{"id":"https://openalex.org/W4403577430","doi":"https://doi.org/10.1145/3627673.3679701","title":"HGCH: A Hyperbolic Graph Convolution Network Model for Heterogeneous Collaborative Graph Recommendation","display_name":"HGCH: A Hyperbolic Graph Convolution Network Model for Heterogeneous Collaborative Graph Recommendation","publication_year":2024,"publication_date":"2024-10-20","ids":{"openalex":"https://openalex.org/W4403577430","doi":"https://doi.org/10.1145/3627673.3679701"},"language":"en","primary_location":{"id":"doi:10.1145/3627673.3679701","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627673.3679701","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd 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/A5077805743","display_name":"Lu Zhang","orcid":null},"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":"Lu Zhang","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/A5037370648","display_name":"Ning Wu","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":false,"raw_author_name":"Ning Wu","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":2,"corresponding_author_ids":["https://openalex.org/A5077805743"],"corresponding_institution_ids":["https://openalex.org/I47720641"],"apc_list":null,"apc_paid":null,"fwci":4.5806,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.95203947,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3186","last_page":"3196"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9990000128746033,"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":0.978600025177002,"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":0.662521243095398},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6060918569564819},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.49553048610687256},{"id":"https://openalex.org/keywords/voltage-graph","display_name":"Voltage graph","score":0.49202534556388855},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4067439138889313},{"id":"https://openalex.org/keywords/line-graph","display_name":"Line graph","score":0.35900962352752686},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1357894241809845}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.662521243095398},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6060918569564819},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.49553048610687256},{"id":"https://openalex.org/C22149727","wikidata":"https://www.wikidata.org/wiki/Q7940747","display_name":"Voltage graph","level":4,"score":0.49202534556388855},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4067439138889313},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.35900962352752686},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1357894241809845},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3627673.3679701","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627673.3679701","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd 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":35,"referenced_works":["https://openalex.org/W103340358","https://openalex.org/W2069870183","https://openalex.org/W2290847742","https://openalex.org/W2400540722","https://openalex.org/W2579549467","https://openalex.org/W2600702321","https://openalex.org/W2884134047","https://openalex.org/W2886970679","https://openalex.org/W2893775232","https://openalex.org/W2945623882","https://openalex.org/W2945827670","https://openalex.org/W2963653811","https://openalex.org/W2966349618","https://openalex.org/W3034992133","https://openalex.org/W3044311607","https://openalex.org/W3045200674","https://openalex.org/W3049759836","https://openalex.org/W3080152140","https://openalex.org/W3098934552","https://openalex.org/W3100157108","https://openalex.org/W3100278010","https://openalex.org/W3100324210","https://openalex.org/W3152663991","https://openalex.org/W3153106544","https://openalex.org/W3171364227","https://openalex.org/W3195582280","https://openalex.org/W3201966249","https://openalex.org/W3208227120","https://openalex.org/W3210549871","https://openalex.org/W3211143493","https://openalex.org/W4206865191","https://openalex.org/W4221139626","https://openalex.org/W4224295683","https://openalex.org/W4286231814","https://openalex.org/W4306317072"],"related_works":["https://openalex.org/W2903067171","https://openalex.org/W2380035736","https://openalex.org/W1927597435","https://openalex.org/W3207232533","https://openalex.org/W1568744482","https://openalex.org/W4299307887","https://openalex.org/W2952803432","https://openalex.org/W2106452164","https://openalex.org/W2389138578","https://openalex.org/W2954009223"],"abstract_inverted_index":{"User-item":[0],"interaction":[1],"data":[2],"in":[3,152],"collaborative":[4,79,114,124],"filtering":[5,115],"and":[6,38,41,66,81,92,126,196,205],"graph":[7,125,139],"modeling":[8],"tasks":[9],"often":[10],"exhibit":[11],"power-law":[12],"characteristics,":[13],"which":[14],"suggest":[15],"the":[16,34,78,134,138,156,172,197],"suitability":[17],"of":[18,36,137],"hyperbolic":[19,39,58,153,169,181],"space":[20,59,69,154],"modeling.":[21],"Hyperbolic":[22,102],"Graph":[23,103],"Convolution":[24],"Neural":[25],"Networks":[26],"(HGCNs)":[27],"are":[28],"a":[29,122,180],"novel":[30],"technique":[31],"that":[32,75,116,200],"leverages":[33],"advantages":[35],"GCN":[37],"space,":[40],"then":[42,148],"achieves":[43,202],"remarkable":[44],"results.":[45],"However,":[46],"existing":[47],"HGCN":[48],"methods":[49],"have":[50],"several":[51],"drawbacks:":[52],"they":[53,71],"fail":[54],"to":[55,62,88,185],"fully":[56],"leverage":[57],"properties":[60],"due":[61,87],"arbitrary":[63],"embedding":[64],"initialization":[65],"imprecise":[67],"tangent":[68],"aggregation;":[70],"overlook":[72],"auxiliary":[73],"information":[74,120],"could":[76],"enrich":[77],"graph;":[80],"their":[82],"training":[83,128],"convergence":[84,129],"is":[85],"slow":[86],"margin":[89],"ranking":[90],"loss":[91],"random":[93],"negative":[94,183],"sampling.":[95],"To":[96],"overcome":[97],"these":[98],"challenges,":[99],"we":[100],"propose":[101],"Collaborative":[104],"for":[105,113,159],"Heterogeneous":[106],"Recommendation":[107],"(HGCH),":[108],"an":[109],"enhanced":[110],"HGCN-based":[111],"model":[112],"integrates":[117],"diverse":[118],"side":[119],"into":[121],"heterogeneous":[123],"improves":[127],"speed.":[130],"HGCH":[131,178,191,201],"first":[132],"preserves":[133],"long-tailed":[135],"nature":[136],"by":[140,171],"initializing":[141],"node":[142],"embeddings":[143,166],"with":[144,175],"power":[145],"law":[146],"prior;":[147],"it":[149,163],"aggregates":[150],"neighbors":[151],"using":[155],"gyromidpoint":[157],"method":[158],"accurate":[160],"computation;":[161],"finally,":[162],"fuses":[164],"multiple":[165],"from":[167],"different":[168],"spaces":[170],"gate":[173],"fusion":[174],"prior.":[176],"Moreover,":[177],"employs":[179],"user-specific":[182],"sampling":[184],"speed":[186],"up":[187],"convergence.":[188],"We":[189],"evaluate":[190],"on":[192],"four":[193],"real":[194],"datasets,":[195],"results":[198,204],"show":[199],"competitive":[203],"outperforms":[206],"leading":[207],"baselines,":[208],"including":[209],"HGCNs.":[210],"Extensive":[211],"ablation":[212],"studies":[213],"further":[214],"confirm":[215],"its":[216],"effectiveness.":[217]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
