{"id":"https://openalex.org/W7166847333","doi":"https://doi.org/10.18653/v1/2026.findings-acl.726","title":"HSUGA: LLM-Enhanced Recommendation with Hierarchical Semantic Understanding and Group-Aware Alignment","display_name":"HSUGA: LLM-Enhanced Recommendation with Hierarchical Semantic Understanding and Group-Aware Alignment","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7166847333","doi":"https://doi.org/10.18653/v1/2026.findings-acl.726"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.findings-acl.726","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.726","pdf_url":"https://aclanthology.org/2026.findings-acl.726.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.findings-acl.726.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121238029","display_name":"Guorui Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guorui Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139838777","display_name":"Dugang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dugang Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139778197","display_name":"Lei Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lei Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139740036","display_name":"Xing Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xing Tang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139781776","display_name":"Zhong Ming","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhong Ming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.85263807,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"14774","last_page":"14788"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.3199000060558319,"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":0.3199000060558319,"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":0.3151000142097473,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.08460000157356262,"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/semantics","display_name":"Semantics (computer science)","score":0.3497999906539917},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.335099995136261},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.3156000077724457},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.26510000228881836},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.258899986743927}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6973999738693237},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4422000050544739},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3416999876499176},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3310999870300293},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3156000077724457},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.258899986743927},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2578999996185303}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.findings-acl.726","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.726","pdf_url":"https://aclanthology.org/2026.findings-acl.726.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.findings-acl.726","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.726","pdf_url":"https://aclanthology.org/2026.findings-acl.726.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166847333.pdf","grobid_xml":"https://content.openalex.org/works/W7166847333.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"model":[2],"(LLM)-enhanced":[3],"sequential":[4],"recommendation":[5],"typically":[6],"aims":[7],"to":[8,54,85],"improve":[9],"two":[10,25,105],"core":[11,106],"components:":[12,107],"user":[13,58,81,135,146],"semantic":[14,69,136,142],"embedding":[15,70],"extraction":[16,30],"and":[17,112,122,155,173],"utilization.Despite":[18],"promising":[19],"results,":[20],"existing":[21],"methods":[22,33,65],"still":[23],"have":[24],"limitations:":[26],"1)":[27],"In":[28,60],"the":[29,61,67,77,104,132,139,171],"stage,":[31,63],"most":[32,64],"directly":[34],"input":[35],"long":[36,46],"interaction":[37],"sequence":[38],"fragments":[39],"into":[40],"LLM":[41],"for":[42,73,101,152,158],"preference":[43,120,124],"summarization.However,":[44],"excessively":[45],"sequences":[47],"increase":[48],"inference":[49],"difficulty,":[50],"making":[51],"it":[52],"challenging":[53],"reliably":[55],"infer":[56],"accurate":[57],"embeddings.2)":[59],"utilization":[62,71,143],"employ":[66],"same":[68],"strategy":[72],"all":[74],"users,":[75],"neglecting":[76],"differences":[78],"caused":[79],"by":[80],"activity":[82,147],"levels,":[83,148],"leading":[84],"suboptimal":[86],"performance.To":[87],"address":[88],"these":[89],"issues,":[90],"we":[91],"propose":[92],"HSUGA,":[93],"which":[94],"introduces":[95],"a":[96,117],"simple":[97],"yet":[98],"effective":[99],"plugin":[100],"each":[102],"of":[103,134,141,175],"Hierarchical":[108],"Semantic":[109],"Understanding":[110],"(HSU)":[111],"Group-Aware":[113],"Alignment":[114],"(GAA).HSU":[115],"performs":[116],"staged":[118],"twophase":[119],"mining":[121],"models":[123],"evolution":[125],"through":[126],"constrained":[127],"editing":[128],"operations,":[129],"thereby":[130],"improving":[131],"reliability":[133],"extraction.GAA":[137],"adjusts":[138],"intensity":[140],"based":[144],"on":[145,166],"providing":[149],"weaker":[150],"alignment":[151],"active":[153],"users":[154,159],"stronger":[156],"guidance":[157],"with":[160],"sparse":[161],"historical":[162],"data.Finally,":[163],"extensive":[164],"experiments":[165],"three":[167],"benchmark":[168],"datasets":[169],"demonstrate":[170],"effectiveness":[172],"compatibility":[174],"HSUGA.":[176]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-07-02T00:00:00"}