{"id":"https://openalex.org/W4403791660","doi":"https://doi.org/10.1145/3664647.3680615","title":"Mitigating Sample Selection Bias with Robust Domain Adaption in Multimedia Recommendation","display_name":"Mitigating Sample Selection Bias with Robust Domain Adaption in Multimedia Recommendation","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4403791660","doi":"https://doi.org/10.1145/3664647.3680615"},"language":"en","primary_location":{"id":"doi:10.1145/3664647.3680615","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3664647.3680615","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3664647.3680615","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059445819","display_name":"Jiaye Lin","orcid":"https://orcid.org/0009-0002-4485-6612"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiaye Lin","raw_affiliation_strings":["Tsinghua University & Peng Cheng Laboratory, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University & Peng Cheng Laboratory, Beijing, China","institution_ids":["https://openalex.org/I4210136793","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100734609","display_name":"Qing Li","orcid":"https://orcid.org/0000-0002-6071-473X"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qing Li","raw_affiliation_strings":["Peng Cheng Laboratory, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078895699","display_name":"Guorui Xie","orcid":"https://orcid.org/0000-0001-7532-9116"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guorui Xie","raw_affiliation_strings":["Tsinghua University & Peng Cheng Laboratory, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University & Peng Cheng Laboratory, Beijing, China","institution_ids":["https://openalex.org/I4210136793","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110249395","display_name":"Z. C. Guan","orcid":"https://orcid.org/0009-0002-8911-5092"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongxu Guan","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101626204","display_name":"Yong Jiang","orcid":"https://orcid.org/0000-0002-4260-1395"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Jiang","raw_affiliation_strings":["Tsinghua University & Peng Cheng Laboratory, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University & Peng Cheng Laboratory, Beijing, China","institution_ids":["https://openalex.org/I4210136793","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108920782","display_name":"Ting Xu","orcid":"https://orcid.org/0000-0002-0498-2393"},"institutions":[{"id":"https://openalex.org/I33434090","display_name":"University of Massachusetts Boston","ror":"https://ror.org/04ydmy275","country_code":"US","type":"education","lineage":["https://openalex.org/I33434090"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ting Xu","raw_affiliation_strings":["University of Massachusetts Boston, Boston, USA"],"affiliations":[{"raw_affiliation_string":"University of Massachusetts Boston, Boston, USA","institution_ids":["https://openalex.org/I33434090"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100350979","display_name":"Zhong Zhang","orcid":"https://orcid.org/0009-0008-7584-057X"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhong Zhang","raw_affiliation_strings":["Tencent AI Lab, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015991234","display_name":"Peilin Zhao","orcid":"https://orcid.org/0000-0001-8543-3953"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peilin Zhao","raw_affiliation_strings":["Tencent AI Lab, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5059445819"],"corresponding_institution_ids":["https://openalex.org/I4210136793","https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.7794,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.80079333,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"7581","last_page":"7590"},"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9916999936103821,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9850999712944031,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7476722598075867},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.7372629046440125},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.6798227429389954},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5233606100082397},{"id":"https://openalex.org/keywords/selection-bias","display_name":"Selection bias","score":0.46445393562316895},{"id":"https://openalex.org/keywords/multimedia","display_name":"Multimedia","score":0.4622378945350647},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.4294380247592926},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.21536365151405334},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.18701201677322388},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09054756164550781}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7476722598075867},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.7372629046440125},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.6798227429389954},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5233606100082397},{"id":"https://openalex.org/C40423286","wikidata":"https://www.wikidata.org/wiki/Q284172","display_name":"Selection