{"id":"https://openalex.org/W4403220835","doi":"https://doi.org/10.1145/3640457.3688051","title":"Sliding Window Training - Utilizing Historical Recommender Systems Data for Foundation Models","display_name":"Sliding Window Training - Utilizing Historical Recommender Systems Data for Foundation Models","publication_year":2024,"publication_date":"2024-10-08","ids":{"openalex":"https://openalex.org/W4403220835","doi":"https://doi.org/10.1145/3640457.3688051"},"language":"en","primary_location":{"id":"doi:10.1145/3640457.3688051","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3640457.3688051","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3640457.3688051","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"18th ACM Conference on Recommender Systems","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3640457.3688051","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086117342","display_name":"Swanand Joshi","orcid":null},"institutions":[{"id":"https://openalex.org/I869089601","display_name":"Netflix (United States)","ror":"https://ror.org/0197qw696","country_code":"US","type":"company","lineage":["https://openalex.org/I869089601"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Swanand Joshi","raw_affiliation_strings":["Algorithms Engineering, Netflix, USA"],"raw_orcid":"https://orcid.org/0009-0003-6316-4440","affiliations":[{"raw_affiliation_string":"Algorithms Engineering, Netflix, USA","institution_ids":["https://openalex.org/I869089601"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102946244","display_name":"Yesu Feng","orcid":"https://orcid.org/0009-0008-4459-883X"},"institutions":[{"id":"https://openalex.org/I869089601","display_name":"Netflix (United States)","ror":"https://ror.org/0197qw696","country_code":"US","type":"company","lineage":["https://openalex.org/I869089601"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yesu Feng","raw_affiliation_strings":["Algorithms Engineering, Netflix, USA"],"raw_orcid":"https://orcid.org/0009-0008-4459-883X","affiliations":[{"raw_affiliation_string":"Algorithms Engineering, Netflix, USA","institution_ids":["https://openalex.org/I869089601"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108819538","display_name":"Ko-Jen Hsiao","orcid":null},"institutions":[{"id":"https://openalex.org/I869089601","display_name":"Netflix (United States)","ror":"https://ror.org/0197qw696","country_code":"US","type":"company","lineage":["https://openalex.org/I869089601"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ko-Jen Hsiao","raw_affiliation_strings":["Algorithms Engineering, Netflix, USA"],"raw_orcid":"https://orcid.org/0009-0003-6784-7556","affiliations":[{"raw_affiliation_string":"Algorithms Engineering, Netflix, USA","institution_ids":["https://openalex.org/I869089601"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100442985","display_name":"Zhe Zhang","orcid":"https://orcid.org/0000-0002-4056-1318"},"institutions":[{"id":"https://openalex.org/I869089601","display_name":"Netflix (United States)","ror":"https://ror.org/0197qw696","country_code":"US","type":"company","lineage":["https://openalex.org/I869089601"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhe Zhang","raw_affiliation_strings":["Algorithms Engineering, Netflix, USA"],"raw_orcid":"https://orcid.org/0000-0002-4056-1318","affiliations":[{"raw_affiliation_string":"Algorithms Engineering, Netflix, USA","institution_ids":["https://openalex.org/I869089601"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014158785","display_name":"Sudarshan Lamkhede","orcid":"https://orcid.org/0000-0001-8699-3776"},"institutions":[{"id":"https://openalex.org/I869089601","display_name":"Netflix (United States)","ror":"https://ror.org/0197qw696","country_code":"US","type":"company","lineage":["https://openalex.org/I869089601"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sudarshan Lamkhede","raw_affiliation_strings":["Algorithms Engineering, Netflix, USA"],"raw_orcid":"https://orcid.org/0000-0001-8699-3776","affiliations":[{"raw_affiliation_string":"Algorithms Engineering, Netflix, USA","institution_ids":["https://openalex.org/I869089601"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I869089601"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"835","last_page":"837"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10317","display_name":"Advanced Database Systems and Queries","score":0.9900000095367432,"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"}},"topics":[{"id":"https://openalex.org/T10317","display_name":"Advanced Database Systems and Queries","score":0.9900000095367432,"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/T10215","display_name":"Semantic Web and Ontologies","score":0.9886000156402588,"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":0.972100019454956,"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/recommender-system","display_name":"Recommender system","score":0.7887569069862366},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7651115655899048},{"id":"https://openalex.org/keywords/sliding-window-protocol","display_name":"Sliding window protocol","score":0.6773003935813904},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.6545161008834839},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.6519863605499268},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.6015734672546387},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.44931477308273315},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4287707805633545},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37156209349632263},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.21664097905158997},{"id":"https://openalex.org/keywords/history","display_name":"History","score":0.05646601319313049}],"concepts":[{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.7887569069862366},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7651115655899048},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.6773003935813904},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.6545161008834839},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.6519863605499268},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6015734672546387},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.44931477308273315},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4287707805633545},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37156209349632263},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.21664097905158997},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.05646601319313049},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","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/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3640457.3688051","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3640457.3688051","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3640457.3688051","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"18th ACM Conference on Recommender Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2409.14517","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.14517","pdf_url":"https://arxiv.org/pdf/2409.14517","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:10.1145/3640457.3688051","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3640457.3688051","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3640457.3688051","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"18th ACM Conference on Recommender Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4403220835.pdf"},"referenced_works_count":4,"referenced_works":["https://openalex.org/W2984100107","https://openalex.org/W4205848394","https://openalex.org/W4376312036","https://openalex.org/W4385568029"],"related_works":["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/W4288390103","https://openalex.org/W4317039510","https://openalex.org/W3014558862"],"abstract_inverted_index":{"Long-lived":[0],"recommender":[1],"systems":[2],"(RecSys)":[3],"often":[4],"encounter":[5],"lengthy":[6],"user-item":[7],"interaction":[8],"histories":[9],"that":[10,129],"span":[11],"many":[12],"years.":[13],"To":[14],"effectively":[15],"learn":[16],"long":[17,41,93,123],"term":[18,124],"user":[19,67,94,122],"preferences,":[20],"Large":[21],"RecSys":[22,118],"foundation":[23],"models":[24],"(FM)":[25],"need":[26],"to":[27,45,69,91,116],"encode":[28],"this":[29,34,82,113],"information":[30],"in":[31,120,135,139],"pretraining.":[32],"Usually,":[33],"is":[35],"done":[36],"by":[37,61],"either":[38],"generating":[39],"a":[40,86],"enough":[42],"sequence":[43],"length":[44],"take":[46],"all":[47],"history":[48,68,95],"sequences":[49,96],"as":[50],"input":[51,58,104],"at":[52],"the":[53,66,77,102,108,117,130,136],"cost":[54],"of":[55,65,133],"large":[56],"model":[57,71,103],"dimension":[59],"or":[60],"dropping":[62],"some":[63],"parts":[64],"accommodate":[70],"size":[72],"and":[73],"latency":[74],"requirements":[75],"on":[76],"production":[78],"serving":[79],"side.":[80],"In":[81],"paper,":[83],"we":[84],"introduce":[85],"sliding":[87],"window":[88],"training":[89,98],"technique":[90,114],"incorporate":[92],"during":[97],"time":[99],"without":[100],"increasing":[101],"dimension.":[105],"We":[106,126],"show":[107,128],"quantitative":[109],"&":[110],"qualitative":[111],"improvements":[112],"brings":[115],"FM":[119],"learning":[121],"preferences.":[125],"additionally":[127],"average":[131],"quality":[132],"items":[134],"catalog":[137],"learnt":[138],"pretraining":[140],"also":[141],"improves.":[142]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2024-10-09T00:00:00"}
