{"id":"https://openalex.org/W4406461714","doi":"https://doi.org/10.1109/bigdata62323.2024.10825740","title":"ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing","display_name":"ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406461714","doi":"https://doi.org/10.1109/bigdata62323.2024.10825740"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825740","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825740","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061691774","display_name":"Kiran Madhusudhanan","orcid":"https://orcid.org/0000-0001-6356-8646"},"institutions":[{"id":"https://openalex.org/I155765044","display_name":"University of Hildesheim","ror":"https://ror.org/02f9det96","country_code":"DE","type":"education","lineage":["https://openalex.org/I155765044"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Kiran Madhusudhanan","raw_affiliation_strings":["University Of Hildesheim,Information Systems and Machine Learning Lab VWFS Data Analytics Research Center"],"affiliations":[{"raw_affiliation_string":"University Of Hildesheim,Information Systems and Machine Learning Lab VWFS Data Analytics Research Center","institution_ids":["https://openalex.org/I155765044"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070191844","display_name":"Gunnar Behrens","orcid":"https://orcid.org/0000-0002-5921-5327"},"institutions":[{"id":"https://openalex.org/I4210096591","display_name":"Volkswagen Financial Services (Germany)","ror":"https://ror.org/00bf6gk85","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210096591"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Gunnar Behrens","raw_affiliation_strings":["Volkswagen Financial Services AG,Data Analytics &#x0026; AI Engineering"],"affiliations":[{"raw_affiliation_string":"Volkswagen Financial Services AG,Data Analytics &#x0026; AI Engineering","institution_ids":["https://openalex.org/I4210096591"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053258043","display_name":"Maximilian Stubbemann","orcid":"https://orcid.org/0000-0003-1579-1151"},"institutions":[{"id":"https://openalex.org/I155765044","display_name":"University of Hildesheim","ror":"https://ror.org/02f9det96","country_code":"DE","type":"education","lineage":["https://openalex.org/I155765044"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Maximilian Stubbemann","raw_affiliation_strings":["University Of Hildesheim,Information Systems and Machine Learning Lab VWFS Data Analytics Research Center"],"affiliations":[{"raw_affiliation_string":"University Of Hildesheim,Information Systems and Machine Learning Lab VWFS Data Analytics Research Center","institution_ids":["https://openalex.org/I155765044"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039470755","display_name":"Lars Schmidt-Thieme","orcid":"https://orcid.org/0000-0001-5729-6023"},"institutions":[{"id":"https://openalex.org/I155765044","display_name":"University of Hildesheim","ror":"https://ror.org/02f9det96","country_code":"DE","type":"education","lineage":["https://openalex.org/I155765044"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Lars Schmidt-Thieme","raw_affiliation_strings":["University Of Hildesheim,Information Systems and Machine Learning Lab VWFS Data Analytics Research Center"],"affiliations":[{"raw_affiliation_string":"University Of Hildesheim,Information Systems and Machine Learning Lab VWFS Data Analytics Research Center","institution_ids":["https://openalex.org/I155765044"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5061691774"],"corresponding_institution_ids":["https://openalex.org/I155765044"],"apc_list":null,"apc_paid":null,"fwci":0.3229,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.50812792,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2179","last_page":"2187"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12617","display_name":"Energy, Environment, and Transportation Policies","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12617","display_name":"Energy, Environment, and Transportation Policies","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9854000210762024,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.7049688696861267},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6034821271896362},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4985373020172119},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4694734811782837},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33744317293167114},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.25066280364990234},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.20207685232162476},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13603001832962036}],"concepts":[{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7049688696861267},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6034821271896362},