{"id":"https://openalex.org/W7224077747","doi":"https://doi.org/10.21275/sr261022111025","title":"Assisted Pricing and Foundational Pricing Capabilities: A Scalable, Explainable, and Fair AI-Driven Pricing Architecture","display_name":"Assisted Pricing and Foundational Pricing Capabilities: A Scalable, Explainable, and Fair AI-Driven Pricing Architecture","publication_year":2026,"publication_date":"2026-10-28","ids":{"openalex":"https://openalex.org/W7224077747","doi":"https://doi.org/10.21275/sr261022111025"},"language":"en","primary_location":{"id":"doi:10.21275/sr261022111025","doi":"https://doi.org/10.21275/sr261022111025","is_oa":true,"landing_page_url":"https://doi.org/10.21275/sr261022111025","pdf_url":"https://www.ijsr.net/archive/v15i10/SR261022111025.pdf","source":{"id":"https://openalex.org/S4210240710","display_name":"International Journal of Science and Research (IJSR)","issn_l":"2319-7064","issn":["2319-7064"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Science and Research (IJSR)","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.ijsr.net/archive/v15i10/SR261022111025.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031889344","display_name":"Tanushree Gupta","orcid":"https://orcid.org/0000-0002-3738-1690","observed_orcids":["https://orcid.org/0000-0002-3738-1690"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tanushree Gupta","raw_affiliation_strings":["San Diego , California , USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"San Diego , California , USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5031889344"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.8690649,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1704","last_page":"1712"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.3521000146865845,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.3521000146865845,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10328","display_name":"Supply Chain and Inventory Management","score":0.2694999873638153,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.06560000032186508,"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/dynamic-pricing","display_name":"dynamic pricing","score":0.847000002861023},{"id":"https://openalex.org/keywords/price-fairness","display_name":"price fairness","score":0.6499999761581421},{"id":"https://openalex.org/keywords/shap-attribution","display_name":"SHAP feature attribution","score":0.8410000205039978},{"id":"https://openalex.org/keywords/gradient-boosted-decision-trees","display_name":"gradient boosting decision tree","score":0.9010000228881836},{"id":"https://openalex.org/keywords/q-learning","display_name":"Q-learning","score":0.7120000123977661},{"id":"https://openalex.org/keywords/revenue-leakage","display_name":"revenue leakage","score":0.593999981880188},{"id":"https://openalex.org/keywords/service-sector","display_name":"service sector","score":0.35499998927116394}],"concepts":[{"id":"https://openalex.org/C195487862","wikidata":"https://www.wikidata.org/wiki/Q850210","display_name":"Revenue","level":2,"score":0.5666999816894531},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5315999984741211},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5062999725341797},{"id":"https://openalex.org/C164624739","wikidata":"https://www.wikidata.org/wiki/Q754331","display_name":"Activity-based costing","level":2,"score":0.4507000148296356},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.4334999918937683},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.3416000008583069},{"id":"https://openalex.org/C66887028","wikidata":"https://www.wikidata.org/wiki/Q382444","display_name":"Marginal cost","level":2,"score":0.32409998774528503},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.31949999928474426},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.31380000710487366},{"id":"https://openalex.org/C45874996","wikidata":"https://www.wikidata.org/wiki/Q37045","display_name":"Markup language","level":3,"score":0.3059999942779541},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.3021000027656555},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2921000123023987},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C2777151079","wikidata":"https://www.wikidata.org/wiki/Q141160","display_name":"Parity (physics)","level":2,"score":0.2761000096797943},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C2780958618","wikidata":"https://www.wikidata.org/wiki/Q945621","display_name":"Average cost","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C2779182362","wikidata":"https://www.wikidata.org/wiki/Q17126187","display_name":"Session (web analytics)","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C98025372","wikidata":"https://www.wikidata.org/wiki/Q477538","display_name":"Systems