{"id":"https://openalex.org/W2954698196","doi":"https://doi.org/10.1145/3331184.3331196","title":"Bayesian Personalized Feature Interaction Selection for Factorization Machines","display_name":"Bayesian Personalized Feature Interaction Selection for Factorization Machines","publication_year":2019,"publication_date":"2019-07-18","ids":{"openalex":"https://openalex.org/W2954698196","doi":"https://doi.org/10.1145/3331184.3331196","mag":"2954698196"},"language":"en","primary_location":{"id":"doi:10.1145/3331184.3331196","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3331184.3331196","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100405121","display_name":"Yifan Chen","orcid":"https://orcid.org/0000-0001-6450-3617"},"institutions":[{"id":"https://openalex.org/I4210135670","display_name":"Amsterdam University of the Arts","ror":"https://ror.org/04dde1554","country_code":"NL","type":"education","lineage":["https://openalex.org/I4210135670"]},{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Yifan Chen","raw_affiliation_strings":["University of Amsterdam, Amsterdam, Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Amsterdam, Amsterdam, Netherlands","institution_ids":["https://openalex.org/I4210135670","https://openalex.org/I887064364"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046700486","display_name":"Pengjie Ren","orcid":"https://orcid.org/0000-0003-2964-6422"},"institutions":[{"id":"https://openalex.org/I4210135670","display_name":"Amsterdam University of the Arts","ror":"https://ror.org/04dde1554","country_code":"NL","type":"education","lineage":["https://openalex.org/I4210135670"]},{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Pengjie Ren","raw_affiliation_strings":["University of Amsterdam, Amsterdam, Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Amsterdam, Amsterdam, Netherlands","institution_ids":["https://openalex.org/I4210135670","https://openalex.org/I887064364"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100322887","display_name":"Yang Wang","orcid":"https://orcid.org/0000-0003-1029-9280"},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Wang","raw_affiliation_strings":["Hefei University of Technology, Hefei, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hefei University of Technology, Hefei, China","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031439294","display_name":"Maarten de Rijke","orcid":"https://orcid.org/0000-0002-1086-0202"},"institutions":[{"id":"https://openalex.org/I4210135670","display_name":"Amsterdam University of the Arts","ror":"https://ror.org/04dde1554","country_code":"NL","type":"education","lineage":["https://openalex.org/I4210135670"]},{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Maarten de Rijke","raw_affiliation_strings":["University of Amsterdam, Amsterdam, Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Amsterdam, Amsterdam, Netherlands","institution_ids":["https://openalex.org/I4210135670","https://openalex.org/I887064364"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.39,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.95402481,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"665","last_page":"674"},"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/T10028","display_name":"Topic Modeling","score":0.984499990940094,"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/T11309","display_name":"Music and Audio Processing","score":0.9764999747276306,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/feature-selection","display_name":"Feature selection","score":0.794305682182312},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7732840776443481},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6869757175445557},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6642377376556396},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.6257999539375305},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6022301912307739},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5734596252441406},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.48863083124160767},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.484565407037735},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.44469594955444336},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.44155120849609375},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.44114676117897034},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3582800626754761}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.794305682182312},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7732840776443481},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6869757175445557},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6642377376556396},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.6257999539375305},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6022301912307739},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5734596252441406},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.48863083124160767},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.484565407037735},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.44469594955444336},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.44155120849609375},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.44114676117897034},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3582800626754761},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/3331184.3331196","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3331184.3331196","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:dare.uva.nl:openaire_cris_publications/8940af0b-248a-4c71-9105-36e2259ab726","is_oa":false,"landing_page_url":"https://hdl.handle.net/11245.1/8940af0b-248a-4c71-9105-36e2259ab726","pdf_url":null,"source":{"id":"https://openalex.org/S4306400088","display_name":"UvA-DARE (University of Amsterdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887064364","host_organization_name":"University of Amsterdam","host_organization_lineage":["https://openalex.org/I887064364"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Chen, Y, Ren, P, Wang, Y & de Rijke, M 2019, Bayesian personalized feature interaction selection for factorization machines. in SIGIR '19 : proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval : July 21-25, 2019, Paris, France. New York, New York, pp. 665-674, 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, 21/07/19. https://doi.org/10.1145/3331184.3331196","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:dare.uva.nl:publications/8940af0b-248a-4c71-9105-36e2259ab726","is_oa":false,"landing_page_url":"https://handle.uba.uva.nl/personal/pure/en/publications/bayesian-personalized-feature-interaction-selection-for-factorization-machines(8940af0b-248a-4c71-9105-36e2259ab726).