{"id":"https://openalex.org/W1095970959","doi":"https://doi.org/10.1145/2740908.2741998","title":"Ad Recommendation Systems for Life-Time Value Optimization","display_name":"Ad Recommendation Systems for Life-Time Value Optimization","publication_year":2015,"publication_date":"2015-05-18","ids":{"openalex":"https://openalex.org/W1095970959","doi":"https://doi.org/10.1145/2740908.2741998","mag":"1095970959"},"language":"en","primary_location":{"id":"doi:10.1145/2740908.2741998","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2740908.2741998","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th International Conference on World Wide Web","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/A5066303412","display_name":"Georgios Theocharous","orcid":null},"institutions":[{"id":"https://openalex.org/I1306409833","display_name":"Adobe Systems (United States)","ror":"https://ror.org/059tvcg64","country_code":"US","type":"company","lineage":["https://openalex.org/I1306409833"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Georgios Theocharous","raw_affiliation_strings":["Adobe Research, San Jose, USA"],"affiliations":[{"raw_affiliation_string":"Adobe Research, San Jose, USA","institution_ids":["https://openalex.org/I1306409833"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066332280","display_name":"Philip S. Thomas","orcid":"https://orcid.org/0000-0002-9904-1800"},"institutions":[{"id":"https://openalex.org/I1306409833","display_name":"Adobe Systems (United States)","ror":"https://ror.org/059tvcg64","country_code":"US","type":"company","lineage":["https://openalex.org/I1306409833"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Philip S. Thomas","raw_affiliation_strings":["UMassAmherst and Adobe Research, Amherst, USA"],"affiliations":[{"raw_affiliation_string":"UMassAmherst and Adobe Research, Amherst, USA","institution_ids":["https://openalex.org/I1306409833"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013843778","display_name":"Mohammad Ghavamzadeh","orcid":"https://orcid.org/0000-0003-0930-8688"},"institutions":[{"id":"https://openalex.org/I1306409833","display_name":"Adobe Systems (United States)","ror":"https://ror.org/059tvcg64","country_code":"US","type":"company","lineage":["https://openalex.org/I1306409833"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohammad Ghavamzadeh","raw_affiliation_strings":["Adobe Research and INRIA, San Jose, USA"],"affiliations":[{"raw_affiliation_string":"Adobe Research and INRIA, San Jose, USA","institution_ids":["https://openalex.org/I1306409833"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5066303412"],"corresponding_institution_ids":["https://openalex.org/I1306409833"],"apc_list":null,"apc_paid":null,"fwci":2.0031,"has_fulltext":false,"cited_by_count":56,"citation_normalized_percentile":{"value":0.86853975,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1305","last_page":"1310"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9998999834060669,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9998999834060669,"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/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9957000017166138,"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/T10603","display_name":"Smart Grid Energy Management","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/computer-science","display_name":"Computer science","score":0.8073689937591553},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.6824344396591187},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6602737903594971},{"id":"https://openalex.org/keywords/visitor-pattern","display_name":"Visitor pattern","score":0.5889163613319397},{"id":"https://openalex.org/keywords/greedy-algorithm","display_name":"Greedy algorithm","score":0.5077956318855286},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4728289544582367},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4709499776363373},{"id":"https://openalex.org/keywords/click-through-rate","display_name":"Click-through rate","score":0.4694654047489166},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.455191969871521},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.36249589920043945},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1738700270652771},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.09486019611358643},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07969588041305542}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8073689937591553},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.6824344396591187},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6602737903594971},{"id":"https://openalex.org/C48947383","wikidata":"https://www.wikidata.org/wiki/Q830719","display_name":"Visitor pattern","level":2,"score":0.5889163613319397},{"id":"https://openalex.org/C51823790","wikidata":"https://www.wikidata.org/wiki/Q504353","display_name":"Greedy