{"id":"https://openalex.org/W4412876972","doi":"https://doi.org/10.1145/3711896.3737276","title":"Leveraging Label Distributions as Anchors to Enhance Video Recommendation","display_name":"Leveraging Label Distributions as Anchors to Enhance Video Recommendation","publication_year":2025,"publication_date":"2025-08-03","ids":{"openalex":"https://openalex.org/W4412876972","doi":"https://doi.org/10.1145/3711896.3737276"},"language":"en","primary_location":{"id":"doi:10.1145/3711896.3737276","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737276","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737276","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737276","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100419187","display_name":"Yuan Xu","orcid":"https://orcid.org/0000-0002-2836-5293"},"institutions":[{"id":"https://openalex.org/I204250578","display_name":"University of California, Irvine","ror":"https://ror.org/04gyf1771","country_code":"US","type":"education","lineage":["https://openalex.org/I204250578"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yulin Xu","raw_affiliation_strings":["University of California, Irvine, Irvine, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Irvine, Irvine, CA, USA","institution_ids":["https://openalex.org/I204250578"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113083206","display_name":"Chao Cui","orcid":"https://orcid.org/0009-0000-2097-7888"},"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":"Chao Cui","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/A5102951040","display_name":"Shisong Tang","orcid":"https://orcid.org/0000-0002-4550-3950"},"institutions":[{"id":"https://openalex.org/I4210155967","display_name":"OriginWater (China)","ror":"https://ror.org/04h7gmn81","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210155967"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shisong Tang","raw_affiliation_strings":["KuaiShou Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"KuaiShou Inc., Beijing, China","institution_ids":["https://openalex.org/I4210155967"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048556378","display_name":"Fan Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4210155967","display_name":"OriginWater (China)","ror":"https://ror.org/04h7gmn81","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210155967"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Li","raw_affiliation_strings":["KuaiShou Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"KuaiShou Inc., Beijing, China","institution_ids":["https://openalex.org/I4210155967"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101364458","display_name":"Bing Han","orcid":"https://orcid.org/0000-0003-2838-2824"},"institutions":[{"id":"https://openalex.org/I4210155967","display_name":"OriginWater (China)","ror":"https://ror.org/04h7gmn81","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210155967"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bing Han","raw_affiliation_strings":["KuaiShou Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"KuaiShou Inc., Beijing, China","institution_ids":["https://openalex.org/I4210155967"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089100173","display_name":"Huafeng Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huafeng Cao","raw_affiliation_strings":["Kuaishou Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Kuaishou Inc., Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012051562","display_name":"Jiechao Gao","orcid":"https://orcid.org/0000-0003-0628-1416"},"institutions":[{"id":"https://openalex.org/I1743320","display_name":"Palo Alto University","ror":"https://ror.org/04f812k67","country_code":"US","type":"education","lineage":["https://openalex.org/I1743320"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiechao Gao","raw_affiliation_strings":["Center for SDGC, Stanford University, Palo Alto, CA, USA"],"affiliations":[{"raw_affiliation_string":"Center for SDGC, Stanford University, Palo Alto, CA, USA","institution_ids":["https://openalex.org/I1743320"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108294333","display_name":"Hechang Chen","orcid":"https://orcid.org/0000-0001-7835-9556"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]},{"id":"https://openalex.org/I4210136497","display_name":"Jilin Medical University","ror":"https://ror.org/03mzw7781","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136497"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hechang Chen","raw_affiliation_strings":["Jilin University, Jilin, China"],"affiliations":[{"raw_affiliation_string":"Jilin University, Jilin, China","institution_ids":["https://openalex.org/I4210136497","https://openalex.org/I194450716"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5100419187"],"corresponding_institution_ids":["https://openalex.org/I204250578"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.32350645,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5129","last_page":"5138"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9980999827384949,"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":0.9980999827384949,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9973999857902527,"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/T11309","display_name":"Music