{"id":"https://openalex.org/W2626376796","doi":"https://doi.org/10.1145/3109859.3109876","title":"Getting Deep Recommenders Fit","display_name":"Getting Deep Recommenders Fit","publication_year":2017,"publication_date":"2017-08-24","ids":{"openalex":"https://openalex.org/W2626376796","doi":"https://doi.org/10.1145/3109859.3109876","mag":"2626376796"},"language":"en","primary_location":{"id":"doi:10.1145/3109859.3109876","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3109859.3109876","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eleventh 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/A5103092198","display_name":"Joan Serr\u00e0","orcid":"https://orcid.org/0000-0003-1303-6558"},"institutions":[{"id":"https://openalex.org/I4210134591","display_name":"Telefonica Research and Development","ror":"https://ror.org/03qgzzb04","country_code":"ES","type":"company","lineage":["https://openalex.org/I4210097190","https://openalex.org/I4210134591"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Joan Serr\u00e0","raw_affiliation_strings":["Telef\u00f3nica Research, Barcelona, Spain"],"affiliations":[{"raw_affiliation_string":"Telef\u00f3nica Research, Barcelona, Spain","institution_ids":["https://openalex.org/I4210134591"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082408006","display_name":"Alexandros Karatzoglou","orcid":"https://orcid.org/0000-0001-6063-9023"},"institutions":[{"id":"https://openalex.org/I4210134591","display_name":"Telefonica Research and Development","ror":"https://ror.org/03qgzzb04","country_code":"ES","type":"company","lineage":["https://openalex.org/I4210097190","https://openalex.org/I4210134591"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Alexandros Karatzoglou","raw_affiliation_strings":["Telef\u00f3nica Research, Barcelona, Spain"],"affiliations":[{"raw_affiliation_string":"Telef\u00f3nica Research, Barcelona, Spain","institution_ids":["https://openalex.org/I4210134591"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103092198"],"corresponding_institution_ids":["https://openalex.org/I4210134591"],"apc_list":null,"apc_paid":null,"fwci":11.6071,"has_fulltext":false,"cited_by_count":45,"citation_normalized_percentile":{"value":0.98409041,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"279","last_page":"287"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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/T11478","display_name":"Caching and Content Delivery","score":0.9950000047683716,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9851999878883362,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8524107933044434},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5331864356994629},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.5166721343994141},{"id":"https://openalex.org/keywords/bloom-filter","display_name":"Bloom filter","score":0.49551546573638916},{"id":"https://openalex.org/keywords/constant","display_name":"Constant (computer programming)","score":0.4941008388996124},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44420355558395386},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4377436935901642},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4172933101654053},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36132490634918213},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.33973392844200134},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33137601613998413},{"id":"https://openalex.org/keywords/arithmetic","display_name":"Arithmetic","score":0.1092022955417633}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8524107933044434},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5331864356994629},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5166721343994141},{"id":"https://openalex.org/C147224247","wikidata":"https://www.wikidata.org/wiki/Q885373","display_name":"Bloom filter","level":2,"score":0.49551546573638916},{"id":"https://openalex.org/C2777027219","wikidata":"https://www.wikidata.org/wiki/Q1284190","display_name":"Constant (computer