{"id":"https://openalex.org/W7152115503","doi":"https://doi.org/10.48550/arxiv.2604.05379","title":"Retrieve-then-Adapt: Retrieval-Augmented Test-Time Adaptation for Sequential Recommendation","display_name":"Retrieve-then-Adapt: Retrieval-Augmented Test-Time Adaptation for Sequential Recommendation","publication_year":2026,"publication_date":"2026-04-07","ids":{"openalex":"https://openalex.org/W7152115503","doi":"https://doi.org/10.48550/arxiv.2604.05379"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.05379","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05379","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":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.05379","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133198778","display_name":"Xing Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tang, Xing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133172635","display_name":"Jingyang Bin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bin, Jingyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133162218","display_name":"Ziqiang Cui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cui, Ziqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133184925","display_name":"Xiaokun Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xiaokun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133205685","display_name":"Fuyuan Lyu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lyu, Fuyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032508900","display_name":"Jingyan Jiang","orcid":"https://orcid.org/0000-0003-4897-2645"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Jingyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003106644","display_name":"Dugang Liu","orcid":"https://orcid.org/0000-0003-3612-709X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Dugang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133225144","display_name":"Chen Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133189436","display_name":"Xiuqiang He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Xiuqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5133198778"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9239000082015991,"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":0.9239000082015991,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.00800000037997961,"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":0.005799999926239252,"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/task","display_name":"Task (project management)","score":0.5954999923706055},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.589900016784668},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5354999899864197},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5274999737739563},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.498199999332428},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.45969998836517334},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.41819998621940613},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.3785000145435333}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7985000014305115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6026999950408936},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5954999923706055},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.589900016784668},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.582099974155426},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5354999899864197},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5274999737739563},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.498199999332428},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.45969998836517334},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.41819998621940613},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.3785000145435333},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.3643999993801117},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.36149999499320984},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32089999318122864},{"id":"https://openalex.org/C165464430","wikidata":"https://www.wikidata.org/wiki/Q1570441","display_name":"Parameterized complexity","level":2,"score":0.30880001187324524},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.3043000102043152},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2987000048160553},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.29820001125335693},{"id":"https://openalex.org/C2779182362","wikidata":"https://www.wikidata.org/wiki/Q17126187","display_name":"Session (web analytics)","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2874999940395355},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.26350000500679016},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.26339998841285706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.05379","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05379","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:10.48550/arxiv.2604.05379","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05379","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":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"sequential":[1],"recommendation":[2],"(SR)":[3],"task":[4],"aims":[5],"to":[6,26,28,35,46,87,117],"predict":[7],"the":[8,85,118,171],"next":[9],"item":[10],"based":[11],"on":[12,19,67],"users'":[13],"historical":[14,20],"interaction":[15],"sequences.":[16],"Typically":[17],"trained":[18,129],"data,":[21],"SR":[22,115,130,173,197],"models":[23],"often":[24],"struggle":[25],"adapt":[27],"real-time":[29],"preference":[30,124],"shifts":[31],"during":[32],"inference":[33],"due":[34],"challenges":[36],"posed":[37],"by":[38],"distributional":[39],"divergence":[40],"and":[41,55,97,167],"parameterized":[42],"constraints.":[43],"Existing":[44],"approaches":[45],"address":[47],"this":[48,80,101,183],"issue":[49],"include":[50],"test-time":[51,53,89],"training,":[52],"augmentation,":[54],"retrieval-augmented":[56],"fine-tuning.":[57],"However,":[58],"these":[59,155],"methods":[60],"either":[61],"introduce":[62],"significant":[63],"computational":[64],"overhead,":[65],"rely":[66],"random":[68],"augmentation":[69,96,160],"strategies,":[70],"or":[71],"require":[72],"a":[73,107,113,128,139,143,178],"carefully":[74],"designed":[75],"two-stage":[76],"training":[77],"paradigm.":[78],"In":[79],"paper,":[81],"we":[82,103],"argue":[83],"that":[84,110,162,181,192],"key":[86],"effective":[88,95],"adaptation":[90],"lies":[91],"in":[92],"achieving":[93],"both":[94,164],"efficient":[98],"adaptation.":[99],"To":[100],"end,":[102],"propose":[104],"Retrieve-then-Adapt":[105],"(ReAd),":[106],"novel":[108],"framework":[109],"dynamically":[111],"adapts":[112],"deployed":[114],"model":[116],"test":[119,140],"distribution":[120],"through":[121],"retrieved":[122],"user":[123,141],"signals.":[125],"Specifically,":[126],"given":[127],"model,":[131],"ReAd":[132,193],"first":[133],"retrieves":[134],"collaboratively":[135],"similar":[136],"items":[137,156],"for":[138],"from":[142],"constructed":[144],"collaborative":[145,165],"memory":[146],"database.":[147],"A":[148],"lightweight":[149],"retrieval":[150],"learning":[151],"module":[152],"then":[153],"integrates":[154],"into":[157],"an":[158],"informative":[159],"embedding":[161],"captures":[163],"signals":[166],"prediction-refinement":[168],"cues.":[169],"Finally,":[170],"initial":[172],"prediction":[174],"is":[175],"refined":[176],"via":[177],"fusion":[179],"mechanism":[180],"incorporates":[182],"embedding.":[184],"Extensive":[185],"experiments":[186],"across":[187],"five":[188],"benchmark":[189],"datasets":[190],"demonstrate":[191],"consistently":[194],"outperforms":[195],"existing":[196],"methods.":[198]},"counts_by_year":[],"updated_date":"2026-05-04T08:30:34.212998","created_date":"2026-04-09T00:00:00"}