bias","level":2,"score":0.46445393562316895},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.4622378945350647},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.4294380247592926},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.21536365151405334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.18701201677322388},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09054756164550781},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3664647.3680615","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3664647.3680615","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3664647.3680615","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3664647.3680615","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":70,"referenced_works":["https://openalex.org/W398859631","https://openalex.org/W1622676895","https://openalex.org/W2000431947","https://openalex.org/W2002834872","https://openalex.org/W2132083787","https://openalex.org/W2136189984","https://openalex.org/W2217007515","https://openalex.org/W2512971201","https://openalex.org/W2523239021","https://openalex.org/W2593507512","https://openalex.org/W2622338386","https://openalex.org/W2629213068","https://openalex.org/W2723293840","https://openalex.org/W2741632195","https://openalex.org/W2741914399","https://openalex.org/W2783666221","https://openalex.org/W2788495710","https://openalex.org/W2808847742","https://openalex.org/W2908332126","https://openalex.org/W2945772520","https://openalex.org/W2950960796","https://openalex.org/W2962745591","https://openalex.org/W2963004721","https://openalex.org/W2964182926","https://openalex.org/W2984589663","https://openalex.org/W2997636389","https://openalex.org/W3023045848","https://openalex.org/W3032044946","https://openalex.org/W3035404611","https://openalex.org/W3036320503","https://openalex.org/W3092103025","https://openalex.org/W3093002391","https://openalex.org/W3093519337","https://openalex.org/W3096437212","https://openalex.org/W3098468692","https://openalex.org/W3103310105","https://openalex.org/W3105595718","https://openalex.org/W3106181667","https://openalex.org/W3106252282","https://openalex.org/W3131163951","https://openalex.org/W3133451866","https://openalex.org/W3153906321","https://openalex.org/W3154197656","https://openalex.org/W3159243351","https://openalex.org/W3164238513","https://openalex.org/W3170163874","https://openalex.org/W3192113933","https://openalex.org/W3205778609","https://openalex.org/W3206324923","https://openalex.org/W3206458369","https://openalex.org/W4220744054","https://openalex.org/W4284687944","https://openalex.org/W4284702440","https://openalex.org/W4284707446","https://openalex.org/W4284709839","https://openalex.org/W4287124998","https://openalex.org/W4292258433","https://openalex.org/W4293569092","https://openalex.org/W4304014904","https://openalex.org/W4306317202","https://openalex.org/W4306317673","https://openalex.org/W4306317707","https://openalex.org/W4317884287","https://openalex.org/W4321593910","https://openalex.org/W4385486275","https://openalex.org/W4385567997","https://openalex.org/W4386114032","https://openalex.org/W4387848990","https://openalex.org/W4387934884","https://openalex.org/W4390873600"],"related_works":["https://openalex.org/W4390273403","https://openalex.org/W4386781444","https://openalex.org/W2150182025","https://openalex.org/W3092950680","https://openalex.org/W3197542405","https://openalex.org/W2056712470","https://openalex.org/W3125580266","https://openalex.org/W4288390103","https://openalex.org/W1884218869","https://openalex.org/W2159994411"],"abstract_inverted_index":{"Industrial":[0],"multimedia":[1,207],"recommendation":[2,55,209],"systems":[3],"extensively":[4],"utilize":[5,59,154],"cascade":[6],"architectures":[7],"to":[8,35,75,89,110,126,144,159],"deliver":[9],"personalized":[10],"content":[11],"for":[12,44,93,147,211],"users,":[13],"generally":[14],"consisting":[15],"of":[16,168,205,221],"multiple":[17],"stages":[18],"like":[19],"retrieval":[20,24,60,176,203],"and":[21,47,77,91,135,197],"ranking.":[22],"However,":[23],"models":[25],"have":[26],"long":[27],"suffered":[28],"from":[29,123],"Sample":[30],"Selection":[31],"Bias":[32],"(SSB)":[33],"due":[34],"the":[36,40,48,129,166,202,219],"distribution":[37],"discrepancy":[38],"between":[39],"exposed":[41],"items":[42],"used":[43],"model":[45,163,177,204],"training":[46,64,187],"candidates":[49,61],"(almost":[50],"unexposed)":[51],"during":[52,182],"inference,":[53],"affecting":[54],"performance.":[56],"Traditional":[57],"methods":[58],"as":[62,70,128],"augmented":[63],"data,":[65],"indiscriminately":[66],"treating":[67],"unexposed":[68,124],"data":[69,125,133],"negative":[71],"samples,":[72],"which":[73,106],"leads":[74],"inaccuracies":[76],"noise.":[78],"Some":[79],"efforts":[80],"rely":[81],"on":[82,193],"unbiased":[83],"datasets,":[84],"while":[85],"they":[86],"are":[87],"costly":[88],"collect":[90],"insufficient":[92],"industrial":[94],"models.":[95],"In":[96],"this":[97],"paper,":[98],"we":[99,119,153,173],"propose":[100],"a":[101,161,175,185,206],"debiasing":[102],"framework":[103],"named":[104],"DAMCAR,":[105],"introduces":[107],"Domain":[108],"Adaptation":[109],"mitigate":[111],"SSB":[112],"in":[113,201,223],"Multimedia":[114],"CAscade":[115],"Recommendation":[116],"systems.":[117],"Firstly,":[118],"sample":[120],"hard-to-distinguish":[121],"samples":[122],"serve":[127],"target":[130],"domain,":[131],"optimizing":[132],"quality":[134],"resource":[136],"utilization.":[137],"Secondly,":[138],"adversarial":[139],"domain":[140],"adaptation":[141],"is":[142],"employed":[143],"generate":[145],"pseudo-labels":[146,169],"each":[148],"sample.":[149],"To":[150],"enhance":[151],"robustness,":[152],"Exponential":[155],"Moving":[156],"Average":[157],"(EMA)":[158],"create":[160],"teacher":[162],"that":[164,178],"supervises":[165],"generation":[167],"via":[170],"self-distillation.":[171],"Finally,":[172],"obtain":[174],"maintains":[179],"stable":[180],"performance":[181],"inference":[183],"through":[184],"hybrid":[186],"mechanism.":[188],"We":[189],"conduct":[190],"offline":[191],"experiments":[192],"two":[194],"real-world":[195],"datasets":[196],"deploy":[198],"our":[199],"approach":[200],"video":[208],"system":[210],"online":[212],"A/B":[213],"testing.":[214],"Comprehensive":[215],"experimental":[216],"results":[217],"demonstrate":[218],"effectiveness":[220],"DAMCAR":[222],"practical":[224],"applications.":[225]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}