and Audio Processing","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.7462341785430908},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.42670461535453796},{"id":"https://openalex.org/keywords/multimedia","display_name":"Multimedia","score":0.3463786840438843},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.2765369117259979}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7462341785430908},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.42670461535453796},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.3463786840438843},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2765369117259979}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3711896.3737276","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737276","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737276","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3711896.3737276","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737276","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737276","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412876972.pdf","grobid_xml":"https://content.openalex.org/works/W4412876972.grobid-xml"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W2040367556","https://openalex.org/W2512971201","https://openalex.org/W2730106296","https://openalex.org/W2883725317","https://openalex.org/W2997279057","https://openalex.org/W3087124270","https://openalex.org/W3180355996","https://openalex.org/W4200573943","https://openalex.org/W4224318508","https://openalex.org/W4233762729","https://openalex.org/W4290857499","https://openalex.org/W4290944246","https://openalex.org/W4292423901","https://openalex.org/W4300672471","https://openalex.org/W4306317504","https://openalex.org/W4385562485","https://openalex.org/W4385565675","https://openalex.org/W4385965604","https://openalex.org/W4390872598","https://openalex.org/W4392607719","https://openalex.org/W4393147762","https://openalex.org/W4396833508","https://openalex.org/W4399695571","https://openalex.org/W4401864223","https://openalex.org/W4402404329","https://openalex.org/W6680539271","https://openalex.org/W6849373658"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","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"],"abstract_inverted_index":{"In":[0],"video":[1],"recommendation":[2],"systems,":[3],"accurately":[4],"predicting":[5],"watch":[6,89,121,179,216],"time":[7,180,217],"is":[8],"crucial":[9],"for":[10,178],"enhancing":[11],"user":[12,104],"engagement":[13],"and":[14,82,142,148,195],"retention.":[15],"Traditional":[16],"methods":[17],"typically":[18],"apply":[19],"label":[20,72],"transformations":[21],"or":[22],"mitigate":[23],"duration":[24,93,134],"bias":[25],"to":[26,117,153,163],"improve":[27],"performance":[28],"but":[29],"overlook":[30],"that":[31,65,88,127],"erroneous":[32],"instance":[33,67,140,176],"representations":[34,68,141,177],"are":[35],"the":[36,77,100,119,158,210],"primary":[37],"cause":[38],"of":[39,103,213],"significant":[40],"prediction":[41,63,181],"errors.":[42],"Moreover,":[43],"these":[44,56,129],"approaches":[45],"predominantly":[46],"rely":[47],"on":[48,107,192,200],"point":[49],"perdition,":[50],"limiting":[51],"their":[52],"robustness.":[53],"To":[54],"address":[55],"challenges,":[57],"we":[58,110,137,173],"propose":[59],"LDA,":[60],"a":[61,145,201],"novel":[62],"paradigm":[64],"optimizes":[66],"by":[69,182],"explicitly":[70],"leveraging":[71],"distributions":[73],"as":[74],"anchors":[75,126,143],"within":[76,132,167],"model,":[78],"enabling":[79],"more":[80],"accurate":[81],"robust":[83],"predictions.":[84],"Our":[85],"analysis":[86],"reveals":[87],"ratio":[90,122],"across":[91],"different":[92],"groups":[94],"exhibit":[95],"distinct":[96],"multi-peak":[97,130],"distributions,":[98],"reflecting":[99],"strong":[101],"aggregation":[102],"behavior.":[105],"Based":[106],"this":[108,168],"finding,":[109],"employ":[111],"Vector":[112],"Quantized":[113],"Variational":[114],"Auto-encoder":[115],"(VQ-VAE)":[116],"convert":[118],"continuous":[120],"distribution":[123],"into":[124,144],"representative":[125],"capture":[128],"characteristics":[131],"each":[133],"group.":[135],"Subsequently,":[136],"project":[138],"both":[139],"common":[146],"space":[147,169],"utilize":[149],"Optimal":[150],"Transport":[151],"(OT)":[152],"generate":[154],"pseudo-labels":[155],"aligned":[156],"with":[157,204],"anchor":[159,184],"distribution,":[160],"allowing":[161],"instances":[162],"obtain":[164],"structured":[165],"coordinates":[166],"during":[170],"training.":[171],"Finally,":[172],"derive":[174],"optimized":[175],"aggregating":[183],"vectors":[185],"through":[186],"weighted":[187],"integration.":[188],"Extensive":[189],"offline":[190],"experiments":[191],"two":[193],"datasets":[194],"large-scale":[196],"online":[197],"A/B":[198],"testing":[199],"short-video":[202],"platform":[203],"over":[205],"300":[206],"million":[207],"DAUs":[208],"demonstrate":[209],"consistent":[211],"superiority":[212],"LDA":[214],"in":[215],"prediction.":[218]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}