{"id":"https://openalex.org/W7152053550","doi":"https://doi.org/10.48550/arxiv.2604.05866","title":"Beyond Paper-to-Paper: Structured Profiling and Rubric Scoring for Paper-Reviewer Matching","display_name":"Beyond Paper-to-Paper: Structured Profiling and Rubric Scoring for Paper-Reviewer Matching","publication_year":2026,"publication_date":"2026-04-07","ids":{"openalex":"https://openalex.org/W7152053550","doi":"https://doi.org/10.48550/arxiv.2604.05866"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.05866","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05866","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.2604.05866","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133218599","display_name":"Yicheng Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Pan, Yicheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133158700","display_name":"Zhiyuan Ning","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ning, Zhiyuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101663060","display_name":"Ludi Wang","orcid":"https://orcid.org/0000-0002-9346-6250"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Ludi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133196297","display_name":"Yi Du","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Yi","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5133218599"],"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/T13274","display_name":"Expert finding and Q&A systems","score":0.9922999739646912,"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/T13274","display_name":"Expert finding and Q&A systems","score":0.9922999739646912,"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":0.0044999998062849045,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.00019999999494757503,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/rubric","display_name":"Rubric","score":0.7364000082015991},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5834000110626221},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.506600022315979},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4198000133037567},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4009999930858612},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.30660000443458557}],"concepts":[{"id":"https://openalex.org/C111640148","wikidata":"https://www.wikidata.org/wiki/Q847349","display_name":"Rubric","level":2,"score":0.7364000082015991},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6377000212669373},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5834000110626221},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.506600022315979},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45660001039505005},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.44190001487731934},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4198000133037567},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4009999930858612},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.32589998841285706},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C163763905","wikidata":"https://www.wikidata.org/wiki/Q17075943","display_name":"Precision medicine","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C68859911","wikidata":"https://www.wikidata.org/wiki/Q1503724","display_name":"Pattern matching","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2628999948501587},{"id":"https://openalex.org/C187191949","wikidata":"https://www.wikidata.org/wiki/Q1138496","display_name":"Profiling (computer programming)","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.05866","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05866","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.2604.05866","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05866","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"As":[0],"conference":[1],"submission":[2],"volumes":[3],"continue":[4],"to":[5,42,65,73,98,114,126,182],"grow,":[6],"accurately":[7],"recommending":[8],"suitable":[9],"reviewers":[10],"has":[11],"become":[12],"a":[13,19,25,56,95,116,138],"challenge.":[14],"Most":[15],"existing":[16],"methods":[17],"follow":[18],"``Paper-to-Paper''":[20],"matching":[21,34,64],"paradigm,":[22],"implicitly":[23],"representing":[24],"reviewer":[26,33,183],"by":[27],"their":[28],"publication":[29],"history.":[30],"However,":[31],"effective":[32],"requires":[35],"capturing":[36],"multi-dimensional":[37,134],"expertise,":[38],"and":[39,80,87,101,111,120,137,147,175],"textual":[40],"similarity":[41],"past":[43],"papers":[44],"alone":[45],"is":[46],"often":[47],"insufficient.":[48],"To":[49],"address":[50],"this":[51],"gap,":[52],"we":[53],"propose":[54],"P2R,":[55],"training-free":[57],"framework":[58],"that":[59,108,150],"shifts":[60],"from":[61],"implicit":[62],"paper-to-paper":[63],"explicit":[66],"profile-based":[67],"matching.":[68,184],"P2R":[69,93,151,166],"uses":[70],"general-purpose":[71],"LLMs":[72,181],"construct":[74],"structured":[75,172],"profiles":[76],"for":[77,179],"both":[78,133],"submissions":[79],"reviewers,":[81],"disentangling":[82],"them":[83],"into":[84],"Topics,":[85],"Methodologies,":[86],"Applications.":[88],"Building":[89],"on":[90,144],"these":[91],"profiles,":[92],"adopts":[94],"coarse-to-fine":[96],"pipeline":[97],"balance":[99],"efficiency":[100],"depth.":[102],"It":[103],"first":[104],"performs":[105],"hybrid":[106],"retrieval":[107],"combines":[109],"semantic":[110],"aspect-level":[112],"signals":[113],"form":[115],"high-recall":[117],"candidate":[118],"pool,":[119],"then":[121],"applies":[122],"an":[123],"LLM-based":[124],"committee":[125],"evaluate":[127],"candidates":[128],"under":[129],"strict":[130],"rubrics,":[131],"integrating":[132],"expert":[135],"views":[136],"holistic":[139],"Area":[140],"Chair":[141],"perspective.":[142],"Experiments":[143],"NeurIPS,":[145],"SIGIR,":[146],"SciRepEval":[148],"show":[149],"consistently":[152],"outperforms":[153],"state-of-the-art":[154],"baselines.":[155],"Ablation":[156],"studies":[157],"further":[158],"verify":[159],"the":[160,168],"necessity":[161],"of":[162,170],"each":[163],"component.":[164],"Overall,":[165],"highlights":[167],"value":[169],"explicit,":[171],"expertise":[173],"modeling":[174],"offers":[176],"practical":[177],"guidance":[178],"applying":[180]},"counts_by_year":[],"updated_date":"2026-04-09T06:13:59.934233","created_date":"2026-04-09T00:00:00"}
