{"id":"https://openalex.org/W7161055004","doi":"https://doi.org/10.48550/arxiv.2605.11447","title":"Conditional Memory Enhanced Item Representation for Generative Recommendation","display_name":"Conditional Memory Enhanced Item Representation for Generative Recommendation","publication_year":2026,"publication_date":"2026-05-12","ids":{"openalex":"https://openalex.org/W7161055004","doi":"https://doi.org/10.48550/arxiv.2605.11447"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.11447","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11447","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.11447","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136031340","display_name":"Ziwei Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Ziwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136062899","display_name":"Yejing Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yejing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136052276","display_name":"Shengyu Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Shengyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136017828","display_name":"Xinhang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xinhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136067763","display_name":"Xiangyu Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Xiangyu","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":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":0.8743000030517578,"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":0.8743000030517578,"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":0.022700000554323196,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.014499999582767487,"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/scalability","display_name":"Scalability","score":0.5422999858856201},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5260999798774719},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5012000203132629},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.5011000037193298},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4253000020980835},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.39649999141693115},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.38760000467300415},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.384799987077713},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.3765999972820282}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7712000012397766},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5422999858856201},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5260999798774719},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5012000203132629},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.5011000037193298},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48730000853538513},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4253000020980835},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.39649999141693115},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.38760000467300415},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.384799987077713},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3765999972820282},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.36820000410079956},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.36640000343322754},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.3610000014305115},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34769999980926514},{"id":"https://openalex.org/C203357204","wikidata":"https://www.wikidata.org/wiki/Q1089605","display_name":"Chunking (psychology)","level":2,"score":0.3409999907016754},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3409999907016754},{"id":"https://openalex.org/C197129107","wikidata":"https://www.wikidata.org/wiki/Q1921621","display_name":"Merge (version control)","level":2,"score":0.32910001277923584},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3176000118255615},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C154504017","wikidata":"https://www.wikidata.org/wiki/Q853614","display_name":"Identifier","level":2,"score":0.29660001397132874},{"id":"https://openalex.org/C2775945657","wikidata":"https://www.wikidata.org/wiki/Q381442","display_name":"Structuring","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27549999952316284},{"id":"https://openalex.org/C12186640","wikidata":"https://www.wikidata.org/wiki/Q6815743","display_name":"Memory model","level":3,"score":0.2718999981880188},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C60048249","wikidata":"https://www.wikidata.org/wiki/Q37437","display_name":"Syntax","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.11447","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11447","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.11447","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11447","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":"Preprint"},"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":{"Generative":[0],"recommendation":[1],"(GR)":[2],"has":[3],"emerged":[4],"as":[5],"a":[6,24,31,62,148,196],"promising":[7],"paradigm":[8],"that":[9,155],"predicts":[10],"target":[11,44],"items":[12],"by":[13,92],"autoregressively":[14],"generating":[15],"their":[16],"semantic":[17],"identifiers":[18],"(SID).":[19],"Most":[20],"GR":[21],"methods":[22,104],"follow":[23],"quantization-representation-generation":[25],"pipeline,":[26],"first":[27],"assigning":[28],"each":[29,179],"item":[30,107],"SID,":[32,183],"then":[33],"constructing":[34],"input":[35],"representations":[36,68],"from":[37,220],"SID-token":[38,59,157],"embeddings,":[39],"and":[40,94,138,162,191,195,211,215],"finally":[41],"predicting":[42],"the":[43,88,113,134,164,176,182,201,209],"SID":[45,99,168,204],"through":[46,72],"autoregressive":[47],"generation.":[48,119],"Existing":[49],"item-level":[50,67,77],"representation":[51,123],"constructions":[52],"mainly":[53],"take":[54],"two":[55,81,130],"forms:":[56],"directly":[57],"merging":[58,85],"embeddings":[60,158],"into":[61,159],"compact":[63],"vector,":[64],"or":[65],"enriching":[66],"with":[69],"external":[70],"inputs":[71,161],"additional":[73],"networks.":[74],"However,":[75],"these":[76],"constructors":[78],"still":[79],"expose":[80],"practical":[82],"challenges:":[83],"direct":[84],"may":[86],"amplify":[87],"information":[89],"loss":[90],"caused":[91],"quantization":[93],"ID":[95],"collision":[96],"while":[97],"obscuring":[98],"code":[100,180,189],"relations,":[101],"whereas":[102],"external-input-based":[103],"can":[105],"strengthen":[106],"semantics":[108],"but":[109],"cannot":[110],"reliably":[111],"preserve":[112],"SID-structured":[114],"evidence":[115],"required":[116],"for":[117],"token-level":[118],"These":[120],"limitations":[121],"make":[122],"construction":[124],"an":[125],"underexplored":[126],"bottleneck,":[127],"leading":[128],"to":[129],"severe":[131],"problems,":[132],"\\ie{}":[133],"Identity-Structure":[135],"Preservation":[136],"Conflict":[137],"Input-Output":[139],"Granularity":[140],"Mismatch.":[141],"To":[142],"this":[143],"end,":[144],"we":[145],"propose":[146],"ComeIR,":[147,214],"Conditional":[149],"Memory":[150],"enhanced":[151],"Item":[152],"Representation":[153],"framework":[154],"reconstructs":[156],"item-aware":[160],"restores":[163],"token":[165,172],"granularity":[166],"during":[167,203],"decoding.":[169,205],"Specifically,":[170],"MM-guided":[171],"scoring":[173],"adaptively":[174],"estimates":[175],"contribution":[177],"of":[178,213],"within":[181],"dual-level":[184],"Engram":[185],"memory":[186],"captures":[187],"intra-item":[188],"composition":[190],"inter-item":[192],"transition":[193],"patterns,":[194],"memory-restoring":[197],"prediction":[198],"head":[199],"reuses":[200],"memories":[202],"Extensive":[206],"experiments":[207],"demonstrate":[208],"effectiveness":[210],"flexibility":[212],"further":[216],"reveal":[217],"scalable":[218],"gains":[219],"enlarging":[221],"conditional":[222],"memory.":[223]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-14T00:00:00"}
