{"id":"https://openalex.org/W4296591856","doi":"https://doi.org/10.1145/3523227.3547384","title":"Dynamic Surrogate Switching: Sample-Efficient Search for Factorization Machine Configurations in Online Recommendations","display_name":"Dynamic Surrogate Switching: Sample-Efficient Search for Factorization Machine Configurations in Online Recommendations","publication_year":2022,"publication_date":"2022-09-13","ids":{"openalex":"https://openalex.org/W4296591856","doi":"https://doi.org/10.1145/3523227.3547384"},"language":"en","primary_location":{"id":"doi:10.1145/3523227.3547384","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3523227.3547384","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 16th ACM Conference on Recommender Systems","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2209.14598","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007096285","display_name":"Bla\u017e \u0160krlj","orcid":"https://orcid.org/0000-0002-9916-8756"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bla\u017e \u0160krlj","raw_affiliation_strings":["Outbrain inc., Slovenia"],"affiliations":[{"raw_affiliation_string":"Outbrain inc., Slovenia","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063841796","display_name":"Adi Schwartz","orcid":"https://orcid.org/0000-0002-2141-032X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Adi Schwartz","raw_affiliation_strings":["Outbrain Inc., Israel"],"affiliations":[{"raw_affiliation_string":"Outbrain Inc., Israel","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001790726","display_name":"Jure Ferle\u017e","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jure Ferle\u017e","raw_affiliation_strings":["Outbrain inc., Slovenia"],"affiliations":[{"raw_affiliation_string":"Outbrain inc., Slovenia","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003664098","display_name":"Davorin Kopi\u010d","orcid":"https://orcid.org/0000-0003-4847-6916"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Davorin Kopi\u010d","raw_affiliation_strings":["Outbrain inc., Slovenia"],"affiliations":[{"raw_affiliation_string":"Outbrain inc., Slovenia","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079167776","display_name":"Naama Ziporin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Naama Ziporin","raw_affiliation_strings":["Outbrain inc., Israel"],"affiliations":[{"raw_affiliation_string":"Outbrain inc., Israel","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5007096285"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10409729,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"472","last_page":"475"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9979000091552734,"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"}},"topics":[{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9979000091552734,"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"}},{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9965000152587891,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.8247621059417725},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7459107637405396},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7136740684509277},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6367694139480591},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.6306639909744263},{"id":"https://openalex.org/keywords/surrogate-model","display_name":"Surrogate model","score":0.6107854247093201},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5975430011749268},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.5424450635910034},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter optimization","score":0.5213640928268433},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.5024590492248535},{"id":"https://openalex.org/keywords/relevance-vector-machine","display_name":"Relevance vector machine","score":0.48739057779312134},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.2910877466201782},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.1966787874698639}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.8247621059417725},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7459107637405396},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7136740684509277},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6367694139480591},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6306639909744263},{"id":"https://openalex.org/C131675550","wikidata":"https://www.wikidata.org/wiki/Q7646884","display_name":"Surrogate model","level":2,"score":0.6107854247093201},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5975430011749268},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.5424450635910034},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.5213640928268433},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.5024590492248535},{"id":"https://openalex.org/C14948415","wikidata":"https://www.wikidata.org/wiki/Q7310972","display_name":"Relevance vector machine","level":3,"score":0.48739057779312134},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2910877466201782},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.1966787874698639},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3523227.3547384","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3523227.3547384","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 16th ACM Conference on Recommender Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2209.14598","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2209.14598","pdf_url":"https://arxiv.org/pdf/2209.14598","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2209.14598","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2209.14598","pdf_url":"https://arxiv.org/pdf/2209.14598","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1502922572","https://openalex.org/W1678356000","https://openalex.org/W1986490585","https://openalex.org/W2076618162","https://openalex.org/W2094286023","https://openalex.org/W2244501064","https://openalex.org/W2509235963","https://openalex.org/W2755602972","https://openalex.org/W2889526258","https://openalex.org/W2966284335","https://openalex.org/W3207968197","https://openalex.org/W4238746485","https://openalex.org/W4289763996","https://openalex.org/W4310917384","https://openalex.org/W4324317500"],"related_works":["https://openalex.org/W3169687406","https://openalex.org/W3206613651","https://openalex.org/W4286902601","https://openalex.org/W2200000192","https://openalex.org/W2395916875","https://openalex.org/W2405673391","https://openalex.org/W3103707007","https://openalex.org/W2906178137","https://openalex.org/W2963001956","https://openalex.org/W2782093256"],"abstract_inverted_index":{"Hyperparameter":[0],"optimization":[1,152,170],"is":[2,31,61,148,172],"the":[3,7,35,42,48,76,79,83,87,96,112,115,133,137,156,159,169,181],"process":[4],"of":[5,11,44,59,75,127,132,139,166,176,199,201],"identifying":[6],"appropriate":[8,183],"hyperparameter":[9,80,128],"configuration":[10,45,134],"a":[12,20,104,146,173],"given":[13,21],"machine":[14,141],"learning":[15,22,98,109,142,227],"model":[16,39,74,91,119],"with":[17],"regard":[18],"to":[19,110,123,151],"task.":[23],"For":[24],"smaller":[25],"data":[26,36,88,195],"sets,":[27],"an":[28,71],"exhaustive":[29],"search":[30],"possible;":[32],"However,":[33],"when":[34],"size":[37],"and":[38,82,154,210],"complexity":[40],"increase,":[41],"number":[43],"evaluations":[46],"becomes":[47],"main":[49,65,97],"computational":[50],"bottleneck.":[51],"A":[52],"promising":[53],"paradigm":[54,69],"for":[55,89,102,179,187],"tackling":[56],"this":[57,68,90],"type":[58],"problem":[60],"surrogate-based":[62],"optimization.":[63],"The":[64,214],"idea":[66,175],"underlying":[67],"considers":[70],"incrementally":[72],"updated":[73],"relation":[77],"between":[78],"space":[81,135],"output":[84],"(target)":[85],"space;":[86],"are":[92],"obtained":[93],"by":[94],"evaluating":[95],"engine,":[99],"which":[100],"is,":[101],"example,":[103],"factorization":[105,184],"machine-based":[106,185],"model.":[107],"By":[108],"approximate":[111],"hyperparameter-target":[113],"relation,":[114],"surrogate":[116,147,218],"(machine":[117],"learning)":[118],"can":[120,220],"be":[121],"used":[122],"score":[124],"large":[125],"amounts":[126],"configurations,":[129],"exploring":[130],"parts":[131],"beyond":[136],"reach":[138],"direct":[140],"engine":[143,228],"evaluation.":[144],"Commonly,":[145],"selected":[149],"prior":[150],"initialization":[153],"remains":[155],"same":[157],"during":[158,168],"search.":[160],"We":[161,191],"investigated":[162],"whether":[163],"dynamic":[164],"switching":[165,219],"surrogates":[167],"itself":[171],"sensible":[174],"practical":[177],"relevance":[178],"selecting":[180],"most":[182],"models":[186],"large-scale":[188],"online":[189],"recommendation.":[190],"conducted":[192],"benchmarks":[193],"on":[194],"sets":[196],"containing":[197],"hundreds":[198],"millions":[200],"instances":[202],"against":[203],"established":[204],"baselines":[205],"such":[206],"as":[207],"Random":[208],"Forest-":[209],"Gaussian":[211],"process-based":[212],"surrogates.":[213],"results":[215],"indicate":[216],"that":[217],"offer":[221],"good":[222],"performance":[223],"while":[224],"considering":[225],"fewer":[226],"evaluations.":[229]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}