{"id":"https://openalex.org/W4284974072","doi":"https://doi.org/10.1145/3534678.3539193","title":"Multi-objective Optimization of Notifications Using Offline Reinforcement Learning","display_name":"Multi-objective Optimization of Notifications Using Offline Reinforcement Learning","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4284974072","doi":"https://doi.org/10.1145/3534678.3539193"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539193","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539193","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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/A5017662269","display_name":"Prakruthi Prabhakar","orcid":null},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Prakruthi Prabhakar","raw_affiliation_strings":["LinkedIn Corporation, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"LinkedIn Corporation, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101634762","display_name":"Yiping Yuan","orcid":"https://orcid.org/0000-0002-0509-0493"},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiping Yuan","raw_affiliation_strings":["LinkedIn Corporation, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"LinkedIn Corporation, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101461626","display_name":"Guangyu Yang","orcid":"https://orcid.org/0000-0002-4692-1361"},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guangyu Yang","raw_affiliation_strings":["LinkedIn Corporation, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"LinkedIn Corporation, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101040870","display_name":"Wensheng Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wensheng Sun","raw_affiliation_strings":["LinkedIn Corporation, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"LinkedIn Corporation, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061435342","display_name":"Ajith Muralidharan","orcid":"https://orcid.org/0000-0001-5734-526X"},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ajith Muralidharan","raw_affiliation_strings":["LinkedIn Corporation, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"LinkedIn Corporation, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5017662269"],"corresponding_institution_ids":["https://openalex.org/I1316064682"],"apc_list":null,"apc_paid":null,"fwci":1.2975,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.77193714,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3752","last_page":"3760"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11478","display_name":"Caching and Content Delivery","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11478","display_name":"Caching and Content Delivery","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11165","display_name":"Image and Video Quality Assessment","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12238","display_name":"Green IT and Sustainability","score":0.9930999875068665,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.9263653755187988},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.8595149517059326},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8325211405754089},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.5899535417556763},{"id":"https://openalex.org/keywords/q-learning","display_name":"Q-learning","score":0.5146340131759644},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4928727149963379},{"id":"https://openalex.org/keywords/offline-learning","display_name":"Offline learning","score":0.4874720573425293},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4799690246582031},{"id":"https://openalex.org/keywords/online-and-offline","display_name":"Online and offline","score":0.4796362519264221},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4448612630367279},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.43579035997390747},{"id":"https://openalex.org/keywords/online-learning","display_name":"Online learning","score":0.39092469215393066},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.35658448934555054},{"id":"https://openalex.org/keywords/multimedia","display_name":"Multimedia","score":0.18283209204673767}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.9263653755187988},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.8595149517059326},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8325211405754089},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.5899535417556763},{"id":"https://openalex.org/C188116033","wikidata":"https://www.wikidata.org/wiki/Q2664563","display_name":"Q-learning","level":3,"score":0.5146340131759644},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4928727149963379},{"id":"https://openalex.org/C2780490138","wikidata":"https://www.wikidata.org/wiki/Q7079636","display_name":"Offline learning","level":3,"score":0.4874720573425293},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4799690246582031},{"id":"https://openalex.org/C2780102126","wikidata":"https://www.wikidata.org/wiki/Q10928179","display_name":"Online and offline","level":2,"score":0.4796362519264221},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4448612630367279},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.43579035997390747},{"id":"https://openalex.org/C2986087404","wikidata":"https://www.wikidata.org/wiki/Q15946010","display_name":"Online learning","level":2,"score":0.39092469215393066},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.35658448934555054},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.18283209204673767},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"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:10.1145/3534678.3539193","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539193","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7900000214576721,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W41554520","https://openalex.org/W1514587017","https://openalex.org/W2063438171","https://openalex.org/W2119738171","https://openalex.org/W2746553466","https://openalex.org/W2753910905","https://openalex.org/W2767453670","https://openalex.org/W2767807341","https://openalex.org/W2788295351","https://openalex.org/W2809069282","https://openalex.org/W2809074060","https://openalex.org/W2902572901","https://openalex.org/W2907642321","https://openalex.org/W2963842088","https://openalex.org/W3033324992","https://openalex.org/W3081189998","https://openalex.org/W3102778384","https://openalex.org/W3164005523","https://openalex.org/W3193504637","https://openalex.org/W4213192559","https://openalex.org/W4401558999"],"related_works":["https://openalex.org/W2808418668","https://openalex.org/W2357975469","https://openalex.org/W2101748387","https://openalex.org/W3096874164","https://openalex.org/W4281812492","https://openalex.org/W3105579180","https://openalex.org/W2970347269","https://openalex.org/W3167472281","https://openalex.org/W4400868993","https://openalex.org/W3207447243"],"abstract_inverted_index":{"Mobile":[0],"notification":[1,36,64],"systems":[2],"play":[3],"a":[4,8,40,74],"major":[5],"role":[6],"in":[7,50],"variety":[9],"of":[10,70,103],"applications":[11],"to":[12,18,21,61],"communicate,":[13],"send":[14],"alerts":[15],"and":[16,89,97,101,110],"reminders":[17],"the":[19,34,51,68,85,99,104],"users":[20],"inform":[22],"them":[23],"about":[24],"news,":[25],"events":[26],"or":[27],"messages.":[28],"In":[29],"this":[30],"paper,":[31],"we":[32,45],"formulate":[33],"near-real-time":[35],"decision":[37],"problem":[38,88],"as":[39],"Markov":[41],"Decision":[42],"Process":[43],"where":[44],"optimize":[46,62],"for":[47],"multiple":[48],"objectives":[49],"rewards.":[52],"We":[53,66,92],"propose":[54],"an":[55],"end-to-end":[56],"offline":[57,71,109],"reinforcement":[58],"learning":[59,72],"framework":[60],"sequential":[63],"decisions.":[65],"address":[67],"challenge":[69],"using":[73],"Double":[75],"Deep":[76],"Q-network":[77],"method":[78],"based":[79],"on":[80],"Conservative":[81],"Q-learning":[82],"that":[83],"mitigates":[84],"distributional":[86],"shift":[87],"Q-value":[90],"overestimation.":[91],"illustrate":[93],"our":[94],"fully-deployed":[95],"system":[96],"demonstrate":[98],"performance":[100],"benefits":[102],"proposed":[105],"approach":[106],"through":[107],"both":[108],"online":[111],"experiments.":[112]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