algorithm","level":2,"score":0.5077956318855286},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4728289544582367},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4709499776363373},{"id":"https://openalex.org/C115174607","wikidata":"https://www.wikidata.org/wiki/Q1100934","display_name":"Click-through rate","level":2,"score":0.4694654047489166},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.455191969871521},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.36249589920043945},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1738700270652771},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.09486019611358643},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07969588041305542},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2740908.2741998","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2740908.2741998","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th International Conference on World Wide Web","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1513618424","https://openalex.org/W1514587017","https://openalex.org/W1515851193","https://openalex.org/W1786332878","https://openalex.org/W1809653203","https://openalex.org/W1979643318","https://openalex.org/W2072758956","https://openalex.org/W2077806964","https://openalex.org/W2098184356","https://openalex.org/W2112420033","https://openalex.org/W2113065326","https://openalex.org/W2120346334","https://openalex.org/W2121863487","https://openalex.org/W2126728600","https://openalex.org/W2142620093","https://openalex.org/W2143748761","https://openalex.org/W2155982020","https://openalex.org/W2273088453","https://openalex.org/W2289748525","https://openalex.org/W2559997609","https://openalex.org/W2911964244","https://openalex.org/W2913668833","https://openalex.org/W2964297722","https://openalex.org/W3006656721","https://openalex.org/W3023069927","https://openalex.org/W6677102192","https://openalex.org/W6694250863","https://openalex.org/W6766130871"],"related_works":["https://openalex.org/W4387497383","https://openalex.org/W3183948672","https://openalex.org/W3173606202","https://openalex.org/W3110381201","https://openalex.org/W2948807893","https://openalex.org/W2935909890","https://openalex.org/W2778153218","https://openalex.org/W2758277628","https://openalex.org/W1531601525","https://openalex.org/W2389484663"],"abstract_inverted_index":{"The":[0],"main":[1],"objective":[2],"in":[3,118,241],"the":[4,18,21,25,48,65,83,97,100,113,172,191,197,200,218,227,230,242],"ad":[5,22,185,243],"recommendation":[6,186,244],"problem":[7],"is":[8,116],"to":[9,71,123,128,136,143,160,182],"find":[10],"a":[11,61,105,130,138,162,175,183],"strategy":[12,32,133],"that,":[13],"for":[14,60,93,189,195,238],"each":[15],"visitor":[16],"of":[17,28,47,82,104,155,164,174,199],"website,":[19],"selects":[20],"that":[23,206],"has":[24],"highest":[26],"probability":[27],"being":[29],"clicked.":[30],"This":[31],"could":[33],"be":[34,70],"computed":[35],"using":[36,140],"supervised":[37],"learning":[38,74],"or":[39,99],"contextual":[40],"bandit":[41],"algorithms,":[42],"which":[43,77],"treat":[44],"two":[45,52,80,87,124],"visits":[46,81],"same":[49,84],"user":[50,85],"as":[51],"separate":[53],"independent":[54],"visitors,":[55,89],"and":[56,86,90,134,194,235],"thus,":[57,91],"optimize":[58],"greedily":[59],"single":[62],"step":[63],"into":[64,96],"future.":[66],"Another":[67],"approach":[68,115],"would":[69],"use":[72,161],"reinforcement":[73],"(RL)":[75],"methods,":[76],"differentiate":[78],"between":[79,229],"different":[88],"optimizes":[92],"multiple":[94],"steps":[95],"future":[98],"life-time":[101],"value":[102],"(LTV)":[103],"customer.":[106],"While":[107],"greedy":[108,219],"methods":[109,181],"have":[110],"been":[111],"well-studied,":[112],"LTV":[114,132,208,236],"still":[117],"its":[119,145],"infancy,":[120],"mainly":[121],"due":[122],"fundamental":[125,224],"challenges:":[126],"how":[127,135],"compute":[129],"good":[131],"evaluate":[137],"solution":[139],"historical":[141],"data":[142],"ensure":[144],"\"safety\"":[146],"before":[147],"deployment.":[148],"In":[149],"this":[150],"paper,":[151],"we":[152],"tackle":[153],"both":[154,188],"these":[156,180,213],"challenges":[157],"by":[158],"proposing":[159],"family":[163],"off-policy":[165,214],"evaluation":[166,215,240],"techniques":[167,216],"with":[168,212],"statistical":[169],"guarantees":[170],"about":[171],"performance":[173,193,239],"new":[176],"strategy.":[177],"We":[178],"apply":[179],"real":[184],"problem,":[187],"evaluating":[190],"final":[192],"optimizing":[196],"parameters":[198],"RL":[201],"algorithm.":[202],"Our":[203],"results":[204],"show":[205],"our":[207],"optimization":[209],"algorithm":[210],"equipped":[211],"outperforms":[217],"approaches.":[220],"They":[221],"also":[222],"give":[223],"insights":[225],"on":[226],"difference":[228],"click":[231],"through":[232],"rate":[233],"(CTR)":[234],"metrics":[237],"problem.":[245]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}