{"id":"https://openalex.org/W4412876996","doi":"https://doi.org/10.1145/3711896.3737225","title":"GAS: Large-Scale Heterogeneous Personalization in Social Network Applications at Meta","display_name":"GAS: Large-Scale Heterogeneous Personalization in Social Network Applications at Meta","publication_year":2025,"publication_date":"2025-08-03","ids":{"openalex":"https://openalex.org/W4412876996","doi":"https://doi.org/10.1145/3711896.3737225"},"language":"en","primary_location":{"id":"doi:10.1145/3711896.3737225","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737225","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737225","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737225","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046735576","display_name":"Yihan Wu","orcid":"https://orcid.org/0000-0003-2718-8704"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yihan Wu","raw_affiliation_strings":["University of Maryland, College Park, MD, USA"],"raw_orcid":"https://orcid.org/0000-0003-2718-8704","affiliations":[{"raw_affiliation_string":"University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011007474","display_name":"Mingze Gao","orcid":"https://orcid.org/0000-0003-2064-3452"},"institutions":[{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mingze Gao","raw_affiliation_strings":["Meta Platforms, Inc., Menlo Park, CA, USA"],"raw_orcid":"https://orcid.org/0000-0003-2064-3452","affiliations":[{"raw_affiliation_string":"Meta Platforms, Inc., Menlo Park, CA, USA","institution_ids":["https://openalex.org/I4210114444"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Haoran Liu","orcid":"https://orcid.org/0009-0001-0349-6617"},"institutions":[{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haoran Liu","raw_affiliation_strings":["Meta Platforms, Inc., Menlo Park, CA, USA"],"raw_orcid":"https://orcid.org/0009-0001-0349-6617","affiliations":[{"raw_affiliation_string":"Meta Platforms, Inc., Menlo Park, CA, USA","institution_ids":["https://openalex.org/I4210114444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082237163","display_name":"Weiwei Li","orcid":"https://orcid.org/0000-0002-7402-6874"},"institutions":[{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weiwei Li","raw_affiliation_strings":["Meta Platforms, Inc., New York, NY, USA"],"raw_orcid":"https://orcid.org/0000-0002-7402-6874","affiliations":[{"raw_affiliation_string":"Meta Platforms, Inc., New York, NY, USA","institution_ids":["https://openalex.org/I4210114444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039613858","display_name":"Kevin Wu Han","orcid":"https://orcid.org/0009-0009-1369-0278"},"institutions":[{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kevin Han","raw_affiliation_strings":["Meta Platforms, Inc., Menlo Park, CA, USA"],"raw_orcid":"https://orcid.org/0009-0009-1369-0278","affiliations":[{"raw_affiliation_string":"Meta Platforms, Inc., Menlo Park, CA, USA","institution_ids":["https://openalex.org/I4210114444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109935473","display_name":"Junfeng Pan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junfeng Pan","raw_affiliation_strings":["Meta Platforms, Inc., Menlo Park, CA, USA"],"raw_orcid":"https://orcid.org/0009-0003-3253-5157","affiliations":[{"raw_affiliation_string":"Meta Platforms, Inc., Menlo Park, CA, USA","institution_ids":["https://openalex.org/I4210114444"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xinyi Zhang","orcid":"https://orcid.org/0009-0000-0512-1854"},"institutions":[{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xinyi Zhang","raw_affiliation_strings":["Meta Platforms, Inc., Menlo Park, CA, USA"],"raw_orcid":"https://orcid.org/0009-0000-0512-1854","affiliations":[{"raw_affiliation_string":"Meta Platforms, Inc., Menlo Park, CA, USA","institution_ids":["https://openalex.org/I4210114444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101192101","display_name":"Jiawei Wen","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiawei Wen","raw_affiliation_strings":["Meta Platforms, Inc., Menlo Park, CA, USA"],"raw_orcid":"https://orcid.org/0009-0009-3554-0430","affiliations":[{"raw_affiliation_string":"Meta Platforms, Inc., Menlo Park, CA, USA","institution_ids":["https://openalex.org/I4210114444"]}]},{"author_position":"last","author":{"id":null,"display_name":"Gedi Zhou","orcid":"https://orcid.org/0009-0009-8143-6415"},"institutions":[{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gedi Zhou","raw_affiliation_strings":["Meta Platforms, Inc., New York, NY, USA"],"raw_orcid":"https://orcid.org/0009-0009-8143-6415","affiliations":[{"raw_affiliation_string":"Meta Platforms, Inc., New York, NY, USA","institution_ids":["https://openalex.org/I4210114444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5046735576"],"corresponding_institution_ids":["https://openalex.org/I66946132"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.27512649,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5049","last_page":"5058"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9984999895095825,"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":0.9984999895095825,"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/T11478","display_name":"Caching and Content