{"id":"https://openalex.org/W4400525246","doi":"https://doi.org/10.1145/3626772.3657699","title":"DHMAE: A Disentangled Hypergraph Masked Autoencoder for Group Recommendation","display_name":"DHMAE: A Disentangled Hypergraph Masked Autoencoder for Group Recommendation","publication_year":2024,"publication_date":"2024-07-10","ids":{"openalex":"https://openalex.org/W4400525246","doi":"https://doi.org/10.1145/3626772.3657699"},"language":"en","primary_location":{"id":"doi:10.1145/3626772.3657699","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3626772.3657699","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval","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/A5101194617","display_name":"Yingqi Zhao","orcid":"https://orcid.org/0009-0006-1972-2662"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingqi Zhao","raw_affiliation_strings":["College of CS, TJ Key Lab of NDST, Nankai University, Tianjin, China"],"raw_orcid":"https://orcid.org/0009-0006-1972-2662","affiliations":[{"raw_affiliation_string":"College of CS, TJ Key Lab of NDST, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100684319","display_name":"Haiwei Zhang","orcid":"https://orcid.org/0000-0002-5852-0426"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haiwei Zhang","raw_affiliation_strings":["College of CS, TJ Key Lab of NDST, Nankai University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-5852-0426","affiliations":[{"raw_affiliation_string":"College of CS, TJ Key Lab of NDST, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022729856","display_name":"Qijie Bai","orcid":"https://orcid.org/0000-0002-5632-9521"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qijie Bai","raw_affiliation_strings":["College of CS, TJ Key Lab of NDST, Nankai University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-5632-9521","affiliations":[{"raw_affiliation_string":"College of CS, TJ Key Lab of NDST, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017500556","display_name":"Changli Nie","orcid":"https://orcid.org/0000-0003-2450-6084"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changli Nie","raw_affiliation_strings":["College of CS, TJ Key Lab of NDST, Nankai University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0003-2450-6084","affiliations":[{"raw_affiliation_string":"College of CS, TJ Key Lab of NDST, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062064974","display_name":"Xiaojie Yuan","orcid":"https://orcid.org/0000-0002-5876-6856"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaojie Yuan","raw_affiliation_strings":["College of CS, TJ Key Lab of NDST, Nankai University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-5876-6856","affiliations":[{"raw_affiliation_string":"College of CS, TJ Key Lab of NDST, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.8953,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.9682049,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"914","last_page":"923"},"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/T10028","display_name":"Topic Modeling","score":0.9847000241279602,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9714000225067139,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hypergraph","display_name":"Hypergraph","score":0.7065203785896301},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7055211663246155},{"id":"https://openalex.org/keywords/group","display_name":"Group (periodic table)","score":0.5582510232925415},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5562936663627625},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3931959271430969},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.34392616152763367},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3277105689048767},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24389824271202087},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.22716298699378967},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.10858237743377686},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07048577070236206}],"concepts":[{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.7065203785896301},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7055211663246155},{"id":"https://openalex.org/C2781311116","wikidata":"https://www.wikidata.org/wiki/Q83306","display_name":"Group (periodic