{"id":"https://openalex.org/W7167890455","doi":"https://doi.org/10.48550/arxiv.2607.08202","title":"PIT-SUN: A Deployable Empirical Marginal Transform Framework with Expectation-Consistent Recovery for Regression in Recommender Systems","display_name":"PIT-SUN: A Deployable Empirical Marginal Transform Framework with Expectation-Consistent Recovery for Regression in Recommender Systems","publication_year":2026,"publication_date":"2026-07-09","ids":{"openalex":"https://openalex.org/W7167890455","doi":"https://doi.org/10.48550/arxiv.2607.08202"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.08202","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.08202","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2607.08202","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140421938","display_name":"Mingyu Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Mingyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140434248","display_name":"Zhaohan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhaohan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049744522","display_name":"Zhenxiong Miao","orcid":"https://orcid.org/0000-0002-5176-6191"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Miao, Zhenxiong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140416523","display_name":"Xu Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006466867","display_name":"Dewei Leng","orcid":"https://orcid.org/0009-0002-8898-7673"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Leng, Dewei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140414160","display_name":"Yanan Niu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niu, Yanan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140409379","display_name":"Kun Gai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gai, Kun","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/T12761","display_name":"Data Stream Mining Techniques","score":0.24300000071525574,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.24300000071525574,"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":0.19290000200271606,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.10760000348091125,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/estimator","display_name":"Estimator","score":0.5536999702453613},{"id":"https://openalex.org/keywords/bounded-function","display_name":"Bounded function","score":0.5289999842643738},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.4618000090122223},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.4431999921798706},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.42480000853538513},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.4090999960899353},{"id":"https://openalex.org/keywords/inverse","display_name":"Inverse","score":0.37610000371932983},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.36250001192092896},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.361299991607666}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5536999702453613},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.5289999842643738},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5278000235557556},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.4618000090122223},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.4431999921798706},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.42480000853538513},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.4090999960899353},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.38339999318122864},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.37610000371932983},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36559998989105225},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.36250001192092896},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.361299991607666},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.3610999882221222},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.34779998660087585},{"id":"https://openalex.org/C42747912","wikidata":"https://www.wikidata.org/wiki/Q1048447","display_name":"Multiplicative function","level":2,"score":0.3296000063419342},{"id":"https://openalex.org/C186215838","wikidata":"https://www.wikidata.org/wiki/Q772232","display_name":"Conditional expectation","level":2,"score":0.32850000262260437},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3199999928474426},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.31369999051094055},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C156273044","wikidata":"https://www.wikidata.org/wiki/Q4913766","display_name":"Bin","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3068999946117401},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.28630000352859497},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.2777000069618225},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.271699994802475},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.2531999945640564}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.08202","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.08202","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.08202","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.08202","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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":{"Estimating":[0],"original-space":[1,155],"conditional":[2],"expectations":[3],"is":[4,19,59,68,76],"central":[5],"to":[6,131,152],"value-driven":[7],"recommender":[8],"systems,":[9],"including":[10],"dwell":[11],"time,":[12],"GMV,":[13],"and":[14,31,37,103,144,172,182],"LTV":[15],"forecasting.":[16],"Standard":[17],"MSE":[18],"expectation-consistent":[20,70],"in":[21,178],"principle,":[22],"but":[23,93],"its":[24,137],"gradients":[25],"become":[26],"unstable":[27],"on":[28,164],"heavy-tailed,":[29],"zero-inflated,":[30],"multimodal":[32],"targets,":[33],"causing":[34],"mean":[35],"collapse":[36],"tail":[38,83],"shrinkage.":[39],"Target":[40],"transformation":[41],"alleviates":[42],"this":[43],"scale":[44],"conflict,":[45],"yet":[46],"any":[47],"useful":[48],"nonlinear":[49],"marginal":[50,122,129],"transform":[51,75],"loses":[52],"expectation":[53,91,156],"consistency":[54],"under":[55],"direct":[56,65],"inversion.":[57],"This":[58],"not":[60],"an":[61],"implementation":[62],"oversight:":[63],"a":[64,119,133,140],"inverse-transform":[66],"estimator":[67],"universally":[69],"only":[71],"when":[72],"the":[73,154],"inverse":[74,99],"affine,":[77],"which":[78,97],"cannot":[79],"simultaneously":[80],"provide":[81],"bounded":[82,134],"compression.":[84],"Existing":[85],"conditionally":[86],"linear":[87],"recovery":[88,101,117,123,142,151],"methods":[89],"restore":[90],"consistency,":[92],"still":[94],"leave":[95],"open":[96],"coordinate,":[98,136],"lookup,":[100,139],"base,":[102,143],"deployment":[104,174,187],"monitor":[105],"should":[106],"be":[107],"selected":[108],"for":[109],"sparse":[110],"complex":[111],"marginals.":[112],"We":[113],"propose":[114],"\\textbf{P}robability-\\textbf{I}ntegral-\\textbf{TranS}formed":[115],"\\textbf{Un}biased":[116],"(\\textbf{PIT-SUN}),":[118],"deployable":[120],"empirical":[121,128],"framework.":[124],"PIT-SUN":[125],"uses":[126],"one":[127],"table":[130],"define":[132],"normal-score":[135],"inverse-quantile":[138],"variance-controlled":[141],"drift":[145],"monitoring,":[146],"then":[147],"applies":[148],"multiplicative":[149],"SUN":[150],"estimate":[153],"instead":[157],"of":[158],"directly":[159],"inverting":[160],"transformed":[161],"predictions.":[162],"Experiments":[163],"synthetic":[165],"distributions,":[166],"public":[167],"benchmarks,":[168],"large-scale":[169],"industrial":[170],"datasets,":[171],"online":[173],"show":[175],"robust":[176],"improvements":[177],"point":[179],"accuracy,":[180],"calibration,":[181],"ranking":[183],"quality":[184],"with":[185],"lightweight":[186],"overhead.":[188]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-11T00:00:00"}
