{"id":"https://openalex.org/W3088887372","doi":"https://doi.org/10.1145/3383313.3411543","title":"Tutorial: Feature Engineering for Recommender Systems","display_name":"Tutorial: Feature Engineering for Recommender Systems","publication_year":2020,"publication_date":"2020-09-19","ids":{"openalex":"https://openalex.org/W3088887372","doi":"https://doi.org/10.1145/3383313.3411543","mag":"3088887372"},"language":"en","primary_location":{"id":"doi:10.1145/3383313.3411543","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3383313.3411543","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Fourteenth ACM Conference on Recommender Systems","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/A5061514003","display_name":"Benedikt Schifferer","orcid":"https://orcid.org/0009-0007-0941-4110"},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Benedikt Schifferer","raw_affiliation_strings":["NVIDIA, USA"],"affiliations":[{"raw_affiliation_string":"NVIDIA, USA","institution_ids":["https://openalex.org/I4210127875"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026940220","display_name":"Chris Deotte","orcid":null},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chris Deotte","raw_affiliation_strings":["Nvidia, United States"],"affiliations":[{"raw_affiliation_string":"Nvidia, United States","institution_ids":["https://openalex.org/I4210127875"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023954126","display_name":"Even Oldridge","orcid":"https://orcid.org/0009-0002-1990-0941"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Even Oldridge","raw_affiliation_strings":["NVIDIA, Canada"],"affiliations":[{"raw_affiliation_string":"NVIDIA, Canada","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5061514003"],"corresponding_institution_ids":["https://openalex.org/I4210127875"],"apc_list":null,"apc_paid":null,"fwci":0.5302,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.73061638,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"754","last_page":"755"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9699000120162964,"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"}},"topics":[{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9699000120162964,"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/T13650","display_name":"Computational Physics and Python Applications","score":0.9470000267028809,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9176999926567078,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8593113422393799},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.8226522207260132},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.8151736259460449},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.6189835667610168},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5316802859306335},{"id":"https://openalex.org/keywords/session","display_name":"Session (web analytics)","score":0.52869713306427},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.3898359537124634},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.382889986038208},{"id":"https://openalex.org/keywords/multimedia","display_name":"Multimedia","score":0.37133342027664185},{"id":"https://openalex.org/keywords/software-engineering","display_name":"Software engineering","score":0.3303561806678772},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.32930266857147217},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.2570790946483612}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8593113422393799},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.8226522207260132},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.8151736259460449},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.6189835667610168},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5316802859306335},{"id":"https://openalex.org/C2779182362","wikidata":"https://www.wikidata.org/wiki/Q17126187","display_name":"Session (web analytics)","level":2,"score":0.52869713306427},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.3898359537124634},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.382889986038208},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.37133342027664185},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.3303561806678772},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.32930266857147217},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.2570790946483612}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3383313.3411543","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3383313.3411543","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Fourteenth ACM Conference on Recommender Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.6100000143051147,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2341492732","https://openalex.org/W3187193180","https://openalex.org/W106542691","https://openalex.org/W4287027380","https://openalex.org/W1699080303","https://openalex.org/W4297799326","https://openalex.org/W3116064965","https://openalex.org/W3193760048","https://openalex.org/W4390273403","https://openalex.org/W3034267371"],"abstract_inverted_index":{"The":[0,48],"selection":[1],"of":[2,7,23,44,85,177],"features":[3,107],"and":[4,36,42,56,58,72,91,94,127,167,182],"proper":[5],"preparation":[6],"data":[8],"for":[9,39],"deep":[10],"learning":[11,14],"or":[12],"machine":[13],"models":[15],"plays":[16],"a":[17,31,83,86,174],"significant":[18],"role":[19],"in":[20,113,159],"the":[21,64,80,89,105,118,136,151],"performance":[22],"recommender":[24,45,178],"systems.":[25],"To":[26],"address":[27],"this":[28],"we":[29],"propose":[30],"tutorial":[32,49,81,119,137],"highlighting":[33],"best":[34],"practices":[35],"optimization":[37],"techniques":[38],"feature":[40,52],"engineering":[41,53],"preprocessing":[43],"system":[46],"datasets.":[47],"will":[50,59,103,116,130],"explore":[51],"using":[54,66,162],"pandas":[55,166],"Dask,":[57],"also":[60],"cover":[61],"acceleration":[62],"on":[63,99,120],"GPU":[65],"open":[67],"source":[68],"libraries":[69],"like":[70],"RAPIDS":[71],"NVTabular.":[73],"Proposed":[74],"length":[75],"is":[76],"180min.":[77],"We\u2019ve":[78],"designed":[79],"as":[82,165],"combination":[84],"lecture":[87],"covering":[88],"mathematical":[90],"theoretical":[92],"background":[93],"an":[95],"interactive":[96],"session":[97],"based":[98],"jupyter":[100,125],"notebooks.":[101],"Participants":[102,129],"practice":[104],"discussed":[106],"by":[108],"writing":[109],"their":[110,121,139],"own":[111],"implementation":[112],"Python.":[114],"NVIDIA":[115],"host":[117],"infrastructure,":[122],"providing":[123],"dataset,":[124],"notebooks":[126],"GPUs.":[128],"be":[131],"able":[132],"to":[133,147],"easily":[134],"attend":[135],"via":[138],"web":[140],"browsers,":[141],"avoiding":[142],"any":[143],"complicated":[144],"setup.":[145],"Beginner":[146],"intermediate":[148],"users":[149],"are":[150],"target":[152],"audience,":[153],"which":[154],"should":[155,172],"have":[156,173],"prior":[157],"knowledge":[158],"python":[160],"programming":[161],"libraries,":[163],"such":[164],"NumPy.":[168],"In":[169],"addition,":[170],"they":[171],"basic":[175],"understanding":[176],"systems,":[179],"decision":[180],"trees":[181],"feed":[183],"forward":[184],"neural":[185],"networks.":[186]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
