{"id":"https://openalex.org/W3012593211","doi":"https://doi.org/10.1145/3366423.3380283","title":"Clustering and Constructing User Coresets to Accelerate Large-scale Top-K Recommender Systems","display_name":"Clustering and Constructing User Coresets to Accelerate Large-scale Top-K Recommender Systems","publication_year":2020,"publication_date":"2020-04-20","ids":{"openalex":"https://openalex.org/W3012593211","doi":"https://doi.org/10.1145/3366423.3380283","mag":"3012593211"},"language":"en","primary_location":{"id":"doi:10.1145/3366423.3380283","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380283","pdf_url":null,"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 Web Conference 2020","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3366423.3380283","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5048749901","display_name":"Jyun\u2010Yu Jiang","orcid":"https://orcid.org/0000-0002-1753-8099"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jyun-Yu Jiang","raw_affiliation_strings":["University of California Los Angeles"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California Los Angeles","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075080451","display_name":"Patrick H. Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Patrick H. Chen","raw_affiliation_strings":["University of California Los Angeles"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California Los Angeles","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010841999","display_name":"Cho\u2010Jui Hsieh","orcid":"https://orcid.org/0000-0002-3520-9627"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cho-Jui Hsieh","raw_affiliation_strings":["University of California Los Angeles"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California Los Angeles","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100392089","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0002-8180-2886"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["University of California Los Angeles"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California Los Angeles","institution_ids":["https://openalex.org/I161318765"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I161318765"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2177","last_page":"2187"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.989799976348877,"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/T11478","display_name":"Caching and Content Delivery","score":0.9887999892234802,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8086262941360474},{"id":"https://openalex.org/keywords/movielens","display_name":"MovieLens","score":0.7280205488204956},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.710788905620575},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6486993432044983},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.6302027702331543},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.5442885756492615},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4680347740650177},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.4311109781265259},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.426597535610199},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.4149588346481323},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.4148268699645996},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3120429515838623},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09587505459785461}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8086262941360474},{"id":"https://openalex.org/C2776156558","wikidata":"https://www.wikidata.org/wiki/Q4353746","display_name":"MovieLens","level":4,"score":0.7280205488204956},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.710788905620575},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6486993432044983},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.6302027702331543},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.5442885756492615},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4680347740650177},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.4311109781265259},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.426597535610199},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.4149588346481323},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.4148268699645996},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3120429515838623},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09587505459785461},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"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":1,"locations":[{"id":"doi:10.1145/3366423.3380283","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380283","pdf_url":null,"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 Web Conference 2020","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3366423.3380283","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380283","pdf_url":null,"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 