table)","level":2,"score":0.5582510232925415},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5562936663627625},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3931959271430969},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.34392616152763367},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3277105689048767},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24389824271202087},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.22716298699378967},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.10858237743377686},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07048577070236206},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3626772.3657699","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3626772.3657699","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1677182931","https://openalex.org/W2003605635","https://openalex.org/W2011815965","https://openalex.org/W2064702560","https://openalex.org/W2172118809","https://openalex.org/W2498525840","https://openalex.org/W2605350416","https://openalex.org/W2753738274","https://openalex.org/W2798538558","https://openalex.org/W2907130618","https://openalex.org/W2951217911","https://openalex.org/W2955081857","https://openalex.org/W2964051675","https://openalex.org/W2969689322","https://openalex.org/W3030899497","https://openalex.org/W3033661031","https://openalex.org/W3034714642","https://openalex.org/W3035289325","https://openalex.org/W3081170586","https://openalex.org/W3085990079","https://openalex.org/W3099602291","https://openalex.org/W3198760092","https://openalex.org/W3211072770","https://openalex.org/W4200610603","https://openalex.org/W4207040932","https://openalex.org/W4226027484","https://openalex.org/W4284701460","https://openalex.org/W4290876361","https://openalex.org/W4312262772","https://openalex.org/W4312583258","https://openalex.org/W4313156423","https://openalex.org/W4319653986","https://openalex.org/W4321479940","https://openalex.org/W4367047461","https://openalex.org/W4382239855","https://openalex.org/W4384644538","https://openalex.org/W4385270524"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W4376608589","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W1537073411","https://openalex.org/W3138003926","https://openalex.org/W1630514295","https://openalex.org/W4297051394"],"abstract_inverted_index":{"Group":[0],"recommendation":[1,104,169],"aims":[2],"to":[3,6,40,171],"suggest":[4],"items":[5,178],"a":[7,93,107,113,144,162],"group":[8,59,103,168],"of":[9,79,89,129,156,175],"users":[10],"that":[11,147,189],"are":[12],"suitable":[13],"for":[14,102,153,167],"the":[15,77,127,139,154,183,199],"group.":[16],"Although":[17],"some":[18,130],"existing":[19,34,71],"powerful":[20],"deep":[21],"learning":[22,38],"models":[23,35,75,195],"have":[24],"achieved":[25],"improved":[26],"performance,":[27],"various":[28],"aspects":[29],"remain":[30],"unexplored:":[31],"(1)":[32],"Most":[33,70],"using":[36],"contrastive":[37,49],"tend":[39],"rely":[41],"on":[42,149],"high-quality":[43],"data":[44,67,123],"augmentation":[45,124],"which":[46],"requires":[47],"precise":[48],"view":[50],"generation;":[51],"(2)":[52],"There":[53],"is":[54,64],"multifaceted":[55],"natural":[56],"noise":[57,63,140,200],"in":[58],"recommendation,":[60],"and":[61,81,132,134,177,179,196],"additional":[62],"introduced":[65],"during":[66,182],"augmentation;":[68],"(3)":[69],"hypergraph":[72,109,164],"neural":[73,110,165],"network-based":[74],"over-entangle":[76],"information":[78],"members":[80,176],"items,":[82],"ignoring":[83],"their":[84],"unique":[85],"characteristics.":[86],"In":[87],"light":[88],"this,":[90],"we":[91,142,160],"propose":[92,161],"highly":[94],"effective":[95],"<u>D</u>isentangled":[96],"<u>H</u>ypergraph":[97],"<u>M</u>asked":[98],"<u>A</u>uto":[99],"<u>E</u>ncoder-enhanced":[100],"method":[101,191],"(DHMAE),":[105],"combining":[106],"disentangled":[108,163],"network":[111,166],"with":[112],"graph":[114],"masked":[115],"autoencoder.":[116],"This":[117],"approach":[118],"creates":[119],"self-supervised":[120],"signals":[121],"without":[122],"by":[125],"masking":[126,145,157],"features":[128],"nodes":[131],"hyperedges":[133],"then":[135],"reconstructing":[136],"them.":[137],"For":[138],"problem,":[141],"design":[143],"strategy":[146],"relies":[148],"pre-computed":[150],"degree-sensitive":[151],"probabilities":[152],"process":[155],"features.":[158],"Furthermore,":[159],"scenarios":[170],"extract":[172],"common":[173],"messages":[174],"disentangle":[180],"them":[181],"convolution":[184],"process.":[185],"Extensive":[186],"experiments":[187],"demonstrate":[188],"our":[190],"significantly":[192],"outperforms":[193],"state-of-the-art":[194],"effectively":[197],"addresses":[198],"issue.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":8}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}