programming)","level":2,"score":0.4941008388996124},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44420355558395386},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4377436935901642},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4172933101654053},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36132490634918213},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.33973392844200134},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33137601613998413},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.1092022955417633},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3109859.3109876","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3109859.3109876","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W36903255","https://openalex.org/W1532325895","https://openalex.org/W1544599463","https://openalex.org/W1556219185","https://openalex.org/W1580997674","https://openalex.org/W1595409123","https://openalex.org/W1605569224","https://openalex.org/W1676820704","https://openalex.org/W1810943226","https://openalex.org/W1825640910","https://openalex.org/W1902934009","https://openalex.org/W1991418309","https://openalex.org/W2025341678","https://openalex.org/W2064675550","https://openalex.org/W2070996757","https://openalex.org/W2102765684","https://openalex.org/W2103194807","https://openalex.org/W2112490010","https://openalex.org/W2119144962","https://openalex.org/W2123845384","https://openalex.org/W2140679639","https://openalex.org/W2146502635","https://openalex.org/W2151211319","https://openalex.org/W2154109204","https://openalex.org/W2155144632","https://openalex.org/W2155912844","https://openalex.org/W2156387975","https://openalex.org/W2158139315","https://openalex.org/W2171061940","https://openalex.org/W2177847924","https://openalex.org/W2219888463","https://openalex.org/W2253995343","https://openalex.org/W2262817822","https://openalex.org/W2295739661","https://openalex.org/W2400921766","https://openalex.org/W2471920251","https://openalex.org/W2475334473","https://openalex.org/W2505072785","https://openalex.org/W2519314406","https://openalex.org/W2899702797","https://openalex.org/W2950577311","https://openalex.org/W2951238624","https://openalex.org/W2952432176","https://openalex.org/W2963603213","https://openalex.org/W2964121744","https://openalex.org/W2964199361","https://openalex.org/W2998508934","https://openalex.org/W3000138670","https://openalex.org/W3102895136","https://openalex.org/W4213009331","https://openalex.org/W4285719527","https://openalex.org/W6682939927","https://openalex.org/W6726378182"],"related_works":["https://openalex.org/W2086572746","https://openalex.org/W2604468458","https://openalex.org/W2157216338","https://openalex.org/W121740227","https://openalex.org/W1662107788","https://openalex.org/W3082379938","https://openalex.org/W2135966669","https://openalex.org/W2594143027","https://openalex.org/W2732769800","https://openalex.org/W4390846322"],"abstract_inverted_index":{"Recommendation":[0],"algorithms":[1,30],"that":[2,39,80,180],"incorporate":[3],"techniques":[4],"from":[5,20],"deep":[6],"learning":[7],"are":[8,101],"becoming":[9],"increasingly":[10],"popular.":[11],"Due":[12],"to":[13,32,48,51,61,84,115,133,187],"the":[14,17,52,85,109,112,126,178,188],"structure":[15],"of":[16,26,55,89,111,158,161],"data":[18,141],"coming":[19],"recommendation":[21],"domains":[22],"(i.e.,":[23],"one-hot-encoded":[24],"vectors":[25],"item":[27],"preferences),":[28],"these":[29,71],"tend":[31],"have":[33],"large":[34],"input":[35,86],"and":[36,59,87,104,143],"output":[37,88],"dimensionalities":[38],"dominate":[40],"their":[41],"overall":[42],"size.":[43],"This":[44],"makes":[45],"them":[46],"difficult":[47,60],"train,":[49],"due":[50],"limited":[53,67],"memory":[54],"graphical":[56],"processing":[57],"units,":[58],"deploy":[62],"on":[63,139],"mobile":[64],"devices":[65],"with":[66,94,129],"hardware.":[68],"To":[69],"address":[70],"difficulties,":[72],"we":[73],"propose":[74],"Bloom":[75,99,137,162],"embeddings,":[76,163],"a":[77,156],"compression":[78,117],"technique":[79],"can":[81],"be":[82],"applied":[83],"neural":[90],"network":[91],"models":[92],"dealing":[93],"sparse":[95],"high-dimensional":[96],"binary-coded":[97],"instances.":[98],"embeddings":[100,138],"computationally":[102],"efficient,":[103],"do":[105,182],"not":[106,183],"seriously":[107],"compromise":[108],"accuracy":[110],"model":[113,190],"up":[114,132],"1/5":[116],"ratios.":[118],"In":[119],"some":[120],"cases,":[121],"they":[122,181],"even":[123],"improve":[124],"over":[125],"original":[127],"accuracy,":[128],"relative":[130],"increases":[131],"12%.":[134],"We":[135,153],"evaluate":[136],"7":[140],"sets":[142],"compare":[144],"it":[145],"against":[146],"4":[147],"alternative":[148],"methods,":[149],"obtaining":[150],"favorable":[151],"results.":[152],"also":[154],"discuss":[155],"number":[157],"further":[159],"advantages":[160],"such":[164],"as":[165],"'on-the-fly'":[166],"constant-time":[167],"operation,":[168],"zero":[169],"or":[170,177,192],"marginal":[171],"space":[172],"requirements,":[173],"training":[174,193],"time":[175],"speedups,":[176],"fact":[179],"require":[184],"any":[185],"change":[186],"core":[189],"architecture":[191],"configuration.":[194]},"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":5},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":14},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