Web Conference 2020","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W131619556","https://openalex.org/W1757785627","https://openalex.org/W1974627246","https://openalex.org/W2054141820","https://openalex.org/W2086179657","https://openalex.org/W2090398718","https://openalex.org/W2100026763","https://openalex.org/W2107569009","https://openalex.org/W2116684163","https://openalex.org/W2140310134","https://openalex.org/W2142144955","https://openalex.org/W2146682077","https://openalex.org/W2147717514","https://openalex.org/W2149381887","https://openalex.org/W2169054943","https://openalex.org/W2219888463","https://openalex.org/W2231488453","https://openalex.org/W2253654881","https://openalex.org/W2294540049","https://openalex.org/W2416951723","https://openalex.org/W2480140084","https://openalex.org/W2560674852","https://openalex.org/W2750846973","https://openalex.org/W2808168001","https://openalex.org/W2898744876","https://openalex.org/W2904671658","https://openalex.org/W2921521223","https://openalex.org/W2951573205","https://openalex.org/W2962818688","https://openalex.org/W2962863202","https://openalex.org/W2963034893","https://openalex.org/W2963276349","https://openalex.org/W2963469388","https://openalex.org/W2965212020","https://openalex.org/W3101974065"],"related_works":["https://openalex.org/W2355698112","https://openalex.org/W2022984797","https://openalex.org/W4394818607","https://openalex.org/W2986679525","https://openalex.org/W2797500822","https://openalex.org/W4299358966","https://openalex.org/W2794458286","https://openalex.org/W4205822456","https://openalex.org/W2537367858","https://openalex.org/W4288082747"],"abstract_inverted_index":{"Top-K":[0],"recommender":[1],"systems":[2],"aim":[3],"to":[4,39,96,114,118,154,172,184,262],"generate":[5],"few":[6],"but":[7],"satisfactory":[8],"personalized":[9,226],"recommendations":[10,42,102,189,253],"for":[11,19,24,67,88,92,140,180,190,222,251],"various":[12],"practical":[13],"applications,":[14],"such":[15],"as":[16,43,45],"item":[17,64,223],"recommendation":[18,224],"e-commerce":[20],"and":[21,32,50,71,90,225],"link":[22,227],"prediction":[23],"social":[25],"networks.":[26],"However,":[27],"the":[28,46,63,72,98,163,194,202,206,209,263,267],"numbers":[29],"of":[30,100,125,137,177,208],"users":[31,75,120,128],"items":[33,179],"can":[34,199,247,270],"be":[35,185],"enormous,":[36],"thereby":[37],"leading":[38],"myriad":[40],"potential":[41],"well":[44],"bottleneck":[47],"in":[48,162,193,254],"evaluating":[49],"ranking":[51,65],"all":[52],"possibilities.":[53],"Existing":[54],"Maximum":[55],"Inner":[56],"Product":[57],"Search":[58],"(MIPS)":[59],"based":[60,103],"methods":[61],"treat":[62],"problem":[66],"each":[68,124,141,181,191],"user":[69,116,155,192],"independently":[70],"relationship":[73],"between":[74],"has":[76],"not":[77],"been":[78],"explored.":[79],"In":[80],"this":[81],"paper,":[82],"we":[83],"propose":[84],"a":[85,135,146,150,174,255],"novel":[86],"model":[87],"clustering":[89],"navigating":[91],"top-K":[93,101],"recommenders":[94],"(CANTOR)":[95],"expedite":[97],"computation":[99,203],"on":[104,215],"latent":[105,156],"factor":[106],"models.":[107],"A":[108],"clustering-based":[109],"framework":[110],"is":[111,169],"first":[112],"presented":[113],"leverage":[115],"relationships":[117],"partition":[119],"into":[121],"affinity":[122,142,182,195],"groups,":[123],"which":[126],"contains":[127],"with":[129,149,241,258,275],"similar":[130],"preferences.":[131],"CANTOR":[132,234,246],"then":[133,170],"derives":[134],"coreset":[136],"representative":[138,160],"vectors":[139,161],"group":[143,183],"by":[144],"constructing":[145],"set":[147,176],"cover":[148],"theoretically":[151],"guaranteed":[152],"difference":[153],"vectors.":[157],"Using":[158],"these":[159],"coreset,":[164],"approximate":[165],"nearest":[166],"neighbor":[167],"search":[168],"applied":[171],"obtain":[173,272],"small":[175],"candidate":[178],"used":[186],"when":[187],"computing":[188],"group.":[196],"This":[197],"approach":[198],"significantly":[200,235],"reduce":[201],"without":[204],"compromising":[205],"quality":[207],"recommendations.":[210],"Extensive":[211],"experiments":[212],"are":[213],"conducted":[214],"six":[216],"publicly":[217],"available":[218],"large-scale":[219],"real-world":[220],"datasets":[221],"prediction.":[228],"The":[229],"experimental":[230],"results":[231],"demonstrate":[232],"that":[233],"speeds":[236],"up":[237],"matrix":[238],"factorization":[239],"models":[240],"high":[242],"precision.":[243],"For":[244],"instance,":[245],"achieve":[248],"355.1x":[249],"speedup":[250,274],"inferring":[252],"million-user":[256],"network":[257],"99.5%":[259],"[email":[260,277],"protected]":[261,278],"original":[264],"system":[265],"while":[266],"state-of-the-art":[268],"method":[269],"only":[271],"93.7x":[273],"99.0%":[276]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":9}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
