{"id":"https://openalex.org/W4387846779","doi":"https://doi.org/10.1145/3583780.3615002","title":"Out of the Box Thinking: Improving Customer Lifetime Value Modelling via Expert Routing and Game Whale Detection","display_name":"Out of the Box Thinking: Improving Customer Lifetime Value Modelling via Expert Routing and Game Whale Detection","publication_year":2023,"publication_date":"2023-10-21","ids":{"openalex":"https://openalex.org/W4387846779","doi":"https://doi.org/10.1145/3583780.3615002"},"language":"en","primary_location":{"id":"doi:10.1145/3583780.3615002","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3615002","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","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/A5076549491","display_name":"Shijie Zhang","orcid":"https://orcid.org/0000-0003-3226-6842"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shijie Zhang","raw_affiliation_strings":["Tencent, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091241447","display_name":"Xin Yan","orcid":"https://orcid.org/0009-0004-3165-6640"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Yan","raw_affiliation_strings":["Tencent, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029321555","display_name":"Xuejiao Yang","orcid":"https://orcid.org/0000-0002-1124-214X"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuejiao Yang","raw_affiliation_strings":["Tencent, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020321261","display_name":"Binfeng Jia","orcid":"https://orcid.org/0000-0001-6820-2846"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Binfeng Jia","raw_affiliation_strings":["Tencent, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052324447","display_name":"Shuangyang Wang","orcid":"https://orcid.org/0000-0002-6180-8607"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuangyang Wang","raw_affiliation_strings":["Tencent, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5076549491"],"corresponding_institution_ids":["https://openalex.org/I2250653659"],"apc_list":null,"apc_paid":null,"fwci":6.3931,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.96698385,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3206","last_page":"3215"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9983999729156494,"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"}},"topics":[{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9983999729156494,"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/T12384","display_name":"Customer churn and segmentation","score":0.9983000159263611,"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/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9972000122070312,"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":0.6958107948303223},{"id":"https://openalex.org/keywords/whale","display_name":"Whale","score":0.6047409176826477},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5496793389320374},{"id":"https://openalex.org/keywords/shapley-value","display_name":"Shapley value","score":0.5368749499320984},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34838616847991943},{"id":"https://openalex.org/keywords/game-theory","display_name":"Game theory","score":0.3103087246417999}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6958107948303223},{"id":"https://openalex.org/C2777704720","wikidata":"https://www.wikidata.org/wiki/Q1865281","display_name":"Whale","level":2,"score":0.6047409176826477},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5496793389320374},{"id":"https://openalex.org/C199022921","wikidata":"https://www.wikidata.org/wiki/Q240046","display_name":"Shapley value","level":3,"score":0.5368749499320984},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34838616847991943},{"id":"https://openalex.org/C177142836","wikidata":"https://www.wikidata.org/wiki/Q44455","display_name":"Game theory","level":2,"score":0.3103087246417999},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C505870484","wikidata":"https://www.wikidata.org/wiki/Q180538","display_name":"Fishery","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3583780.3615002","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3615002","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","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":36,"referenced_works":["https://openalex.org/W2017565819","https://openalex.org/W2025348367","https://openalex.org/W2026882341","https://openalex.org/W2032536435","https://openalex.org/W2032612424","https://openalex.org/W2059888405","https://openalex.org/W2081547566","https://openalex.org/W2127624016","https://openalex.org/W