{"id":"https://openalex.org/W2788490371","doi":"https://doi.org/10.1145/3178876.3186040","title":"Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising","display_name":"Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2788490371","doi":"https://doi.org/10.1145/3178876.3186040","mag":"2788490371"},"language":"en","primary_location":{"id":"doi:10.1145/3178876.3186040","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3178876.3186040","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3186040&type=pdf","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 2018 World Wide Web Conference on World Wide Web - WWW '18","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"http://dl.acm.org/ft_gateway.cfm?id=3186040&type=pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Junwei Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Junwei Pan","raw_affiliation_strings":["Oath Inc., Sunnyvale, CA, USA"],"affiliations":[{"raw_affiliation_string":"Oath Inc., Sunnyvale, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jian Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jian Xu","raw_affiliation_strings":["TouchPal Inc., Shanghai, China"],"affiliations":[{"raw_affiliation_string":"TouchPal Inc., Shanghai, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Alfonso Lobos Ruiz","orcid":null},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alfonso Lobos Ruiz","raw_affiliation_strings":["University of California, Berkeley, Berkeley, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Berkeley, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wenliang Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenliang Zhao","raw_affiliation_strings":["Oath Inc., Sunnyvale, CA, USA"],"affiliations":[{"raw_affiliation_string":"Oath Inc., Sunnyvale, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Shengjun Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shengjun Pan","raw_affiliation_strings":["Oath Inc., Sunnyvale, CA, USA"],"affiliations":[{"raw_affiliation_string":"Oath Inc., Sunnyvale, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yu 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":"Yu 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":null,"display_name":"Quan Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Quan Lu","raw_affiliation_strings":["Ablibaba Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ablibaba Group, Hangzhou, China","institution_ids":["https://openalex.org/I45928872"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":22.9936,"has_fulltext":true,"cited_by_count":168,"citation_normalized_percentile":{"value":0.99410736,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1349","last_page":"1357"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9991000294685364,"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":0.9991000294685364,"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":0.9848999977111816,"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":0.9624000191688538,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.8695999979972839},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.6506999731063843},{"id":"https://openalex.org/keywords/lift","display_name":"Lift (data mining)","score":0.6183000206947327},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5889999866485596},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5568000078201294},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4912000000476837},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.3982999920845032},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.3874000012874603}],"concepts":[{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.8695999979972839},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7009999752044678},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.6506999731063843},{"id":"https://openalex.org/C139002025","wikidata":"https://www.wikidata.org/wiki/Q3001212","display_name":"Lift (data mining)","level":2,"score":0.6183000206947327},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5889999866485596},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5774000287055969},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5568000078201294},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49729999899864197},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4912000000476837},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4108000099658966},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3982999920845032},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.3874000012874603},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34450000524520874},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.32420000433921814},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.25850000977516174}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3178876.3186040","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3178876.3186040","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3186040&type=pdf","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 2018 World Wide Web Conference on World Wide Web - WWW '18","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1806.03514","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1806.03514","pdf_url":"https://arxiv.org/pdf/1806.03514","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":"doi:10.1145/3178876.3186040","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3178876.3186040","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3186040&type=pdf","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 2018 World Wide Web Conference on World Wide Web - WWW '18","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2788490371.pdf","grobid_xml":"https://content.openalex.org/works/W2788490371.grobid-xml"},"referenced_works_count":10,"referenced_works":["https://openalex.org/W1985759455","https://openalex.org/W1992554260","https://openalex.org/W2054141820","https://openalex.org/W2074694452","https://openalex.org/W2090883204","https://openalex.org/W2094286023","https://openalex.org/W2295739661","https://openalex.org/W2443960221","https://openalex.org/W2475334473","https://openalex.org/W2517540742"],"related_works":[],"abstract_inverted_index":{"Click-through":[0],"rate":[1],"(CTR)":[2],"prediction":[3,17,73,137,169],"is":[4,26,44,86,97],"a":[5,123],"critical":[6],"task":[7],"in":[8,15,84,87,99,122],"online":[9],"display":[10],"advertising.":[11],"The":[12],"data":[13,43,170],"involved":[14],"CTR":[16,72,168],"are":[18],"typically":[19],"multi-field":[20],"categorical":[21,27],"data,":[22],"i.e.,":[23],"every":[24],"feature":[25,91,117],"and":[28,32,159],"belongs":[29],"to":[30,113],"one":[31,34,48],"only":[33,140],"field.":[35],"One":[36],"of":[37,41,82,90,146,153],"the":[38,67,80,88,100,115,150],"interesting":[39],"characteristics":[40],"such":[42,77],"that":[45,132],"features":[46,54],"from":[47,55],"field":[49,94],"often":[50],"interact":[51],"differently":[52],"with":[53,139],"different":[56,116,120],"other":[57],"fields.":[58],"Recently,":[59],"Field-aware":[60],"Factorization":[61,110],"Machines":[62,111],"(FFMs)":[63],"have":[64],"been":[65],"among":[66],"best":[68],"performing":[69],"models":[70],"for":[71],"by":[74],"explicitly":[75],"modeling":[76],"difference.":[78],"However,":[79],"number":[81,92,152],"parameters":[83,145],"FFMs":[85,164],"order":[89],"times":[93],"number,":[95],"which":[96],"unacceptable":[98],"real-world":[101],"production":[102],"systems.":[103],"In":[104],"this":[105],"paper,":[106],"we":[107],"propose":[108],"Field-weighted":[109],"(FwFMs)":[112],"model":[114],"interactions":[118],"between":[119],"fields":[121],"much":[124],"more":[125],"memory-efficient":[126],"way.":[127],"Our":[128],"experimental":[129],"evaluations":[130],"show":[131],"FwFMs":[133,155],"can":[134,156],"achieve":[135],"competitive":[136],"performance":[138],"as":[141,143],"few":[142],"4%":[144],"FFMs.":[147],"When":[148],"using":[149],"same":[151],"parameters,":[154],"bring":[157],"0.92%":[158],"0.47%":[160],"AUC":[161],"lift":[162],"over":[163],"on":[165],"two":[166],"real":[167],"sets.":[171]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":34},{"year":2024,"cited_by_count":22},{"year":2023,"cited_by_count":30},{"year":2022,"cited_by_count":23},{"year":2021,"cited_by_count":36},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2018-03-06T00:00:00"}
