{"id":"https://openalex.org/W4385562640","doi":"https://doi.org/10.1145/3580305.3599428","title":"Meta Graph Learning for Long-tail Recommendation","display_name":"Meta Graph Learning for Long-tail Recommendation","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385562640","doi":"https://doi.org/10.1145/3580305.3599428"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599428","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599428","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 SIGKDD Conference on Knowledge Discovery and Data Mining","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/A5026130226","display_name":"Chunyu Wei","orcid":"https://orcid.org/0000-0002-5802-5759"},"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":"Chunyu Wei","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/A5101456567","display_name":"Jian Liang","orcid":"https://orcid.org/0000-0001-5352-0278"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jian Liang","raw_affiliation_strings":["Independent Researcher, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Independent Researcher, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103149574","display_name":"Di Liu","orcid":"https://orcid.org/0000-0001-8633-8747"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Di Liu","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070810556","display_name":"Zehui Dai","orcid":"https://orcid.org/0000-0002-0406-8219"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zehui Dai","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036106799","display_name":"Mang Li","orcid":"https://orcid.org/0009-0009-1802-918X"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mang Li","raw_affiliation_strings":["Alibaba Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, Hangzhou, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100455768","display_name":"Fei Wang","orcid":"https://orcid.org/0000-0001-9459-9461"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fei Wang","raw_affiliation_strings":["Cornell University, New York, USA"],"affiliations":[{"raw_affiliation_string":"Cornell University, New York, USA","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5026130226"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":9.5896,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.98016923,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2512","last_page":"2522"},"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":0.9896000027656555,"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/T10028","display_name":"Topic Modeling","score":0.9861999750137329,"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":0.7732877731323242},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.6393933296203613},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.606460452079773},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.6013844013214111},{"id":"https://openalex.org/keywords/popularity","display_name":"Popularity","score":0.5801246166229248},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4365113377571106},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4308621287345886},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4296714663505554},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4295955300331116}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7732877731323242},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.6393933296203613},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.606460452079773},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.6013844013214111},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.5801246166229248},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4365113377571106},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4308621287345886},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4296714663505554},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4295955300331116},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3580305.3599428","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599428","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 SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1875842236","https://openalex.org/W2023954349","https://openalex.org/W2054141820","https://openalex.org/W2118978333","https://openalex.org/W2140942692","https://openalex.org/W2148143831","https://openalex.org/W2560674852","https://openalex.org/W2605350416","https://openalex.org/W2723293840","https://openalex.org/W2747329762","https://openalex.org/W2748058847","https://openalex.org/W2788283425","https://openalex.org/W2793585241","https://openalex.org/W2945827670","https://openalex.org/W2963017945","https://openalex.org/W2963555845","https://openalex.org/W2963680249","https://openalex.org/W2963878746","https://openalex.org/W2964983698","https://openalex.org/W2998431760","https://openalex.org/W3028201915","https://openalex.org/W3040043284","https://openalex.org/W3045200674","https://openalex.org/W3088231800","https://openalex.org/W3100278010","https://openalex.org/W3102099102","https://openalex.org/W3154113024","https://openalex.org/W4205171160","https://openalex.org/W4301113013"],"related_works":["https://openalex.org/W2368605798","https://openalex.org/W2518037665","https://openalex.org/W2348524959","https://openalex.org/W2477036161","https://openalex.org/W2368049389","https://openalex.org/W2170801710","https://openalex.org/W2384861574","https://openalex.org/W2952704802","https://openalex.org/W4294565801","https://openalex.org/W2142306706"],"abstract_inverted_index":{"Highly":[0],"skewed":[1],"long-tail":[2,54,90],"item":[3,117,126,147],"distribution":[4],"commonly":[5],"hurts":[6],"model":[7,164],"performance":[8],"on":[9,124,157],"tail":[10,170],"items":[11,29,171],"in":[12,44],"recommendation":[13,18,91],"systems,":[14],"especially":[15],"for":[16,89,93,128,169],"graph-based":[17,37],"models.":[19],"We":[20,80],"propose":[21,82,132],"a":[22,45,83,115,133],"novel":[23,84],"idea":[24],"to":[25,34,74,102,113,138],"learn":[26],"relations":[27,127],"among":[28],"as":[30],"an":[31,106],"auxiliary":[32,61,68,154],"graph":[33,62,69],"enhance":[35],"the":[36,53,60,66,75,103,144,152,174],"representation":[38],"learning":[39,104,136],"and":[40,64,120],"make":[41],"recommendations":[42],"collectively":[43],"coupled":[46],"framework.":[47],"This":[48],"raises":[49],"two":[50],"challenges,":[51],"1)":[52],"downstream":[55],"information":[56],"may":[57,70],"also":[58],"bias":[59],"learning,":[63],"2)":[65],"learned":[67,153],"cause":[71],"negative":[72,140],"transfer":[73,141],"original":[76],"user-item":[77],"bipartite":[78],"graph.":[79,155],"innovatively":[81],"Meta":[85],"Graph":[86],"Learning":[87],"framework":[88],"(MGL)":[92],"solving":[94],"both":[95],"challenges.":[96],"The":[97],"meta-learning":[98],"strategy":[99,137],"is":[100,110],"introduced":[101],"of":[105,151],"edge":[107],"generator,":[108],"which":[109],"first":[111],"tuned":[112],"reconstruct":[114],"debiased":[116],"co-occurrence":[118],"matrix,":[119],"then":[121],"virtually":[122],"evaluated":[123],"generating":[125],"recommendation.":[129],"Moreover,":[130],"we":[131],"popularity-aware":[134],"contrastive":[135],"prevent":[139],"by":[142],"aligning":[143],"confident":[145],"head":[146],"representations":[148],"with":[149],"those":[150],"Experiments":[156],"public":[158],"datasets":[159],"demonstrate":[160],"that":[161],"our":[162],"proposed":[163],"significantly":[165],"outperforms":[166],"strong":[167],"baselines":[168],"without":[172],"compromising":[173],"overall":[175],"performance.":[176]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":6}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}