{"id":"https://openalex.org/W2964247748","doi":"https://doi.org/10.1145/3213586.3226206","title":"Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations","display_name":"Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations","publication_year":2018,"publication_date":"2018-07-02","ids":{"openalex":"https://openalex.org/W2964247748","doi":"https://doi.org/10.1145/3213586.3226206","mag":"2964247748"},"language":"en","primary_location":{"id":"doi:10.1145/3213586.3226206","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3213586.3226206","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization","raw_type":"proceedings-article"},"type":"conference-paper","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/A5007407583","display_name":"Chen Karako","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen Karako","raw_affiliation_strings":["Shopify Inc., Montreal, PQ, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shopify Inc., Montreal, PQ, Canada","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057416435","display_name":"Putra Manggala","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Putra Manggala","raw_affiliation_strings":["Shopify Inc., Montreal, PQ, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shopify Inc., Montreal, PQ, Canada","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.293,"has_fulltext":false,"cited_by_count":57,"citation_normalized_percentile":{"value":0.8986187,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"23","last_page":"28"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9523000121116638,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9523000121116638,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9517999887466431,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9434999823570251,"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/ranking","display_name":"Ranking (information retrieval)","score":0.7918004989624023},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7589477300643921},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.7387237548828125},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.7330629229545593},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5132226943969727},{"id":"https://openalex.org/keywords/demographics","display_name":"Demographics","score":0.4973948299884796},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4776148498058319},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44967204332351685},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40801531076431274},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4065614640712738}],"concepts":[{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.7918004989624023},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7589477300643921},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.7387237548828125},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.7330629229545593},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5132226943969727},{"id":"https://openalex.org/C2780084366","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demographics","level":2,"score":0.4973948299884796},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4776148498058319},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44967204332351685},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40801531076431274},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4065614640712738},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3213586.3226206","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3213586.3226206","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6100000143051147}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1987431925","https://openalex.org/W2048571927","https://openalex.org/W2072326387","https://openalex.org/W2100142570","https://openalex.org/W2131571251","https://openalex.org/W2183341477","https://openalex.org/W2527238922","https://openalex.org/W2593864460","https://openalex.org/W2625758617","https://openalex.org/W2704480242","https://openalex.org/W2768075475","https://openalex.org/W3102092462","https://openalex.org/W3138773240"],"related_works":["https://openalex.org/W3121380072","https://openalex.org/W2383111961","https://openalex.org/W1508220431","https://openalex.org/W2365952365","https://openalex.org/W2352448290","https://openalex.org/W2380820513","https://openalex.org/W2005801347","https://openalex.org/W2041353081","https://openalex.org/W1970551129","https://openalex.org/W2581240705"],"abstract_inverted_index":{"The":[0],"trade-off":[1,79],"between":[2,80],"relevance":[3,81],"and":[4,76,82,109,180],"fairness":[5,60,83,133],"in":[6,12,134],"personalized":[7],"recommendations":[8],"has":[9],"been":[10],"explored":[11],"recent":[13],"works,":[14],"with":[15,61],"the":[16,36,78,96,99,116,135,143,163],"goal":[17],"of":[18,35,46,98,105,119,122,155],"minimizing":[19],"learned":[20],"discrimination":[21],"towards":[22],"certain":[23],"demographics":[24],"while":[25,138,145],"still":[26],"producing":[27],"relevant":[28],"results.":[29],"We":[30,67,125],"present":[31],"a":[32,51,72,85,106,112,120,152,174],"fairness-aware":[33],"variation":[34],"Maximal":[37],"Marginal":[38],"Relevance":[39],"(MMR)":[40],"re-ranking":[41,100],"method":[42,55,130,169],"which":[43],"uses":[44],"representations":[45,164],"demographic":[47,65],"groups":[48],"computed":[49],"using":[50,91,102],"labeled":[52,107,156],"dataset.":[53],"This":[54,168],"is":[56],"intended":[57],"to":[58,63,94,161,165],"incorporate":[59,132],"respect":[62],"these":[64],"groups.":[66],"perform":[68],"an":[69],"experiment":[70],"on":[71,115],"stock":[73],"photo":[74],"dataset":[75],"examine":[77,95],"against":[84],"well":[86],"known":[87],"baseline,":[88,144],"MMR,":[89],"by":[90,110],"human":[92],"judgment":[93],"results":[97,118,137],"when":[101],"different":[103],"fractions":[104],"dataset,":[108],"performing":[111],"quantitative":[113],"analysis":[114],"ranked":[117,136],"set":[121],"query":[123],"images.":[124],"show":[126],"that":[127,150],"our":[128,146],"proposed":[129],"can":[131,158,170],"obtaining":[139],"higher":[140],"precision":[141],"than":[142],"case":[147],"study":[148],"shows":[149],"even":[151],"limited":[153],"amount":[154],"data":[157],"be":[159,171],"used":[160,172],"compute":[162],"obtain":[166],"fairness.":[167],"as":[173],"post-processing":[175],"step":[176],"for":[177],"recommender":[178],"systems":[179],"search.":[181]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":4},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}