{"id":"https://openalex.org/W7157602192","doi":"https://doi.org/10.48550/arxiv.2604.25787","title":"Harmonizing Generative Retrieval and Ranking in Chain-of-Recommendation","display_name":"Harmonizing Generative Retrieval and Ranking in Chain-of-Recommendation","publication_year":2026,"publication_date":"2026-04-28","ids":{"openalex":"https://openalex.org/W7157602192","doi":"https://doi.org/10.48550/arxiv.2604.25787"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.25787","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.25787","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.2604.25787","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134865324","display_name":"Yu Liu","orcid":"https://orcid.org/0009-0002-2710-7644"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134838286","display_name":"Jiangxia Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Jiangxia","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":0.9103999733924866,"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":0.9103999733924866,"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":0.008200000040233135,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.0066999997943639755,"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/ranking","display_name":"Ranking (information retrieval)","score":0.7217000126838684},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.6157000064849854},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5094000101089478},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.48669999837875366},{"id":"https://openalex.org/keywords/semantic-gap","display_name":"Semantic gap","score":0.41940000653266907},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.3968999981880188}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7559000253677368},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.7217000126838684},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6157000064849854},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5810999870300293},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5202999711036682},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5094000101089478},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.48669999837875366},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46860000491142273},{"id":"https://openalex.org/C86034646","wikidata":"https://www.wikidata.org/wiki/Q474311","display_name":"Semantic gap","level":4,"score":0.41940000653266907},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3968999981880188},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3562999963760376},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.32739999890327454},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.3142000138759613},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2702000141143799},{"id":"https://openalex.org/C2129575","wikidata":"https://www.wikidata.org/wiki/Q54837","display_name":"Semantic Web","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.25787","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.25787","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.2604.25787","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.25787","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Generative":[0],"recommender":[1],"systems":[2],"have":[3],"recently":[4],"emerged":[5],"as":[6,14,21],"a":[7,61,75,89],"promising":[8],"paradigm":[9],"by":[10],"formulating":[11],"next-item":[12],"prediction":[13,29],"an":[15],"auto-regressive":[16],"semantic":[17,104],"IDs":[18,41],"generation,":[19],"such":[20],"OneRec":[22],"series":[23],"works.":[24],"However,":[25],"with":[26],"the":[27,54,96,109,115,135],"next-item-agnostic":[28],"paradigm,":[30],"its":[31],"could":[32],"beam":[33],"out":[34],"some":[35],"next":[36],"potential":[37],"items":[38,47,101],"via":[39,102],"Semantic":[40],"but":[42],"hard":[43],"to":[44,60,113],"estimate":[45,114],"which":[46],"are":[48],"better":[49],"from":[50,56],"them,":[51],"e.g.,":[52],"select":[53],"top-10":[55],"beam-256":[57],"items,":[58],"leading":[59],"gap":[62,136],"between":[63,137],"generation":[64,85],"and":[65,79,86,140],"ranking":[66,80,87,111],"performance.":[67],"To":[68],"fulfill":[69],"this":[70],"gap,":[71],"we":[72],"propose":[73],"RecoChain,":[74],"unified":[76],"generative":[77,138,150],"retrieval":[78,139],"framework":[81],"that":[82,130],"integrates":[83],"candidate":[84,100,121],"within":[88],"single":[90],"Transformer":[91],"backbone.":[92],"Specifically,":[93],"in":[94],"inference,":[95],"model":[97],"first":[98],"generates":[99],"hierarchical":[103],"ID":[105],"prediction,":[106],"then":[107],"performs":[108],"SIM-based":[110],"process":[112],"click":[116],"possibility":[117],"of":[118],"corresponding":[119],"item":[120],"continuously.":[122],"Extensive":[123],"experiments":[124],"on":[125],"large-scale":[126],"real-world":[127],"datasets":[128],"demonstrate":[129],"our":[131],"approach":[132],"effectively":[133],"bridges":[134],"ranking,":[141],"achieving":[142],"improved":[143],"Top-K":[144],"recommendation":[145],"performance":[146],"while":[147],"maintaining":[148],"strong":[149],"capability.":[151]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-30T00:00:00"}