{"id":"https://openalex.org/W2972779668","doi":"https://doi.org/10.1145/3397271.3401113","title":"Distributed Equivalent Substitution Training for Large-Scale Recommender Systems","display_name":"Distributed Equivalent Substitution Training for Large-Scale Recommender Systems","publication_year":2020,"publication_date":"2020-07-25","ids":{"openalex":"https://openalex.org/W2972779668","doi":"https://doi.org/10.1145/3397271.3401113","mag":"2972779668"},"language":"en","primary_location":{"id":"doi:10.1145/3397271.3401113","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397271.3401113","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1909.04823","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Haidong Rong","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haidong Rong","raw_affiliation_strings":["Tencent Inc., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc., Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yangzihao Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yangzihao Wang","raw_affiliation_strings":["Tencent Inc., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc., Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Feihu Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feihu Zhou","raw_affiliation_strings":["Tencent Inc., Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc., Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Junjie Zhai","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junjie Zhai","raw_affiliation_strings":["Tencent Inc., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc., Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Haiyang Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haiyang Wu","raw_affiliation_strings":["Tencent Inc., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc., Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Rui Lan","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Lan","raw_affiliation_strings":["Tencent Inc., Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc., Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Fan Li","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Li","raw_affiliation_strings":["Tencent Inc., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc., Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Han Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Han Zhang","raw_affiliation_strings":["Tencent Inc., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc., Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yuekui Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuekui Yang","raw_affiliation_strings":["Tencent Inc., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc., Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhenyu Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenyu Guo","raw_affiliation_strings":["Tencent Inc., Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc., Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":null,"display_name":"Di Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Di Wang","raw_affiliation_strings":["Tencent Inc., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc., Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":11,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.9494,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.89179819,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"911","last_page":"920"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9994999766349792,"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":0.9994999766349792,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9958000183105469,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9954000115394592,"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/recommender-system","display_name":"Recommender system","score":0.8668000102043152},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.7024000287055969},{"id":"https://openalex.org/keywords/substitution","display_name":"Substitution (logic)","score":0.5813999772071838},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5307000279426575},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.5175999999046326},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.32989999651908875}],"concepts":[{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.8668000102043152},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8198000192642212},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.7024000287055969},{"id":"https://openalex.org/C2778220771","wikidata":"https://www.wikidata.org/wiki/Q1522579","display_name":"Substitution (logic)","level":2,"score":0.5813999772071838},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5307000279426575},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.5175999999046326},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4884999990463257},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39169999957084656},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.32989999651908875},{"id":"https://openalex.org/C101765175","wikidata":"https://www.wikidata.org/wiki/Q577764","display_name":"Communications system","level":2,"score":0.3043999969959259},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C3018263672","wikidata":"https://www.wikidata.org/wiki/Q1296251","display_name":"Efficient algorithm","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.25369998812675476},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2508000135421753}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3397271.3401113","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397271.3401113","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1909.04823","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1909.04823","pdf_url":"https://arxiv.org/pdf/1909.04823","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:1909.04823","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1909.04823","pdf_url":"https://arxiv.org/pdf/1909.04823","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":12,"referenced_works":["https://openalex.org/W2074694452","https://openalex.org/W2083842231","https://openalex.org/W2090883204","https://openalex.org/W2255575265","https://openalex.org/W2475334473","https://openalex.org/W2494566063","https://openalex.org/W2512971201","https://openalex.org/W2604662567","https://openalex.org/W2723293840","https://openalex.org/W2785452945","https://openalex.org/W2793768763","https://openalex.org/W3101704389"],"related_works":[],"abstract_inverted_index":{"We":[0,100],"present":[1],"Distributed":[2],"Equivalent":[3],"Substitution":[4],"(DES)":[5],"training,":[6],"a":[7],"novel":[8],"distributed":[9],"training":[10,24,39,86,104,121],"framework":[11],"for":[12,29],"large-scale":[13,26,88],"recommender":[14,42,136],"systems":[15,43],"with":[16,60],"dynamic":[17],"sparse":[18,74],"features.":[19],"DES":[20,50,94,103],"introduces":[21],"fully":[22],"synchronous":[23,85],"to":[25,81,125,133],"recommendation":[27],"system":[28],"the":[30,38,57,61,72,78,82,118],"first":[31],"time":[32],"by":[33,55],"reducing":[34],"communication,":[35],"thus":[36],"making":[37],"of":[40,70,84,109],"commercial":[41],"converge":[44],"faster":[45],"and":[46,65,129],"reach":[47],"better":[48],"CTR.":[49],"requires":[51],"much":[52],"less":[53],"communication":[54,127],"substituting":[56],"weights-rich":[58],"operators":[59],"computationally":[62],"equivalent":[63],"sub-operators":[64],"aggregating":[66],"partial":[67],"results":[68],"instead":[69],"transmitting":[71],"huge":[73],"weights":[75],"directly":[76],"through":[77],"network.":[79],"Due":[80],"use":[83],"on":[87,105],"Deep":[89],"Learning":[90],"Recommendation":[91],"Models":[92],"(DLRMs),":[93],"achieves":[95],"higher":[96,130],"AUC(Area":[97],"Under":[98],"ROC).":[99],"successfully":[101],"apply":[102],"multiple":[106],"popular":[107],"DLRMs":[108],"industrial":[110],"scenarios.":[111],"Experiments":[112],"show":[113],"that":[114],"our":[115],"implementation":[116],"outperforms":[117],"state-of-the-art":[119],"PS-based":[120,135],"framework,":[122],"achieving":[123],"up":[124],"68.7%":[126],"savings":[128],"throughput":[131],"compared":[132],"other":[134],"systems.":[137]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2019-09-19T00:00:00"}