{"id":"https://openalex.org/W3201104188","doi":"https://doi.org/10.1145/3460231.3474235","title":"Online Evaluation Methods for the Causal Effect of Recommendations","display_name":"Online Evaluation Methods for the Causal Effect of Recommendations","publication_year":2021,"publication_date":"2021-09-13","ids":{"openalex":"https://openalex.org/W3201104188","doi":"https://doi.org/10.1145/3460231.3474235","mag":"3201104188"},"language":"en","primary_location":{"id":"doi:10.1145/3460231.3474235","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460231.3474235","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Fifteenth ACM Conference on Recommender Systems","raw_type":"proceedings-article"},"type":"conference-paper","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/A5026575535","display_name":"Masahiro Sato","orcid":"https://orcid.org/0000-0003-0000-6341"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Masahiro Sato","raw_affiliation_strings":["independent researcher, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"independent researcher, Japan","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5026575535"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0227,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.76743311,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"96","last_page":"101"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9986000061035156,"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":0.9986000061035156,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9980000257492065,"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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9789999723434448,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interleaving","display_name":"Interleaving","score":0.7599798440933228},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7148141264915466},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.5180192589759827},{"id":"https://openalex.org/keywords/propensity-score-matching","display_name":"Propensity score matching","score":0.5119875073432922},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.49424174427986145},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.4424508810043335},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4301585853099823},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3712688684463501},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2858000099658966},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2776145040988922},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.2771362066268921},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.19727465510368347},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1543784737586975}],"concepts":[{"id":"https://openalex.org/C28034677","wikidata":"https://www.wikidata.org/wiki/Q17092530","display_name":"Interleaving","level":2,"score":0.7599798440933228},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7148141264915466},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.5180192589759827},{"id":"https://openalex.org/C17923572","wikidata":"https://www.wikidata.org/wiki/Q7250160","display_name":"Propensity score matching","level":2,"score":0.5119875073432922},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.49424174427986145},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.4424508810043335},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4301585853099823},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3712688684463501},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2858000099658966},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2776145040988922},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2771362066268921},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.19727465510368347},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1543784737586975},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3460231.3474235","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460231.3474235","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Fifteenth ACM Conference on Recommender Systems","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":32,"referenced_works":["https://openalex.org/W941378780","https://openalex.org/W1963548721","https://openalex.org/W1978108654","https://openalex.org/W1982530130","https://openalex.org/W2003473029","https://openalex.org/W2047221353","https://openalex.org/W2049938617","https://openalex.org/W2050599922","https://openalex.org/W2082831169","https://openalex.org/W2094790959","https://openalex.org/W2116354394","https://openalex.org/W2121710227","https://openalex.org/W2132917208","https://openalex.org/W2150291618","https://openalex.org/W2156541733","https://openalex.org/W2163996478","https://openalex.org/W2168639902","https://openalex.org/W2219888463","https://openalex.org/W2407295445","https://openalex.org/W2463291531","https://openalex.org/W2629213068","https://openalex.org/W2952613481","https://openalex.org/W2969886764","https://openalex.org/W2972548050","https://openalex.org/W2973216567","https://openalex.org/W2998534896","https://openalex.org/W3015106622","https://openalex.org/W3089238887","https://openalex.org/W3103310105","https://openalex.org/W3106212003","https://openalex.org/W3122725723","https://openalex.org/W3142308083"],"related_works":["https://openalex.org/W2947954788","https://openalex.org/W4296363906","https://openalex.org/W4281756720","https://openalex.org/W4322505266","https://openalex.org/W4385077270","https://openalex.org/W4225280467","https://openalex.org/W2541915084","https://openalex.org/W304115605","https://openalex.org/W2884906108","https://openalex.org/W2540939911"],"abstract_inverted_index":{"Evaluating":[0],"the":[1,11,66,89,102,106,110,121,134,143],"causal":[2,12,49,79,107],"effect":[3,13,108],"of":[4,48,55,78,91,147],"recommendations":[5],"is":[6,35,109],"an":[7,21,30,95],"important":[8],"objective":[9],"because":[10],"on":[14,94,101],"user":[15,26],"interactions":[16],"can":[17,71],"directly":[18],"leads":[19],"to":[20,37,41,83,170],"increase":[22],"in":[23,76],"sales":[24],"and":[25,61,98,115,145,164],"engagement.":[27],"To":[28,118],"select":[29,127],"optimal":[31],"recommendation":[32,74],"model,":[33],"it":[34],"common":[36],"conduct":[38],"A/B":[39,46,171],"testing":[40,47],"compare":[42,73],"model":[43],"performance.":[44],"However,":[45],"effects":[50],"requires":[51],"a":[52],"large":[53],"number":[54],"users,":[56],"making":[57],"such":[58],"experiments":[59],"costly":[60],"risky.":[62],"We":[63,140],"therefore":[64],"propose":[65],"first":[67],"interleaving":[68,85],"methods":[69,150,161],"that":[70,120,158,165],"efficiently":[72],"models":[75],"terms":[77],"effects.":[80],"In":[81],"contrast":[82],"conventional":[84],"methods,":[86],"we":[87,125],"measure":[88],"outcomes":[90,113,135],"both":[92],"items":[93,99,128],"interleaved":[96,103],"list":[97],"not":[100],"list,":[104],"since":[105],"difference":[111],"between":[112],"with":[114,129],"without":[116],"recommendations.":[117],"ensure":[119],"evaluations":[122],"are":[123,162],"unbiased,":[124],"either":[126],"equal":[130],"probability":[131],"or":[132],"weight":[133],"using":[136],"inverse":[137],"propensity":[138],"scores.":[139],"then":[141],"verify":[142],"unbiasedness":[144],"efficiency":[146,169],"online":[148,153],"evaluation":[149],"through":[151],"simulated":[152],"experiments.":[154],"The":[155],"results":[156],"indicate":[157],"our":[159],"proposed":[160],"unbiased":[163],"they":[166],"have":[167],"superior":[168],"testing.":[172]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2025-10-10T00:00:00"}