{"id":"https://openalex.org/W2037518952","doi":"https://doi.org/10.1109/bigdata.2013.6691571","title":"GPU accelerated item-based collaborative filtering for big-data applications","display_name":"GPU accelerated item-based collaborative filtering for big-data applications","publication_year":2013,"publication_date":"2013-10-01","ids":{"openalex":"https://openalex.org/W2037518952","doi":"https://doi.org/10.1109/bigdata.2013.6691571","mag":"2037518952"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2013.6691571","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2013.6691571","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Conference on Big Data","raw_type":"proceedings-article"},"type":"article","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/A5085762598","display_name":"Chandima Hewa Nadungodage","orcid":null},"institutions":[{"id":"https://openalex.org/I55769427","display_name":"Indiana University \u2013 Purdue University Indianapolis","ror":"https://ror.org/05gxnyn08","country_code":"US","type":"education","lineage":["https://openalex.org/I55769427","https://openalex.org/I592451"]},{"id":"https://openalex.org/I135191193","display_name":"University of Indianapolis","ror":"https://ror.org/052133d12","country_code":"US","type":"education","lineage":["https://openalex.org/I135191193"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chandima Hewa Nadungodage","raw_affiliation_strings":["Department of Computer & Information Science, Purdue School of Science, Indianapolis, USA","[Purdue Sch. of Sci., Dept. of Comput. & Inf. Sci., IUPUI, Indianapolis, IN, USA]"],"affiliations":[{"raw_affiliation_string":"Department of Computer & Information Science, Purdue School of Science, Indianapolis, USA","institution_ids":["https://openalex.org/I135191193","https://openalex.org/I55769427"]},{"raw_affiliation_string":"[Purdue Sch. of Sci., Dept. of Comput. & Inf. Sci., IUPUI, Indianapolis, IN, USA]","institution_ids":["https://openalex.org/I55769427"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111942905","display_name":"Yuni Xia","orcid":null},"institutions":[{"id":"https://openalex.org/I55769427","display_name":"Indiana University \u2013 Purdue University Indianapolis","ror":"https://ror.org/05gxnyn08","country_code":"US","type":"education","lineage":["https://openalex.org/I55769427","https://openalex.org/I592451"]},{"id":"https://openalex.org/I135191193","display_name":"University of Indianapolis","ror":"https://ror.org/052133d12","country_code":"US","type":"education","lineage":["https://openalex.org/I135191193"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuni Xia","raw_affiliation_strings":["Department of Computer & Information Science, Purdue School of Science, Indianapolis, USA","[Purdue Sch. of Sci., Dept. of Comput. & Inf. Sci., IUPUI, Indianapolis, IN, USA]"],"affiliations":[{"raw_affiliation_string":"Department of Computer & Information Science, Purdue School of Science, Indianapolis, USA","institution_ids":["https://openalex.org/I135191193","https://openalex.org/I55769427"]},{"raw_affiliation_string":"[Purdue Sch. of Sci., Dept. of Comput. & Inf. Sci., IUPUI, Indianapolis, IN, USA]","institution_ids":["https://openalex.org/I55769427"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100762136","display_name":"John J. Lee","orcid":"https://orcid.org/0000-0002-5335-9071"},"institutions":[{"id":"https://openalex.org/I55769427","display_name":"Indiana University \u2013 Purdue University Indianapolis","ror":"https://ror.org/05gxnyn08","country_code":"US","type":"education","lineage":["https://openalex.org/I55769427","https://openalex.org/I592451"]},{"id":"https://openalex.org/I135191193","display_name":"University of Indianapolis","ror":"https://ror.org/052133d12","country_code":"US","type":"education","lineage":["https://openalex.org/I135191193"]},{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John Jaehwan Lee","raw_affiliation_strings":["Department of Electrical & Computer Engineering, Purdue School of Engineering & Technology, Indianapolis, USA","Purdue Sch. of Eng. & Technol., Dept. of Electr. & Comput. Eng., IUPUI, Indianapolis, IN, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical & Computer Engineering, Purdue School of Engineering & Technology, Indianapolis, USA","institution_ids":["https://openalex.org/I135191193"]},{"raw_affiliation_string":"Purdue Sch. of Eng. & Technol., Dept. of Electr. & Comput. Eng., IUPUI, Indianapolis, IN, USA","institution_ids":["https://openalex.org/I55769427","https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021046098","display_name":"Myungcheol Lee","orcid":"https://orcid.org/0000-0002-1251-1727"},"institutions":[{"id":"https://openalex.org/I142401562","display_name":"Electronics and Telecommunications Research Institute","ror":"https://ror.org/03ysstz10","country_code":"KR","type":"facility","lineage":["https://openalex.org/I142401562","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Myungcheol Lee","raw_affiliation_strings":["Big-Data Software Platform Research Department, Software Research Laboratory Electronics & Telecommunications Research Institute, Korea","Res. Dept., Electron. & Telecommun., Daejeon, South Korea"],"affiliations":[{"raw_affiliation_string":"Big-Data Software Platform Research Department, Software Research Laboratory Electronics & Telecommunications Research Institute, Korea","institution_ids":["https://openalex.org/I142401562"]},{"raw_affiliation_string":"Res. Dept., Electron. & Telecommun., Daejeon, South Korea","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023359616","display_name":"Choon Seo Park","orcid":null},"institutions":[{"id":"https://openalex.org/I142401562","display_name":"Electronics and Telecommunications Research Institute","ror":"https://ror.org/03ysstz10","country_code":"KR","type":"facility","lineage":["https://openalex.org/I142401562","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Choon Seo Park","raw_affiliation_strings":["Big-Data Software Platform Research Department, Software Research Laboratory Electronics & Telecommunications Research Institute, Korea","Res. Dept., Electron. & Telecommun., Daejeon, South Korea"],"affiliations":[{"raw_affiliation_string":"Big-Data Software Platform Research