2130903752","https://openalex.org/W2144752499","https://openalex.org/W2162244340","https://openalex.org/W2295739661","https://openalex.org/W2475334473","https://openalex.org/W2510174253","https://openalex.org/W2575693697","https://openalex.org/W2595177306","https://openalex.org/W2595697910","https://openalex.org/W2794065766","https://openalex.org/W2802539019","https://openalex.org/W2902121735","https://openalex.org/W2962989965","https://openalex.org/W2964182926","https://openalex.org/W2990138404","https://openalex.org/W2994850640","https://openalex.org/W3035298482","https://openalex.org/W3035596828","https://openalex.org/W3045170404","https://openalex.org/W3098057095","https://openalex.org/W3141797743","https://openalex.org/W3172874292","https://openalex.org/W4226493408","https://openalex.org/W4232521459","https://openalex.org/W4284701460","https://openalex.org/W4293827866","https://openalex.org/W4321480069","https://openalex.org/W6681414149"],"related_works":["https://openalex.org/W4249226508","https://openalex.org/W2380202880","https://openalex.org/W4233790924","https://openalex.org/W3126099358","https://openalex.org/W4256656994","https://openalex.org/W2051770645","https://openalex.org/W1894585900","https://openalex.org/W2006581498","https://openalex.org/W2013832345","https://openalex.org/W1998066849"],"abstract_inverted_index":{"Customer":[0],"lifetime":[1],"value":[2],"(LTV)":[3],"prediction":[4,64,93,119,151,270],"is":[5,31,131,276],"essential":[6],"for":[7,17,115,246],"mobile":[8,27],"game":[9,44,55,67,80,103,153,170,193,202,230,272],"publishers":[10],"trying":[11],"to":[12,87,101,148,209],"optimize":[13],"the":[14,23,59,89,116,121,124,135,178,201,211,223,257],"advertising":[15],"investment":[16],"each":[18],"user":[19,126],"acquisition":[20],"based":[21],"on":[22,48,282],"estimated":[24],"worth.":[25],"In":[26,138,160],"games,":[28],"deploying":[29],"microtransactions":[30],"a":[32,40,144,157,166,206,242,251],"simple":[33],"yet":[34],"effective":[35],"monetization":[36],"strategy,":[37],"which":[38,130],"attracts":[39],"tiny":[41],"group":[42],"of":[43,53,61,91,215,239,263,267],"whales":[45,56,81,231],"who":[46],"splurge":[47],"in-game":[49],"purchases.":[50],"The":[51,261],"presence":[52],"such":[54],"may":[57],"impede":[58],"practicality":[60],"existing":[62,110],"LTV":[63,92,107,118,150,216,269],"models,":[65],"since":[66],"whales'":[68],"purchase":[69,243],"behaviours":[70],"always":[71],"exhibit":[72],"varied":[73],"distribution":[74],"from":[75],"general":[76],"users.":[77],"Consequently,":[78],"identifying":[79],"can":[82,174,220,255],"open":[83],"up":[84],"new":[85],"opportunities":[86],"improve":[88],"accuracy":[90],"models.":[94],"However,":[95],"little":[96],"attention":[97],"has":[98],"been":[99],"paid":[100],"applying":[102],"whale":[104,154,171,203,273],"detection":[105,155,274],"in":[106,134,156,181,265],"prediction,":[108],"and":[109,152,195,226,233,271],"works":[111],"are":[112,128],"mainly":[113],"specialized":[114],"long-term":[117],"with":[120,183],"assumption":[122],"that":[123,173,254],"high-quality":[125],"features":[127],"available,":[129],"not":[132,175],"applicable":[133],"UA":[136],"stage.":[137],"this":[139],"paper,":[140],"we":[141,162,219,249],"propose":[142],"ExpLTV,":[143,161],"novel":[145],"multi-task":[146],"framework":[147],"perform":[149],"unified":[158],"way.":[159],"first":[163],"innovatively":[164],"design":[165,250],"deep":[167],"neural":[168],"network-based":[169],"detector":[172,204],"only":[176],"infer":[177],"intrinsic":[179],"order":[180],"accordance":[182],"monetary":[184],"value,":[185],"but":[186],"also":[187],"precisely":[188],"identify":[189],"high":[190],"spenders":[191,235],"(i.e.,":[192,229],"whales)":[194],"low":[196,234],"spenders.":[197],"Then,":[198],"by":[199],"treating":[200],"as":[205],"gating":[207],"network":[208],"decide":[210],"different":[212],"mixture":[213],"patterns":[214],"experts":[217],"assembling,":[218],"thoroughly":[221],"leverage":[222],"shared":[224,252],"information":[225,228],"scenario-specific":[227],"modelling":[232],"modelling).":[236],"Finally,":[237],"instead":[238],"separately":[240],"designing":[241],"rate":[244],"estimator":[245,253],"two":[247],"tasks,":[248],"preserve":[256],"inner":[258],"task":[259],"relationships.":[260],"superiority":[262],"ExpLTV":[264],"terms":[266],"its":[268],"effectiveness":[275],"further":[277],"validated":[278],"via":[279],"extensive":[280],"experiments":[281],"three":[283],"industrial":[284],"datasets.":[285]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":5}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}