{"id":"https://openalex.org/W4292949783","doi":"https://doi.org/10.1145/3511808.3557099","title":"Hierarchical Capsule Prediction Network for Marketing Campaigns Effect","display_name":"Hierarchical Capsule Prediction Network for Marketing Campaigns Effect","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4292949783","doi":"https://doi.org/10.1145/3511808.3557099"},"language":"en","primary_location":{"id":"doi:10.1145/3511808.3557099","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557099","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2208.10113","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008967163","display_name":"Zhixuan Chu","orcid":"https://orcid.org/0000-0001-6075-1816"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhixuan Chu","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027989885","display_name":"Hui Ding","orcid":"https://orcid.org/0000-0002-1920-7613"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hui Ding","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065458315","display_name":"Guang Zeng","orcid":"https://orcid.org/0000-0002-5201-9796"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guang Zeng","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101875205","display_name":"Yuchen Huang","orcid":"https://orcid.org/0000-0002-1537-4924"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuchen Huang","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114020377","display_name":"Yan Tan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan Yan","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068558622","display_name":"Yulin Kang","orcid":"https://orcid.org/0000-0002-8150-4854"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yulin Kang","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100359839","display_name":"Sheng Li","orcid":"https://orcid.org/0000-0003-1205-8632"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sheng Li","raw_affiliation_strings":["University of Virginia, Charlottesville, VA, USA"],"affiliations":[{"raw_affiliation_string":"University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5008967163"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4188,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.57857635,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3043","last_page":"3051"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9986000061035156,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9986000061035156,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9973999857902527,"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/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9898999929428101,"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/computer-science","display_name":"Computer science","score":0.7236223816871643},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6552776098251343},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.5669021606445312},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5460200905799866},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4431038200855255},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4318541884422302},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3996892273426056},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38731276988983154},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.363128662109375},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13389766216278076}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7236223816871643},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6552776098251343},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.5669021606445312},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5460200905799866},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4431038200855255},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4318541884422302},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3996892273426056},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38731276988983154},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.363128662109375},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13389766216278076},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3511808.3557099","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557099","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2208.10113","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2208.10113","pdf_url":"https://arxiv.org/pdf/2208.10113","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2208.10113","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2208.10113","pdf_url":"https://arxiv.org/pdf/2208.10113","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.47999998927116394,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1573569448","https://openalex.org/W2084868005","https://openalex.org/W2101259556","https://openalex.org/W2132917208","https://openalex.org/W2140833774","https://openalex.org/W2145473366","https://openalex.org/W2270330859","https://openalex.org/W2295598076","https://openalex.org/W2624871570","https://openalex.org/W2752690044","https://openalex.org/W2785994986","https://openalex.org/W2803471865","https://openalex.org/W2909882940","https://openalex.org/W2945420892","https://openalex.org/W2949448715","https://openalex.org/W2962695761","https://openalex.org/W2963703618","https://openalex.org/W3046975451","https://openalex.org/W3086082883","https://openalex.org/W3100105905","https://openalex.org/W3113037084","https://openalex.org/W3160537436","https://openalex.org/W3171442082","https://openalex.org/W3203505583","https://openalex.org/W4213113494","https://openalex.org/W4221153138","https://openalex.org/W4226369498","https://openalex.org/W4283022696","https://openalex.org/W4294037149","https://openalex.org/W4385245566"],"related_works":["https://openalex.org/W579810227","https://openalex.org/W2952780262","https://openalex.org/W2979495269","https://openalex.org/W2392917763","https://openalex.org/W2083429127","https://openalex.org/W2358855848","https://openalex.org/W2142145894","https://openalex.org/W2033808215","https://openalex.org/W2359307945","https://openalex.org/W4388176285"],"abstract_inverted_index":{"Marketing":[0],"campaigns":[1,19,61],"are":[2,84],"a":[3,11,21,73,92,102,133],"set":[4],"of":[5,58,72,80,117,143,160],"strategic":[6],"activities":[7],"that":[8,34],"can":[9],"promote":[10],"business's":[12],"goal.":[13],"The":[14],"effect":[15,71,126],"prediction":[16,98,127],"for":[17,46,139],"marketing":[18,48,60,75,144],"in":[20,124,171],"real":[22,155,172],"industrial":[23,173],"scenario":[24],"is":[25,37,53],"very":[26],"complex":[27],"and":[28,68,129,154,167],"challenging":[29],"due":[30],"to":[31,89],"the":[32,47,56,70,78,118,125,141,151,158,164],"fact":[33],"prior":[35],"knowledge":[36],"often":[38],"learned":[39],"from":[40],"observation":[41],"data,":[42],"without":[43],"any":[44],"intervention":[45],"campaign.":[49,76],"Furthermore,":[50],"each":[51],"subject":[52],"always":[54],"under":[55],"interference":[57],"several":[59],"simultaneously.":[62],"Therefore,":[63],"we":[64,112,130],"cannot":[65],"easily":[66],"parse":[67,120],"evaluate":[69],"single":[74],"To":[77],"best":[79],"our":[81,161],"knowledge,":[82],"there":[83],"currently":[85],"no":[86],"effective":[87],"methodologies":[88],"solve":[90],"such":[91],"problem,":[93],"i.e.,":[94],"modeling":[95],"an":[96,114],"individual-level":[97],"task":[99,128],"based":[100,148],"on":[101,149],"hierarchical":[103],"structure":[104,122],"with":[105],"multiple":[106],"intertwined":[107],"events.":[108],"In":[109],"this":[110],"paper,":[111],"provide":[113],"in-depth":[115],"analysis":[116],"underlying":[119],"tree-like":[121],"involved":[123],"further":[131],"establish":[132],"Hierarchical":[134],"Capsule":[135],"Prediction":[136],"Network":[137],"(HapNet)":[138],"predicting":[140],"effects":[142],"campaigns.":[145],"Extensive":[146],"results":[147],"both":[150],"synthetic":[152],"data":[153,156],"demonstrate":[157],"superiority":[159],"model":[162],"over":[163],"state-of-the-art":[165],"methods":[166],"show":[168],"remarkable":[169],"practicability":[170],"applications.":[174]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