Department, Software Research Laboratory Electronics & Telecommunications Research Institute, Korea","institution_ids":["https://openalex.org/I142401562"]},{"raw_affiliation_string":"Res. Dept., Electron. & Telecommun., Daejeon, South Korea","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5085762598"],"corresponding_institution_ids":["https://openalex.org/I135191193","https://openalex.org/I55769427"],"apc_list":null,"apc_paid":null,"fwci":10.5163,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.97838605,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"175","last_page":"180"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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/T12384","display_name":"Customer churn and segmentation","score":0.982699990272522,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9767000079154968,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.876496434211731},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.7667068243026733},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.6805996298789978},{"id":"https://openalex.org/keywords/implementation","display_name":"Implementation","score":0.652234673500061},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.6166418790817261},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.6145399212837219},{"id":"https://openalex.org/keywords/cuda","display_name":"CUDA","score":0.5937594771385193},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.46144241094589233},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.359289288520813},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.32655221223831177},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.25280678272247314},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.20391583442687988},{"id":"https://openalex.org/keywords/software-engineering","display_name":"Software engineering","score":0.10548624396324158},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.09322869777679443}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.876496434211731},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.7667068243026733},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.6805996298789978},{"id":"https://openalex.org/C26713055","wikidata":"https://www.wikidata.org/wiki/Q245962","display_name":"Implementation","level":2,"score":0.652234673500061},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.6166418790817261},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6145399212837219},{"id":"https://openalex.org/C2778119891","wikidata":"https://www.wikidata.org/wiki/Q477690","display_name":"CUDA","level":2,"score":0.5937594771385193},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.46144241094589233},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.359289288520813},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.32655221223831177},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25280678272247314},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.20391583442687988},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.10548624396324158},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.09322869777679443}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2013.6691571","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2013.6691571","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Conference on Big Data","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5099999904632568}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W191714665","https://openalex.org/W1978682376","https://openalex.org/W2042281163","https://openalex.org/W2071039340","https://openalex.org/W2076822917","https://openalex.org/W2106692105","https://openalex.org/W2115338030","https://openalex.org/W2124592110","https://openalex.org/W2135598826","https://openalex.org/W2142144955","https://openalex.org/W2159094788","https://openalex.org/W6607861299"],"related_works":["https://openalex.org/W1973046741","https://openalex.org/W2983282793","https://openalex.org/W2772628444","https://openalex.org/W1484355083","https://openalex.org/W2735929803","https://openalex.org/W4220714703","https://openalex.org/W2098758514","https://openalex.org/W3008845055","https://openalex.org/W2170391450","https://openalex.org/W4376854386"],"abstract_inverted_index":{"Recommendation":[0],"systems":[1,12],"are":[2],"a":[3,14,80,86,96],"popular":[4,32],"marketing":[5],"strategy":[6],"for":[7],"online":[8,33],"service":[9],"providers.":[10],"These":[11],"predict":[13],"customer's":[15],"future":[16],"preferences":[17],"from":[18,54],"the":[19,26,31,48,70,74,109,113,117,127,134,149,155],"past":[20,55],"behaviors":[21],"of":[22,30,37,51,63,76,88,116,130],"that":[23,145],"customer":[24],"and":[25,44,132,141,153],"other":[27],"customers.":[28],"Most":[29],"stores":[34],"process":[35,95],"millions":[36],"transactions":[38,56],"per":[39],"day;":[40],"therefore,":[41],"providing":[42],"quick":[43],"quality":[45],"recommendations":[46],"using":[47,108],"large":[49],"amount":[50,75],"data":[52],"collected":[53],"can":[57,65],"be":[58,66,91],"challenging.":[59],"Parallel":[60],"processing":[61],"power":[62],"GPUs":[64],"used":[67],"to":[68,93,125],"accelerate":[69],"recommendation":[71,105],"process.":[72],"However,":[73],"memory":[77],"available":[78],"on":[79,139],"GPU":[81,157],"card":[82],"is":[83],"limited;":[84],"thus,":[85],"number":[87,129],"passes":[89,131],"may":[90],"required":[92,128],"completely":[94],"large-scale":[97],"dataset.":[98],"This":[99],"paper":[100],"proposes":[101],"two":[102,122],"parallel,":[103],"item-based":[104],"algorithms":[106,147],"implemented":[107],"CUDA":[110],"platform.":[111],"Considering":[112],"high":[114],"sparsity":[115],"user-item":[118],"data,":[119],"we":[120],"utilize":[121],"compression":[123],"techniques":[124],"reduce":[126],"increase":[133],"speedup.":[135],"The":[136],"experimental":[137],"results":[138],"synthetic":[140],"real-world":[142],"datasets":[143],"show":[144],"our":[146],"outperform":[148],"respective":[150],"CPU":[151],"implementations":[152],"also":[154],"na\u00efve":[156],"implementation":[158],"which":[159],"does":[160],"not":[161],"use":[162],"compression.":[163]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":8},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}