Delivery","score":0.9936000108718872,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9872999787330627,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.7819148302078247},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6950773596763611},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.6152032017707825},{"id":"https://openalex.org/keywords/social-network","display_name":"Social network (sociolinguistics)","score":0.41957271099090576},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.40324875712394714},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.3253040909767151},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.17321690917015076},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06753993034362793}],"concepts":[{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.7819148302078247},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6950773596763611},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.6152032017707825},{"id":"https://openalex.org/C4727928","wikidata":"https://www.wikidata.org/wiki/Q17164759","display_name":"Social network (sociolinguistics)","level":3,"score":0.41957271099090576},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.40324875712394714},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.3253040909767151},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.17321690917015076},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06753993034362793},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3711896.3737225","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737225","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737225","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3711896.3737225","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737225","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737225","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6000000238418579,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412876996.pdf","grobid_xml":"https://content.openalex.org/works/W4412876996.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W2058248640","https://openalex.org/W2093217068","https://openalex.org/W2095056536","https://openalex.org/W2108233388","https://openalex.org/W2112508839","https://openalex.org/W2154908680","https://openalex.org/W2164985671","https://openalex.org/W2208550830","https://openalex.org/W2624816748","https://openalex.org/W2793124255","https://openalex.org/W2808787330","https://openalex.org/W2809468631","https://openalex.org/W2937302337","https://openalex.org/W2944481928","https://openalex.org/W3035533499","https://openalex.org/W3093522446","https://openalex.org/W3102569414","https://openalex.org/W3110917906","https://openalex.org/W3117193107","https://openalex.org/W3119520891","https://openalex.org/W3124999902","https://openalex.org/W3166344325","https://openalex.org/W4240524487","https://openalex.org/W4287757828","https://openalex.org/W4290927835","https://openalex.org/W4322397527","https://openalex.org/W4328049154","https://openalex.org/W4393986369"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2109940557","https://openalex.org/W2466832359","https://openalex.org/W4391210591","https://openalex.org/W1582019636","https://openalex.org/W1499005795","https://openalex.org/W3172493050","https://openalex.org/W4385420271","https://openalex.org/W4312192618","https://openalex.org/W2109974859"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,64],"introduce":[4],"the":[5,90,95],"Generalized":[6],"Auto":[7],"Segmentation":[8],"(GAS),":[9],"an":[10],"end-to-end,":[11],"cross-domain":[12],"personalization":[13,23],"platform":[14],"developed":[15],"at":[16,108],"Meta.":[17],"GAS":[18,131],"is":[19,47,56],"designed":[20],"to":[21,42,73,114],"optimize":[22,115],"policies":[24],"for":[25,98,124],"online":[26,116],"experiments,":[27],"operating":[28],"independently":[29],"of":[30,68,130],"any":[31],"specific":[32],"server":[33],"infrastructure.":[34],"Our":[35],"process":[36],"begins":[37],"with":[38],"a":[39,60,105],"seed":[40],"experiment":[41],"collect":[43],"user":[44,79,87,126],"data,":[45],"which":[46],"followed":[48],"by":[49],"feature":[50],"logging":[51],"and":[52,77,86,94,120,140],"selection.":[53],"User":[54],"segmentation":[55],"then":[57],"performed":[58],"using":[59],"feature-threshold":[61],"method.":[62],"Specifically,":[63],"utilize":[65],"simple":[66],"combinations":[67],"single":[69],"or":[70],"dual":[71],"features":[72],"identify":[74],"concise,":[75],"explainable,":[76],"sustainable":[78],"cohorts":[80],"that":[81,110],"enhance":[82],"both":[83],"business":[84],"outcomes":[85],"experience":[88],"over":[89],"long":[91],"term.":[92],"Treatments":[93],"reward":[96],"function":[97],"these":[99],"segments":[100],"are":[101],"refined":[102],"through":[103],"ParallAX,":[104],"novel":[106],"methodology":[107],"Meta":[109,134],"uses":[111],"offline":[112],"estimates":[113],"metrics,":[117],"ensuring":[118],"impactful":[119],"precisely":[121],"tailored":[122],"interventions":[123],"diverse":[125],"groups.":[127],"The":[128],"deployment":[129],"across":[132],"various":[133],"productions":[135],"has":[136],"demonstrated":[137],"its":[138],"efficiency":[139],"effectiveness.":[141]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
