{"id":"https://openalex.org/W4384641458","doi":"https://doi.org/10.1145/3539618.3591632","title":"AdaMCL: Adaptive Fusion Multi-View Contrastive Learning for Collaborative Filtering","display_name":"AdaMCL: Adaptive Fusion Multi-View Contrastive Learning for Collaborative Filtering","publication_year":2023,"publication_date":"2023-07-18","ids":{"openalex":"https://openalex.org/W4384641458","doi":"https://doi.org/10.1145/3539618.3591632"},"language":"en","primary_location":{"id":"doi:10.1145/3539618.3591632","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539618.3591632","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"conference-paper","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/A5086985082","display_name":"Guanghui Zhu","orcid":"https://orcid.org/0000-0002-5069-5950"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanghui Zhu","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-5069-5950","affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101700685","display_name":"W. T. Lu","orcid":"https://orcid.org/0009-0002-9846-9132"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wang Lu","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0002-9846-9132","affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115598059","display_name":"Chunfeng Yuan","orcid":"https://orcid.org/0000-0002-8746-8137"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunfeng Yuan","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-8746-8137","affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007538828","display_name":"Yihua Huang","orcid":"https://orcid.org/0000-0003-1806-0936"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yihua Huang","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0003-1806-0936","affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I881766915"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1076","last_page":"1085"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9994999766349792,"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":0.9994999766349792,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.8312174081802368},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8006250858306885},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6034086346626282},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5561787486076355},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.5403563976287842},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5275437831878662},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.46723371744155884},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4490000009536743},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2463482916355133},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.18171542882919312}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.8312174081802368},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8006250858306885},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6034086346626282},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5561787486076355},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.5403563976287842},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5275437831878662},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.46723371744155884},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4490000009536743},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2463482916355133},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.18171542882919312},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3539618.3591632","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539618.3591632","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3076789159","display_name":null,"funder_award_id":"BK20210181","funder_id":"https://openalex.org/F4320322769","funder_display_name":"Natural Science Foundation of Jiangsu Province"}],"funders":[{"id":"https://openalex.org/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W2027731328","https://openalex.org/W2042281163","https://openalex.org/W2054141820","https://openalex.org/W2065427498","https://openalex.org/W2110953678","https://openalex.org/W2122925692","https://openalex.org/W2605350416","https://openalex.org/W2899291427","https://openalex.org/W2907492528","https://openalex.org/W2914050157","https://openalex.org/W2945827670","https://openalex.org/W2946617802","https://openalex.org/W2962992837","https://openalex.org/W3012816161","https://openalex.org/W3044311607","https://openalex.org/W3045200674","https://openalex.org/W3080566854","https://openalex.org/W3094605801","https://openalex.org/W3095602948","https://openalex.org/W3095937012","https://openalex.org/W3100278010","https://openalex.org/W3100324210","https://openalex.org/W3104326162","https://openalex.org/W3129482887","https://openalex.org/W3134210100","https://openalex.org/W3153325943","https://openalex.org/W3155919942","https://openalex.org/W3155928855","https://openalex.org/W3155936517","https://openalex.org/W3158371160","https://openalex.org/W3208227120","https://openalex.org/W4212835176","https://openalex.org/W4220909642","https://openalex.org/W4224983022","https://openalex.org/W6609612681","https://openalex.org/W6784694379"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W1590307681","https://openalex.org/W4312814274","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W2353836703","https://openalex.org/W41015297"],"abstract_inverted_index":{"Graph":[0],"collaborative":[1,113,181],"filtering":[2,182],"has":[3,31],"achieved":[4],"great":[5],"success":[6],"in":[7,24],"capturing":[8],"users'":[9],"preferences":[10],"over":[11],"items.":[12],"Despite":[13],"effectiveness,":[14],"graph":[15,51,112],"neural":[16],"network":[17],"(GNN)-based":[18],"methods":[19,44],"suffer":[20],"from":[21,133],"data":[22,39],"sparsity":[23],"real":[25],"scenarios.":[26],"Recently,":[27],"contrastive":[28,106,145],"learning":[29,107,146],"(CL)":[30],"been":[32],"used":[33],"to":[34,52,128,148,154],"address":[35],"the":[36,47,54,58,62,72,77,80,84,92,117,130,134,156],"problem":[37],"of":[38,61,83,91],"sparsity.":[40],"However,":[41],"most":[42],"CL-based":[43,73],"only":[45],"leverage":[46],"original":[48],"user-item":[49,135],"interaction":[50],"construct":[53,149],"CL":[55,151,168],"task,":[56],"lacking":[57],"explicit":[59],"exploitation":[60],"higher-order":[63,78,85,118,162],"information":[64,86,119,158],"(i.e.,":[65],"user-user":[66,137],"and":[67,89,136,178],"item-item":[68],"relationships).":[69],"Even":[70],"for":[71,111],"method":[74],"that":[75,174],"uses":[76],"information,":[79],"reception":[81],"field":[82],"is":[87,176],"fixed":[88],"regardless":[90],"difference":[93],"between":[94],"nodes.":[95],"In":[96],"this":[97],"paper,":[98],"we":[99,122,140,164],"propose":[100,123,141,165],"a":[101,142,166],"novel":[102],"adaptive":[103,125],"multi-view":[104,143],"fusion":[105,126,144],"framework,":[108],"named":[109],"AdaMCL,":[110],"filtering.":[114],"To":[115],"exploit":[116],"more":[120],"accurately,":[121],"an":[124],"strategy":[127],"fuse":[129],"embeddings":[131],"learned":[132],"graphs.":[138],"Moreover,":[139],"paradigm":[147],"effective":[150,177],"tasks.":[152],"Besides,":[153],"alleviate":[155],"noisy":[157],"caused":[159],"by":[160],"aggregating":[161],"neighbors,":[163],"layer-level":[167],"task.":[169],"Extensive":[170],"experimental":[171],"results":[172],"reveal":[173],"AdaMCL":[175],"outperforms":[179],"existing":[180],"models":[183],"significantly.":[184]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