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4985373020172119},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4694734811782837},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33744317293167114},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.25066280364990234},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.20207685232162476},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13603001832962036}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825740","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825740","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W2095705004","https://openalex.org/W2101234009","https://openalex.org/W2207780529","https://openalex.org/W2295598076","https://openalex.org/W2907304681","https://openalex.org/W2949676527","https://openalex.org/W2963238274","https://openalex.org/W2964022491","https://openalex.org/W2980994438","https://openalex.org/W2989193306","https://openalex.org/W3004944559","https://openalex.org/W3042623101","https://openalex.org/W3111356309","https://openalex.org/W3126131373","https://openalex.org/W3132460254","https://openalex.org/W3138962399","https://openalex.org/W3150807214","https://openalex.org/W3170720134","https://openalex.org/W3171856801","https://openalex.org/W3202428668","https://openalex.org/W3216660278","https://openalex.org/W4243249526","https://openalex.org/W4296280707","https://openalex.org/W4385245566","https://openalex.org/W4391093634","https://openalex.org/W4395098214","https://openalex.org/W6617145748","https://openalex.org/W6674330103","https://openalex.org/W6675354045","https://openalex.org/W6730042731","https://openalex.org/W6745609711","https://openalex.org/W6750729320","https://openalex.org/W6768958581","https://openalex.org/W6770847983","https://openalex.org/W6773967291","https://openalex.org/W6774120203","https://openalex.org/W6780872688","https://openalex.org/W6786948829","https://openalex.org/W6790674540","https://openalex.org/W6796927292","https://openalex.org/W7006806681"],"related_works":["https://openalex.org/W31220157","https://openalex.org/W2312753042","https://openalex.org/W4289356671","https://openalex.org/W2389155397","https://openalex.org/W2165884543","https://openalex.org/W3186837933","https://openalex.org/W2368989808","https://openalex.org/W1969346022","https://openalex.org/W2034959125","https://openalex.org/W2355687852"],"abstract_inverted_index":{"Used":[0],"car":[1],"pricing":[2,33,49,136],"is":[3,69,178,185,201],"a":[4,98,104,108,159],"critical":[5],"aspect":[6],"of":[7,57,78,114,143,192],"the":[8,20,45,55,61,67,76,133,141,145,190],"automotive":[9],"industry,":[10],"influenced":[11],"by":[12,40],"many":[13],"economic":[14],"factors":[15],"and":[16,26,38,87],"market":[17],"dynamics.":[18],"With":[19],"recent":[21,73],"surge":[22],"in":[23,181,196],"online":[24],"marketplaces":[25],"increased":[27],"demand":[28],"for":[29,85,111,149,163,167],"used":[30,137,157],"cars,":[31],"accurate":[32,120,180],"would":[34],"benefit":[35],"both":[36],"buyers":[37],"sellers":[39],"ensuring":[41],"fair":[42],"transactions.":[43],"However,":[44],"transition":[46],"towards":[47],"automated":[48],"algorithms":[50,80,96],"using":[51],"machine":[52],"learning":[53],"necessitates":[54],"comprehension":[56],"model":[58,68,92,105,162],"uncertainties,":[59],"specifically":[60],"ability":[62],"to":[63,126],"flag":[64],"predictions":[65,122,195],"that":[66,106,123,132,176],"unsure":[70],"about.":[71],"Although":[72],"literature":[74],"proposes":[75],"use":[77],"boosting":[79,128],"or":[81],"nearest":[82],"neighbor-based":[83],"approaches":[84],"swift":[86],"precise":[88],"price":[89,116,165],"predictions,":[90,117],"encapsulating":[91],"uncertainties":[93],"with":[94,119],"such":[95],"presents":[97],"complex":[99],"challenge.":[100],"We":[101],"introduce":[102],"ProbSAINT,":[103],"offers":[107],"principled":[109],"approach":[110],"uncertainty":[112],"quantification":[113],"its":[115,193],"along":[118],"point":[121],"are":[124],"comparable":[125],"state-of-the-art":[127],"techniques.":[129],"Furthermore,":[130],"acknowledging":[131],"business":[134],"prefers":[135],"cars":[138],"based":[139],"on":[140],"number":[142],"days":[144],"vehicle":[146],"was":[147],"listed":[148],"sale,":[150],"we":[151],"show":[152],"how":[153],"ProbSAINT":[154,177],"can":[155],"be":[156],"as":[158],"dynamic":[160],"forecasting":[161],"predicting":[164],"probabilities":[166],"different":[168],"expected":[169],"offer":[170],"durations.":[171],"Our":[172],"experiments":[173],"further":[174],"indicate":[175],"especially":[179],"instances":[182],"where":[183,199],"it":[184],"highly":[186],"certain.":[187],"This":[188],"proves":[189],"applicability":[191],"probabilistic":[194],"real-world":[197],"scenarios":[198],"trustworthiness":[200],"crucial.":[202]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}
