],"efficiently":[100],"sampling":[101],"relevant":[102,155],"receptive":[103,156],"field":[104],"from":[105,167],"vast":[106],"neighborhood.":[107],"To":[108,161],"address":[109],"these":[110],"problems,":[111],"we":[112,170],"propose":[113],"RecoGCN,":[114],"stands":[116],"for":[117,158],"RElation-aware":[119],"CO-attentive":[120],"model,":[122],"effectively":[124,162],"aggregate":[125],"features":[127],"HIN.":[130],"It":[131],"makes":[132],"up":[133],"current":[134],"GCN's":[135],"limitation":[136],"modelling":[138],"with":[141],"relation-aware":[143],"aggregator,":[144],"leverages":[146],"semantic-aware":[148],"meta-paths":[149,183],"carve":[151],"out":[152],"concise":[153],"fields":[157],"each":[159],"node.":[160],"fuse":[163],"learned":[166],"different":[168,182],"meta-paths,":[169],"further":[171],"develop":[172],"co-attentive":[174],"mechanism":[175],"dynamically":[177],"assign":[178],"importance":[179],"weights":[180],"by":[184],"attending":[185],"three-way":[187],"among":[189],"Extensive":[195],"experiments":[196],"on":[197],"real-world":[199],"dataset":[200],"demonstrate":[201],"RecoGCN":[202],"able":[204],"learn":[206],"meaningful":[207],"HIN,":[211],"consistently":[213],"outperforms":[214],"baseline":[215],"recommendation":[218],"tasks.":[219]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":21},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":24},{"year":2020,"cited_by_count":20}],"updated_date":"2026-03-31T07:56:22.981413","created_date":"2025-10-10T00:00:00"}