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400088","display_name":"UvA-DARE (University of Amsterdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887064364","host_organization_name":"University of Amsterdam","host_organization_lineage":["https://openalex.org/I887064364"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Chen, Y, Ren, P, Wang, Y & de Rijke, M 2019, Bayesian personalized feature interaction selection for factorization machines. in SIGIR '19 : proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval : July 21-25, 2019, Paris, France. New York, New York, pp. 665-674, 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, 21/07/19. https://doi.org/10.1145/3331184.3331196","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:uvapub:oai:dare.uva.nl:publications/8940af0b-248a-4c71-9105-36e2259ab726","is_oa":false,"landing_page_url":"https://dare.uva.nl/personal/pure/en/publications/bayesian-personalized-feature-interaction-selection-for-factorization-machines(8940af0b-248a-4c71-9105-36e2259ab726).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"SIGIR '19: proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval : July 21-25, 2019, Paris, France, 665 - 674","raw_type":"info:eu-repo/semantics/conferencepaper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W1528905581","https://openalex.org/W1546447935","https://openalex.org/W1572158434","https://openalex.org/W1603353793","https://openalex.org/W1698155719","https://openalex.org/W1959608418","https://openalex.org/W1964509623","https://openalex.org/W1973616272","https://openalex.org/W1978049851","https://openalex.org/W1987431925","https://openalex.org/W1992554260","https://openalex.org/W1994389483","https://openalex.org/W1999974018","https://openalex.org/W2002834872","https://openalex.org/W2024082504","https://openalex.org/W2036536766","https://openalex.org/W2048508267","https://openalex.org/W2051090063","https://openalex.org/W2077653218","https://openalex.org/W2089349245","https://openalex.org/W2094286023","https://openalex.org/W2108920354","https://openalex.org/W2112519400","https://openalex.org/W2122090912","https://openalex.org/W2126497681","https://openalex.org/W2128629010","https://openalex.org/W2135046866","https://openalex.org/W2138019504","https://openalex.org/W2140310134","https://openalex.org/W2152049508","https://openalex.org/W2155676512","https://openalex.org/W2159191733","https://openalex.org/W2219888463","https://openalex.org/W2253995343","https://openalex.org/W2295739661","https://openalex.org/W2475334473","https://openalex.org/W2509235963","https://openalex.org/W2517540742","https://openalex.org/W2518344269","https://openalex.org/W2550070141","https://openalex.org/W2572651649","https://openalex.org/W2583200575","https://openalex.org/W2584966963","https://openalex.org/W2592438485","https://openalex.org/W2740130602","https://openalex.org/W2743159750","https://openalex.org/W2745024368","https://openalex.org/W2782494302","https://openalex.org/W2788685622","https://openalex.org/W2791451272","https://openalex.org/W2800857198","https://openalex.org/W2892821876","https://openalex.org/W2896691185","https://openalex.org/W2952449615","https://openalex.org/W2963004721","https://openalex.org/W2963085847","https://openalex.org/W2963323306","https://openalex.org/W2964044287","https://openalex.org/W2964052347","https://openalex.org/W2964121744","https://openalex.org/W2964290417","https://openalex.org/W4234541916","https://openalex.org/W4251597219"],"related_works":["https://openalex.org/W2772628444","https://openalex.org/W2735929803","https://openalex.org/W4220714703","https://openalex.org/W1484355083","https://openalex.org/W3008845055","https://openalex.org/W2098758514","https://openalex.org/W4376854386","https://openalex.org/W2202724490","https://openalex.org/W2508671622","https://openalex.org/W2556532874"],"abstract_inverted_index":{"Factorization":[0],"Machines":[1],"(FMs)":[2],"are":[3,13,38],"widely":[4],"used":[5],"for":[6,90,138,208],"feature-based":[7],"collaborative":[8,91],"filtering":[9,68,92],"tasks,":[10],"as":[11],"they":[12,74],"very":[14],"effective":[15],"at":[16,67],"modeling":[17],"feature":[18,26,36,42,71,82],"interactions.":[19,72],"Existing":[20],"FM-based":[21],"methods":[22,53],"usually":[23],"take":[24],"all":[25,35,77],"interactions":[27,37,43,211],"into":[28],"account,":[29],"which":[30,84,154],"is":[31,85],"unreasonable":[32],"because":[33,63,172],"not":[34,86],"helpful:":[39],"incorporating":[40],"useless":[41,70],"will":[44],"introduce":[45,128],"noise":[46],"and":[47,101,135,147,212],"degrade":[48],"the":[49,80,119,149,169,189,204,214],"recommendation":[50,215],"performance.":[51,216],"Recently,":[52],"that":[54,76],"perform":[55],"Feature":[56,104,113],"Interaction":[57,105,114],"Selection":[58,106,115,122],"(FIS)":[59],"have":[60],"attracted":[61],"attention":[62],"of":[64,181,206],"their":[65],"effectiveness":[66,205],"out":[69,194],"However,":[73],"assume":[75],"users":[78],"share":[79],"same":[81],"interactions,":[83],"necessarily":[87],"true,":[88],"especially":[89],"tasks.":[93],"In":[94],"this":[95,99],"work,":[96],"we":[97,126,141,183],"address":[98],"issue":[100],"study":[102],"Personalized":[103,112],"(P-FIS)":[107],"by":[108,158],"proposing":[109],"a":[110,143],"Bayesian":[111,120,144],"(BP-FIS)":[116],"mechanism":[117],"under":[118,188],"Variable":[121],"(BVS)":[123],"theory.":[124],"Specifically,":[125],"first":[127],"interaction":[129],"selection":[130],"variables":[131],"with":[132,178],"hereditary":[133],"spike":[134],"slab":[136],"priors":[137],"P-FIS.":[139],"Then,":[140],"form":[142],"generative":[145],"model":[146],"derive":[148],"Evidence":[150],"Lower":[151],"Bound":[152],"(ELBO),":[153],"can":[155,174],"be":[156,175],"optimized":[157],"an":[159],"efficient":[160],"Stochastic":[161],"Gradient":[162],"Variational":[163],"Bayes":[164],"(SGVB)":[165],"method":[166],"to":[167],"learn":[168],"parameters.":[170],"Finally,":[171],"BP-FIS":[173,207],"seamlessly":[176],"integrated":[177],"different":[179],"variants":[180,187],"FMs,":[182],"implement":[184],"two":[185],"FM":[186],"proposed":[190],"BP-FIS.":[191],"We":[192],"carry":[193],"experiments":[195],"on":[196],"three":[197],"benchmark":[198],"datasets.":[199],"The":[200],"empirical":[201],"results":[202],"demonstrate":[203],"selecting":[209],"personalized":[210],"improving":[213]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":6}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