architecture","level":3,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21275/sr261022111025","doi":"https://doi.org/10.21275/sr261022111025","is_oa":true,"landing_page_url":"https://doi.org/10.21275/sr261022111025","pdf_url":"https://www.ijsr.net/archive/v15i10/SR261022111025.pdf","source":{"id":"https://openalex.org/S4210240710","display_name":"International Journal of Science and Research (IJSR)","issn_l":"2319-7064","issn":["2319-7064"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Science and Research (IJSR)","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.21275/sr261022111025","doi":"https://doi.org/10.21275/sr261022111025","is_oa":true,"landing_page_url":"https://doi.org/10.21275/sr261022111025","pdf_url":"https://www.ijsr.net/archive/v15i10/SR261022111025.pdf","source":{"id":"https://openalex.org/S4210240710","display_name":"International Journal of Science and Research (IJSR)","issn_l":"2319-7064","issn":["2319-7064"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Science and Research (IJSR)","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7224077747.pdf","grobid_xml":"https://content.openalex.org/works/W7224077747.grobid-xml"},"referenced_works_count":7,"referenced_works":["https://openalex.org/W32403112","https://openalex.org/W1678356000","https://openalex.org/W2145339207","https://openalex.org/W2340135383","https://openalex.org/W2594475271","https://openalex.org/W3175901868","https://openalex.org/W4205228770"],"related_works":["https://openalex.org/W4415398196","https://openalex.org/W3040283401","https://openalex.org/W4413393053","https://openalex.org/W4410614719","https://openalex.org/W6959799918","https://openalex.org/W7168914772","https://openalex.org/W7128442447","https://openalex.org/W2010776956","https://openalex.org/W7212157994","https://openalex.org/W7157295995"],"abstract_inverted_index":{"Static":[0],"price":[1,104,129],"schedules":[2],"ignore":[3],"how":[4],"much":[5],"service":[6,83],"requests":[7,196],"vary.":[8],"The":[9,39,185,222],"result":[10],"is":[11,139],"estimation":[12],"error,":[13],"revenue":[14,154],"leakage,":[15],"and":[16,56,62,116,153],"prices":[17,64],"that":[18],"customers":[19],"cannot":[20],"interpret.":[21],"This":[22],"study":[23],"tests":[24],"whether":[25],"an":[26,167,176],"assisted":[27],"pricing":[28,220],"architecture":[29],"can":[30],"improve":[31],"accuracy":[32,133],"without":[33],"giving":[34],"up":[35],"fairness":[36],"or":[37],"transparency.":[38],"system":[40,138,187],"pairs":[41],"gradient-boosted":[42],"trees":[43],"with":[44,65],"a":[45,50,68,75,91,144,202],"regularized":[46],"linear":[47],"model,":[48],"applies":[49],"rule":[51],"engine":[52],"for":[53],"margin,":[54],"regional,":[55],"risk":[57],"limits,":[58],"attaches":[59],"SHAP":[60],"attributions,":[61],"adjusts":[63],"Q-learning":[66],"under":[67],"fairness-penalized":[69],"reward.":[70],"It":[71],"was":[72],"run":[73],"on":[74,197],"simulated":[76],"dataset":[77],"of":[78,97,101,120,143,183,193,205],"500,000":[79],"transactions":[80],"from":[81,106,124,162],"four":[82],"industries,":[84],"repeated":[85],"over":[86],"five":[87],"random":[88],"seeds.":[89],"Against":[90],"static":[92],"rate":[93],"card,":[94],"the":[95,102,117,137,173],"share":[96],"estimates":[98],"within":[99,189],"?5%":[100],"realized":[103],"rose":[105,149],"11.2%":[107],"to":[108,126],"34.0%,":[109],"mean":[110],"absolute":[111],"error":[112,122],"fell":[113],"by":[114,150,157],"67%,":[115],"standard":[118],"deviation":[119],"relative":[121],"dropped":[123],"39%":[125],"12%.":[127],"Simulated":[128,147],"noise":[130],"caps":[131],"attainable":[132],"at":[134],"63.7%,":[135],"so":[136,226],"still":[140],"well":[141],"short":[142],"perfect":[145],"estimator.":[146],"CSI":[148],"30":[151],"points":[152],"per":[155],"session":[156],"35%,":[158],"but":[159],"both":[160],"follow":[161],"assumed":[163],"customer-response":[164],"models.":[165],"With":[166],"8%":[168],"age-based":[169],"markup":[170],"injected":[171],"into":[172],"training":[174],"data,":[175],"ungoverned":[177],"boosted-tree":[178],"model":[179],"gave":[180],"parity":[181],"ratios":[182],"0.98?1.06.":[184],"governed":[186],"stayed":[188],"0.99?1.01.":[190],"A":[191],"burst":[192],"10,000":[194],"concurrent":[195],"one":[198],"4-vCPU":[199],"node":[200],"had":[201],"median":[203],"latency":[204],"1.55":[206],"s":[207],"(p95":[208],"2.89":[209],"s,":[210],"p99":[211],"3.02":[212],"s).":[213],"Reinforcement":[214],"learning":[215],"did":[216],"not":[217],"reduce":[218],"six-month":[219],"drift.":[221],"data":[223],"are":[224,229],"simulated,":[225],"all":[227],"results":[228],"preliminary.":[230]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-10-29T00:00:00"}
