{"id":"https://openalex.org/W3035589558","doi":"https://doi.org/10.1145/3397271.3401155","title":"Sampler Design for Implicit Feedback Data by Noisy-label Robust Learning","display_name":"Sampler Design for Implicit Feedback Data by Noisy-label Robust Learning","publication_year":2020,"publication_date":"2020-07-25","ids":{"openalex":"https://openalex.org/W3035589558","doi":"https://doi.org/10.1145/3397271.3401155","mag":"3035589558"},"language":"en","primary_location":{"id":"doi:10.1145/3397271.3401155","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397271.3401155","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","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/A5064364239","display_name":"Wenhui Yu","orcid":"https://orcid.org/0000-0002-0886-3543"},"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":"Wenhui Yu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101460210","display_name":"Zheng Qin","orcid":"https://orcid.org/0000-0002-7090-7869"},"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":"Zheng Qin","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":54,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"861","last_page":"870"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9975000023841858,"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":0.9975000023841858,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9919000267982483,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9915000200271606,"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":0.7639762163162231},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.6416763067245483},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5426988005638123},{"id":"https://openalex.org/keywords/negative-feedback","display_name":"Negative feedback","score":0.5399667024612427},{"id":"https://openalex.org/keywords/noisy-data","display_name":"Noisy data","score":0.5170620083808899},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4952980577945709},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.48165690898895264},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.470389723777771},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.4502602517604828},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43123891949653625},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4242647588253021}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7639762163162231},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6416763067245483},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5426988005638123},{"id":"https://openalex.org/C93586867","wikidata":"https://www.wikidata.org/wiki/Q62527","display_name":"Negative feedback","level":3,"score":0.5399667024612427},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.5170620083808899},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4952980577945709},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.48165690898895264},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.470389723777771},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4502602517604828},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43123891949653625},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4242647588253021},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3397271.3401155","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397271.3401155","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","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":24,"referenced_works":["https://openalex.org/W1976999215","https://openalex.org/W1991055526","https://openalex.org/W2042281163","https://openalex.org/W2061873838","https://openalex.org/W2102035799","https://openalex.org/W2108862644","https://openalex.org/W2123958887","https://openalex.org/W2144685566","https://openalex.org/W2337403844","https://openalex.org/W2470901364","https://openalex.org/W2595890440","https://openalex.org/W2604662567","https://openalex.org/W2741249238","https://openalex.org/W2745560456","https://openalex.org/W2798881875","https://openalex.org/W2913637682","https://openalex.org/W2962712142","https://openalex.org/W2963655167","https://openalex.org/W3080236009","https://openalex.org/W3098649723","https://openalex.org/W4236979447","https://openalex.org/W4247950230","https://openalex.org/W4249267926","https://openalex.org/W4297971002"],"related_works":["https://openalex.org/W2188500270","https://openalex.org/W2303858293","https://openalex.org/W2915512527","https://openalex.org/W51364034","https://openalex.org/W2793336762","https://openalex.org/W2091548507","https://openalex.org/W2368816706","https://openalex.org/W2789936093","https://openalex.org/W3159414774","https://openalex.org/W4385728102"],"abstract_inverted_index":{"Implicit":[0],"feedback":[1,23,95],"data":[2,24],"is":[3,10,25,41],"extensively":[4],"explored":[5],"in":[6],"recommendation":[7],"as":[8,60,68],"it":[9],"easy":[11],"to":[12,43],"collect":[13],"and":[14,37,49],"generally":[15],"applicable.":[16],"However,":[17],"predicting":[18],"users'":[19],"preference":[20],"on":[21,89],"implicit":[22,94],"a":[26,75],"challenging":[27],"task":[28],"since":[29],"we":[30,83],"can":[31],"only":[32],"observe":[33],"positive":[34,51],"(voted)":[35],"samples":[36,48,52,70],"unvoted":[38,55,66],"samples.":[39],"It":[40],"difficult":[42],"distinguish":[44],"between":[45],"the":[46,54],"negative":[47,69],"unlabeled":[50],"from":[53,74],"ones.":[56],"Existing":[57],"works,":[58],"such":[59],"Bayesian":[61],"Personalized":[62],"Ranking":[63],"(BPR),":[64],"sample":[65],"items":[67],"uniformly,":[71],"therefore":[72],"suffer":[73],"critical":[76],"noisy-label":[77,90],"issue.":[78],"To":[79],"address":[80],"this":[81],"gap,":[82],"design":[84],"an":[85],"adaptive":[86],"sampler":[87],"based":[88],"robust":[91],"learning":[92],"for":[93],"data.":[96]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":15},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":13},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
