{"id":"https://openalex.org/W4415317437","doi":"https://doi.org/10.48550/arxiv.2509.13001","title":"Green Recommender Systems: Understanding and Minimizing the Carbon Footprint of AI-Powered Personalization","display_name":"Green Recommender Systems: Understanding and Minimizing the Carbon Footprint of AI-Powered Personalization","publication_year":2025,"publication_date":"2025-09-16","ids":{"openalex":"https://openalex.org/W4415317437","doi":"https://doi.org/10.48550/arxiv.2509.13001"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2509.13001","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.13001","pdf_url":"https://arxiv.org/pdf/2509.13001","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2509.13001","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023751378","display_name":"Lukas Wegmeth","orcid":"https://orcid.org/0000-0001-8848-9434"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Wegmeth, Lukas","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Vente, Tobias","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vente, Tobias","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040472816","display_name":"Alan Said","orcid":"https://orcid.org/0000-0002-2929-0529"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Said, Alan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5081348430","display_name":"Joeran Beel","orcid":"https://orcid.org/0000-0002-4537-5573"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Beel, Joeran","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5023751378"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14260","display_name":"Impact of AI and Big Data on Business and Society","score":0.8851000070571899,"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"}},"topics":[{"id":"https://openalex.org/T14260","display_name":"Impact of AI and Big Data on Business and Society","score":0.8851000070571899,"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"}},{"id":"https://openalex.org/T12885","display_name":"Digital Innovation in Industries","score":0.8805000185966492,"subfield":{"id":"https://openalex.org/subfields/1405","display_name":"Management of Technology and Innovation"},"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/T11942","display_name":"Transportation and Mobility Innovations","score":0.756600022315979,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.896399974822998},{"id":"https://openalex.org/keywords/carbon-footprint","display_name":"Carbon footprint","score":0.8152999877929688},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.6067000031471252},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.5504000186920166},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4172999858856201},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.3917999863624573},{"id":"https://openalex.org/keywords/consumption","display_name":"Consumption (sociology)","score":0.37279999256134033},{"id":"https://openalex.org/keywords/green-computing","display_name":"Green computing","score":0.3662000000476837}],"concepts":[{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.896399974822998},{"id":"https://openalex.org/C2780936489","wikidata":"https://www.wikidata.org/wiki/Q310667","display_name":"Carbon footprint","level":3,"score":0.8152999877929688},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6707000136375427},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.6067000031471252},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.5504000186920166},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4172999858856201},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39629998803138733},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.3917999863624573},{"id":"https://openalex.org/C30772137","wikidata":"https://www.wikidata.org/wiki/Q5164762","display_name":"Consumption (sociology)","level":2,"score":0.37279999256134033},{"id":"https://openalex.org/C75027835","wikidata":"https://www.wikidata.org/wiki/Q1064746","display_name":"Green computing","level":3,"score":0.3662000000476837},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3596000075340271},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3528999984264374},{"id":"https://openalex.org/C134560507","wikidata":"https://www.wikidata.org/wiki/Q753291","display_name":"Environmental economics","level":1,"score":0.3407999873161316},{"id":"https://openalex.org/C83516724","wikidata":"https://www.wikidata.org/wiki/Q234173","display_name":"Ecological footprint","level":3,"score":0.33799999952316284},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.31850001215934753},{"id":"https://openalex.org/C164749845","wikidata":"https://www.wikidata.org/wiki/Q320389","display_name":"Environmental impact assessment","level":2,"score":0.31369999051094055},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.3075999915599823},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.29809999465942383},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.2948000133037567},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.25699999928474426}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2509.13001","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.13001","pdf_url":"https://arxiv.org/pdf/2509.13001","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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"},{"id":"doi:10.48550/arxiv.2509.13001","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.13001","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2509.13001","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.13001","pdf_url":"https://arxiv.org/pdf/2509.13001","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"As":[0],"global":[1],"warming":[2],"soars,":[3],"the":[4,10,22,31,42,69,98,177,191,205,209],"need":[5,207],"to":[6,67,84,188,217,228],"assess":[7],"and":[8,29,63,75,89,100,118,134,212,225],"reduce":[9],"environmental":[11,32,43,70,226],"impact":[12,33,44],"of":[13,34,45,72,171,180,193],"recommender":[14,23,46,78,81,210],"systems":[15,24,47,79,82,211],"is":[16],"becoming":[17],"increasingly":[18],"urgent.":[19],"Despite":[20],"this,":[21],"community":[25],"hardly":[26],"understands,":[27],"addresses,":[28],"evaluates":[30],"their":[35,73,86],"work.":[36],"In":[37],"this":[38],"study,":[39],"we":[40,58],"examine":[41],"research":[48],"by":[49,196],"reproducing":[50],"typical":[51],"experimental":[52,121],"pipelines.":[53],"Based":[54],"on":[55,65],"our":[56],"results,":[57],"provide":[59],"guidelines":[60],"for":[61,123,208],"researchers":[62],"practitioners":[64],"how":[66],"minimize":[68,85],"footprint":[71],"work":[74,203],"implement":[76],"green":[77,219],"-":[80,174],"designed":[83,117],"energy":[87,127,132],"consumption":[88,128],"carbon":[90,178],"footprint.":[91],"Our":[92,140],"analysis":[93],"covers":[94],"79":[95],"papers":[96,144,157],"from":[97,184],"2013":[99],"2023":[101],"ACM":[102],"RecSys":[103],"conferences,":[104],"comparing":[105],"traditional":[106,159],"\"good":[107],"old-fashioned":[108],"AI\"":[109],"models":[110,148],"with":[111,233],"modern":[112],"deep":[113,146,165],"learning":[114,147,215],"models.":[115,160],"We":[116],"reproduced":[119],"representative":[120],"pipelines":[122],"both":[124],"years,":[125],"measuring":[126],"using":[129,158],"a":[130,163,181,230],"hardware":[131],"meter":[133],"converting":[135],"it":[136],"into":[137],"CO2":[138,154,172,194],"equivalents.":[139],"results":[141],"show":[142],"that":[143],"utilizing":[145],"emit":[149],"approximately":[150],"42":[151],"times":[152],"more":[153,175],"equivalents":[155,173],"than":[156,176],"On":[161],"average,":[162],"single":[164],"learning-based":[166],"paper":[167],"generates":[168],"2,909":[169],"kilograms":[170],"emissions":[179],"person":[182],"flying":[183],"New":[185],"York":[186],"City":[187],"Melbourne":[189],"or":[190],"amount":[192],"sequestered":[195],"one":[197],"tree":[198],"over":[199],"260":[200],"years.":[201],"This":[202],"underscores":[204],"urgent":[206],"wider":[213],"machine":[214],"communities":[216],"adopt":[218],"AI":[220],"principles,":[221],"balancing":[222],"algorithmic":[223],"advancements":[224],"responsibility":[227],"build":[229],"sustainable":[231],"future":[232],"AI-powered":[234],"personalization.":[235]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-18T00:00:00"}