{"id":"https://openalex.org/W3009893170","doi":"https://doi.org/10.1145/3374587.3374598","title":"A Dynamic Price Inference Approach HIVE BOX","display_name":"A Dynamic Price Inference Approach HIVE BOX","publication_year":2019,"publication_date":"2019-12-06","ids":{"openalex":"https://openalex.org/W3009893170","doi":"https://doi.org/10.1145/3374587.3374598","mag":"3009893170"},"language":"en","primary_location":{"id":"doi:10.1145/3374587.3374598","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3374587.3374598","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence","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/A5013675735","display_name":"Dongxiao Jiang","orcid":"https://orcid.org/0009-0005-1234-9271"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jiang Dongxiao","raw_affiliation_strings":["HIVE BOX Technology Co., Ltd, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"HIVE BOX Technology Co., Ltd, Shenzhen, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004290542","display_name":"Ming Huang","orcid":"https://orcid.org/0000-0002-9517-2125"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang Ming","raw_affiliation_strings":["HIVE BOX, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"HIVE BOX, Shenzhen, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003998216","display_name":"Jin Zhao","orcid":"https://orcid.org/0000-0002-9807-2648"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin Zhao","raw_affiliation_strings":["HIVE BOX, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"HIVE BOX, Shenzhen, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090383116","display_name":"Min Wuguo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Min Wuguo","raw_affiliation_strings":["HIVE BOX, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"HIVE BOX, Shenzhen, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5013675735"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.24115159,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"338","last_page":"342"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11182","display_name":"Auction Theory and Applications","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T11182","display_name":"Auction Theory and Applications","score":0.9994999766349792,"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/T10328","display_name":"Supply Chain and Inventory Management","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"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/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9947999715805054,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.70590740442276},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6429263353347778},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3747002184391022}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.70590740442276},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6429263353347778},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3747002184391022}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3374587.3374598","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3374587.3374598","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.5699999928474426,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W304140514","https://openalex.org/W1746819321","https://openalex.org/W1964284115","https://openalex.org/W2266130758","https://openalex.org/W2340135383","https://openalex.org/W2487598231","https://openalex.org/W2769108999","https://openalex.org/W2798728990","https://openalex.org/W2808956106","https://openalex.org/W2809160405","https://openalex.org/W3044006788","https://openalex.org/W4300424600"],"related_works":["https://openalex.org/W3107474891","https://openalex.org/W2367950322","https://openalex.org/W4362723189","https://openalex.org/W86463150","https://openalex.org/W4289528260","https://openalex.org/W2911297108","https://openalex.org/W2511279186","https://openalex.org/W4300631627","https://openalex.org/W2953238046","https://openalex.org/W4210999218"],"abstract_inverted_index":{"HIVE":[0,73],"BOX":[1,74],"operates":[2],"150,000+":[3,181],"parcel":[4],"lockers,":[5],"which":[6,98],"cover":[7],"100+":[8],"cities":[9],"and":[10,46,69,105,141,156,166,194,207],"deliver":[11],"more":[12,128],"than":[13,152],"9,000,000+":[14],"parcels":[15],"daily,":[16],"in":[17,28,51,57,75,142,210],"order":[18],"to":[19,71,92,123,131,169,196],"promote":[20],"efficiency":[21],"of":[22,26,38,53,109,202],"the":[23,49,86,125,147],"last":[24],"mile":[25],"logistics":[27],"China.":[29],"However":[30],"prices":[31],"that":[32,146,189],"couriers":[33],"pay":[34],"for":[35,78],"box":[36,95],"use":[37],"lockers":[39,182],"are":[40,48],"still":[41],"depend":[42],"on":[43,83,137],"artificial":[44],"rules,":[45],"always":[47],"same":[50],"terms":[52],"district":[54],"standard.":[55],"So":[56],"this":[58],"paper":[59],"we":[60,116,144],"propose":[61],"a":[62,89,119],"price":[63,127],"inference":[64,183],"approach":[65,87,149],"characterized":[66],"by":[67,97,160,174],"dynamic":[68,126],"personalization,":[70],"assist":[72],"decision":[76],"making":[77],"large":[79],"scale":[80],"pricing.":[81],"Based":[82],"casual":[84],"inference,":[85],"includes":[88],"two-stage":[90],"model":[91,206],"obtain":[93],"optimal":[94],"price,":[96],"first":[99],"probabilistic":[100],"demand":[101],"curve":[102,108],"is":[103,112,201],"inferred":[104],"next":[106],"revenue":[107,158],"each":[110],"locker":[111],"maximized.":[113],"And":[114],"then":[115],"further":[117],"design":[118],"win-win":[120,198],"launching":[121,199],"policy":[122,200],"make":[124],"acceptable":[129],"easier":[130],"couriers.":[132],"By":[133],"experimental":[134],"results":[135],"both":[136],"our":[138],"simulation":[139],"platform":[140],"practice,":[143],"verify":[145],"whole":[148],"(1)":[150],"outperforms":[151],"current":[153],"standard":[154],"pricing":[155,191],"improves":[157],"growth":[159],"its":[161,175],"personalization;":[162],"(2)":[163],"takes":[164],"timely":[165],"correct":[167],"reaction":[168],"market":[170],"without":[171],"human":[172],"intervene":[173],"dynamical":[176],"evolution;":[177],"(3)":[178],"finishes":[179],"all":[180],"process":[184],"within":[185],"25":[186],"minutes":[187],"such":[188],"global":[190],"becomes":[192],"possible":[193],"(4)":[195],"consider":[197],"great":[203],"importance":[204],"when":[205],"algorithm":[208],"apply":[209],"real":[211],"market.":[212]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
