{"id":"https://openalex.org/W4401857354","doi":"https://doi.org/10.1145/3637528.3671593","title":"Achieving a Better Tradeoff in Multi-stage Recommender Systems through Personalization","display_name":"Achieving a Better Tradeoff in Multi-stage Recommender Systems through Personalization","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4401857354","doi":"https://doi.org/10.1145/3637528.3671593"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671593","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671593","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671593","source":null,"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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/10.1145/3637528.3671593","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5106708657","display_name":"Ariel Evnine","orcid":null},"institutions":[{"id":"https://openalex.org/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ariel Evnine","raw_affiliation_strings":["Meta, Menlo Park, CA, USA"],"affiliations":[{"raw_affiliation_string":"Meta, Menlo Park, CA, USA","institution_ids":["https://openalex.org/I4210099336"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049931304","display_name":"Stratis Ioannidis","orcid":"https://orcid.org/0000-0001-8355-4751"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Stratis Ioannidis","raw_affiliation_strings":["Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020946771","display_name":"Dimitris Kalimeris","orcid":null},"institutions":[{"id":"https://openalex.org/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dimitris Kalimeris","raw_affiliation_strings":["Meta, Menlo Park, CA, USA"],"affiliations":[{"raw_affiliation_string":"Meta, Menlo Park, CA, USA","institution_ids":["https://openalex.org/I4210099336"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060857632","display_name":"Shankar Kalyanaraman","orcid":"https://orcid.org/0000-0002-5962-5609"},"institutions":[{"id":"https://openalex.org/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shankar Kalyanaraman","raw_affiliation_strings":["Meta, Menlo Park, CA, USA"],"affiliations":[{"raw_affiliation_string":"Meta, Menlo Park, CA, USA","institution_ids":["https://openalex.org/I4210099336"]}]},{"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/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weiwei Li","raw_affiliation_strings":["Meta, Menlo Park, CA, USA"],"affiliations":[{"raw_affiliation_string":"Meta, Menlo Park, CA, USA","institution_ids":["https://openalex.org/I4210099336"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059111932","display_name":"Israel Nir","orcid":"https://orcid.org/0000-0001-8698-4730"},"institutions":[{"id":"https://openalex.org/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Israel Nir","raw_affiliation_strings":["Meta, Menlo Park, CA, USA"],"affiliations":[{"raw_affiliation_string":"Meta, Menlo Park, CA, USA","institution_ids":["https://openalex.org/I4210099336"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101053082","display_name":"Wei Sun","orcid":"https://orcid.org/0000-0001-9066-3403"},"institutions":[{"id":"https://openalex.org/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Sun","raw_affiliation_strings":["Meta, Menlo Park, CA, USA"],"affiliations":[{"raw_affiliation_string":"Meta, Menlo Park, CA, USA","institution_ids":["https://openalex.org/I4210099336"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000980244","display_name":"Udi Weinsberg","orcid":"https://orcid.org/0000-0002-6966-1945"},"institutions":[{"id":"https://openalex.org/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Udi Weinsberg","raw_affiliation_strings":["Meta, Menlo Park, CA, USA"],"affiliations":[{"raw_affiliation_string":"Meta, Menlo Park, CA, USA","institution_ids":["https://openalex.org/I4210099336"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5106708657"],"corresponding_institution_ids":["https://openalex.org/I4210099336"],"apc_list":null,"apc_paid":null,"fwci":0.8592,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.78811583,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"4939","last_page":"4950"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T11478","display_name":"Caching and Content