{"id":"https://openalex.org/W4415277307","doi":"https://doi.org/10.1145/3774904.3792217","title":"HyMiRec: A Hybrid Multi-interest Learning Framework for LLM-based Sequential Recommendation","display_name":"HyMiRec: A Hybrid Multi-interest Learning Framework for LLM-based Sequential Recommendation","publication_year":2026,"publication_date":"2026-04-12","ids":{"openalex":"https://openalex.org/W4415277307","doi":"https://doi.org/10.1145/3774904.3792217"},"language":null,"primary_location":{"id":"doi:10.1145/3774904.3792217","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792217","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3774904.3792217","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102951145","display_name":"Jingyi Zhou","orcid":"https://orcid.org/0000-0002-0119-3583"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyi Zhou","raw_affiliation_strings":["Xiaohongshu Inc., Beijing, China and Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0005-6967-0090","affiliations":[{"raw_affiliation_string":"Xiaohongshu Inc., Beijing, China and Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100420548","display_name":"Cheng Chen","orcid":"https://orcid.org/0000-0002-4203-2145"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng Chen","raw_affiliation_strings":["Xiaohongshu Inc., Beijing, China"],"raw_orcid":"https://orcid.org/0009-0007-3967-3041","affiliations":[{"raw_affiliation_string":"Xiaohongshu Inc., Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075556202","display_name":"Kai Zuo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kai Zuo","raw_affiliation_strings":["Xiaohongshu Inc., Beijing, China"],"raw_orcid":"https://orcid.org/0009-0001-3230-6261","affiliations":[{"raw_affiliation_string":"Xiaohongshu Inc., Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086097247","display_name":"Manjie Xu","orcid":"https://orcid.org/0000-0001-9220-3662"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Manjie Xu","raw_affiliation_strings":["Xiaohongshu Inc., Beijing, China and Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9220-3662","affiliations":[{"raw_affiliation_string":"Xiaohongshu Inc., Beijing, China and Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026235848","display_name":"Zhendong Fu","orcid":"https://orcid.org/0000-0002-8819-5054"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhendong Fu","raw_affiliation_strings":["Xiaohongshu Inc., Beijing, China"],"raw_orcid":"https://orcid.org/0009-0000-4028-0068","affiliations":[{"raw_affiliation_string":"Xiaohongshu Inc., Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100722130","display_name":"Yibo Chen","orcid":"https://orcid.org/0000-0002-7391-8790"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yibo Chen","raw_affiliation_strings":["Xiaohongshu Inc., Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-6632-6359","affiliations":[{"raw_affiliation_string":"Xiaohongshu Inc., Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059262797","display_name":"Xu Tang","orcid":"https://orcid.org/0000-0003-1375-0778"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu Tang","raw_affiliation_strings":["Xiaohongshu Inc., Beijing, China"],"raw_orcid":"https://orcid.org/0009-0005-9475-9011","affiliations":[{"raw_affiliation_string":"Xiaohongshu Inc., Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108742278","display_name":"Yao Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao Hu","raw_affiliation_strings":["Xiaohongshu Inc., Beijing, China"],"raw_orcid":"https://orcid.org/0009-0006-1274-7111","affiliations":[{"raw_affiliation_string":"Xiaohongshu Inc., Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"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":0.0083955,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5998","last_page":"6008"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9894000291824341,"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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9894000291824341,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9879000186920166,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9603000283241272,"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/recommender-system","display_name":"Recommender system","score":0.6017000079154968},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5551000237464905},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.44519999623298645},{"id":"https://openalex.org/keywords/reuse","display_name":"Reuse","score":0.42669999599456787},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.41929998993873596},{"id":"https://openalex.org/keywords/cosine-similarity","display_name":"Cosine