Delivery","score":0.9865000247955322,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.8839852809906006},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.8650075197219849},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8101832866668701},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3452990651130676},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.3270580768585205}],"concepts":[{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.8839852809906006},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.8650075197219849},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8101832866668701},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3452990651130676},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.3270580768585205}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3637528.3671593","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671593","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671593","source":null,"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3637528.3671593","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671593","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671593","source":null,"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4401857354.pdf"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W2055421030","https://openalex.org/W2069870183","https://openalex.org/W2091379987","https://openalex.org/W2108858998","https://openalex.org/W2154610494","https://openalex.org/W2208550830","https://openalex.org/W2342707026","https://openalex.org/W2500139799","https://openalex.org/W2512971201","https://openalex.org/W2560051165","https://openalex.org/W2963122331","https://openalex.org/W2972801466","https://openalex.org/W3008429003","https://openalex.org/W3011486758","https://openalex.org/W3012881846","https://openalex.org/W3099103484","https://openalex.org/W3113274682","https://openalex.org/W3182192795","https://openalex.org/W4224324117","https://openalex.org/W4281562039","https://openalex.org/W4284707446","https://openalex.org/W4306317502","https://openalex.org/W4321479992","https://openalex.org/W4361804600","https://openalex.org/W4367047381","https://openalex.org/W6673799725"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W4390273403","https://openalex.org/W4386781444","https://openalex.org/W2150182025","https://openalex.org/W3092950680","https://openalex.org/W3197542405","https://openalex.org/W2056712470","https://openalex.org/W3125580266","https://openalex.org/W4317039510"],"abstract_inverted_index":{"Recommender":[0],"systems":[1,150,187],"in":[2,25,91,147,188,193,210],"social":[3],"media":[4],"websites":[5],"provide":[6,115,164],"value":[7],"to":[8,21,39,138,171,205],"their":[9],"communities":[10],"by":[11],"recommending":[12],"engaging":[13],"content":[14],"and":[15,79,124,144,169],"meaningful":[16],"connections.":[17],"Scaling":[18],"high-quality":[19],"recommendations":[20],"billions":[22],"of":[23,36,76,122,131,160,180],"users":[24,81],"real-time":[26],"requires":[27],"sophisticated":[28],"ranking":[29,57,77,99,149],"models":[30,54],"operating":[31],"on":[32,183,201],"a":[33,71,116,128,206,211],"vast":[34],"number":[35],"potential":[37],"items":[38,50,59,78,101],"recommend,":[40],"becoming":[41],"prohibitively":[42],"expensive":[43],"computationally.":[44],"A":[45,86],"common":[46],"technique":[47],"\"funnels''":[48],"these":[49],"through":[51,119],"progressively":[52],"complex":[53],"(\"multi-stage''),":[55],"each":[56],"fewer":[58],"but":[60,107],"at":[61],"higher":[62],"computational":[63,145],"cost":[64,75,146],"for":[65,127],"greater":[66],"accuracy.":[67],"This":[68],"architecture":[69],"introduces":[70],"trade-off":[72,141],"between":[73,142],"the":[74,83,104,120,140],"providing":[80],"with":[82,151,197],"best":[84],"recommendations.":[85],"key":[87],"observation":[88],"we":[89,114,176],"make":[90],"this":[92,112,158,165],"paper":[93],"is":[94,136,167],"that,":[95],"all":[96],"else":[97],"equal,":[98],"more":[100],"indeed":[102],"improves":[103],"overall":[105,194],"objective":[106],"has":[108],"diminishing":[109],"returns.":[110],"Following":[111],"observation,":[113],"rigorous":[117],"formulation":[118],"framework":[121],"DR-submodularity,":[123],"argue":[125],"that":[126,157,163],"certain":[129],"class":[130,159],"objectives":[132],"(reward":[133],"functions),":[134],"it":[135],"possible":[137],"improve":[139],"performance":[143],"multi-stage":[148],"strong":[152],"theoretical":[153],"guarantees.":[154],"We":[155],"show":[156],"reward":[161],"functions":[162],"guarantee":[166],"large":[168],"robust":[170],"various":[172],"noise":[173],"models.":[174],"Finally,":[175],"describe":[177],"extensive":[178],"experimentation":[179],"our":[181],"method":[182],"three":[184],"real-world":[185],"recommender":[186],"Facebook,":[189],"achieving":[190],"8.8%":[191],"reduction":[192],"compute":[195],"resources":[196],"no":[198],"significant":[199],"impact":[200],"recommendation":[202],"quality,":[203],"compared":[204],"0.8%":[207],"quality":[208],"loss":[209],"non-personalized":[212],"budget":[213],"allocation.":[214]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}