similarity","score":0.39570000767707825},{"id":"https://openalex.org/keywords/codebook","display_name":"Codebook","score":0.38989999890327454},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.3458999991416931}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8521999716758728},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.6017000079154968},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5551000237464905},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5286999940872192},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4496000111103058},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.44519999623298645},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.42669999599456787},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.41929998993873596},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.39570000767707825},{"id":"https://openalex.org/C127759330","wikidata":"https://www.wikidata.org/wiki/Q637416","display_name":"Codebook","level":2,"score":0.38989999890327454},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3659000098705292},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.3458999991416931},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3425000011920929},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3375999927520752},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.3255999982357025},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.32499998807907104},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.2721000015735626},{"id":"https://openalex.org/C201025465","wikidata":"https://www.wikidata.org/wiki/Q11248500","display_name":"User experience design","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.2596000134944916},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3774904.3792217","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792217","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2510.13738","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.13738","pdf_url":"https://arxiv.org/pdf/2510.13738","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2510.13738","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.13738","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.1145/3774904.3792217","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792217","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2],"(LLMs)":[3],"have":[4],"recently":[5],"demonstrated":[6],"strong":[7],"potential":[8],"for":[9],"sequential":[10,93],"recommendation.":[11],"However,":[12],"current":[13,61],"LLM-based":[14,112],"approaches":[15],"face":[16],"critical":[17],"limitations":[18],"in":[19,51,210],"modeling":[20],"users'":[21],"long-term":[22],"and":[23,31,79,110,138,188],"diverse":[24,147],"interests.":[25],"First,":[26],"due":[27],"to":[28,43,101,114,163,173],"inference":[29],"latency":[30],"feature":[32],"fetching":[33,124],"bandwidth":[34],"constraints,":[35],"existing":[36,197],"methods":[37,62],"typically":[38],"truncate":[39],"user":[40,77,108,141,178],"behavior":[41],"sequences":[42,109],"include":[44],"only":[45],"the":[46,52,73,121,146,171],"most":[47,60],"recent":[48],"interactions,":[49],"resulting":[50],"loss":[53],"of":[54,76,123,140,149,177],"valuable":[55],"long-range":[56],"preference":[57],"signals.":[58],"Second,":[59],"rely":[63],"on":[64,132,184],"next-item":[65],"prediction":[66],"with":[67],"a":[68,90,98,128,153,189],"single":[69],"predicted":[70],"embedding,":[71],"overlooking":[72],"multifaceted":[74],"nature":[75],"interests":[78],"limiting":[80],"recommendation":[81,94,212],"diversity.":[82],"To":[83,119,144],"address":[84],"these":[85],"challenges,":[86],"we":[87,126,151],"propose":[88],"HyMiRec,":[89],"hybrid":[91],"multi-interest":[92,155],"framework,":[95],"which":[96,158],"leverages":[97,159],"lightweight":[99],"recommender":[100,113],"extracts":[102],"coarse":[103],"interest":[104,117,161,167],"embeddings":[105],"from":[106],"long":[107],"an":[111],"captures":[115],"refined":[116],"embeddings.":[118,143],"alleviate":[120],"overhead":[122],"features,":[125],"introduce":[127],"residual":[129],"codebook":[130],"based":[131],"cosine":[133],"similarity,":[134],"enabling":[135],"efficient":[136],"compression":[137],"reuse":[139],"history":[142],"model":[145,172],"preferences":[148],"users,":[150],"design":[152],"disentangled":[154],"learning":[156],"module,":[157],"multiple":[160,166],"queries":[162],"learn":[164],"disentangles":[165],"signals":[168],"adaptively,":[169],"allowing":[170],"capture":[174],"different":[175],"facets":[176],"intent.":[179],"Extensive":[180],"experiments":[181],"are":[182],"conducted":[183],"both":[185],"benchmark":[186],"datasets":[187],"collected":[190],"industrial":[191],"dataset,":[192],"demonstrating":[193],"our":[194],"effectiveness":[195],"over":[196],"state-of-the-art":[198],"methods.":[199],"Furthermore,":[200],"online":[201],"A/B":[202],"testing":[203],"shows":[204],"that":[205],"HyMiRec":[206],"brings":[207],"consistent":[208],"improvements":[209],"real-world":[211],"systems.":[213]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-17T00:00:00"}