{"id":"https://openalex.org/W7128701037","doi":"https://doi.org/10.48448/r2nt-td25","title":"CroPS: Improving Dense Retrieval with Cross-Perspective Positive Samples in Short-Video Search","display_name":"CroPS: Improving Dense Retrieval with Cross-Perspective Positive Samples in Short-Video Search","publication_year":2026,"publication_date":"2026-01-07","ids":{"openalex":"https://openalex.org/W7128701037","doi":"https://doi.org/10.48448/r2nt-td25"},"language":"en","primary_location":{"id":"doi:10.48448/r2nt-td25","doi":"https://doi.org/10.48448/r2nt-td25","is_oa":true,"landing_page_url":"https://doi.org/10.48448/r2nt-td25","pdf_url":null,"source":{"id":"https://openalex.org/S7407053148","display_name":"Underline Science Inc.","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Audiovisual"},"volume":null,"type":"other","is_traditional":false,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48448/r2nt-td25","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125741655","display_name":"Association for Artificial Intelligence 2026","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Association for Artificial Intelligence 2026","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125743505","display_name":"Jiahui Chen","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jiahui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042480513","display_name":"Xiaoze Jiang","orcid":"https://orcid.org/0000-0002-5463-7176","observed_orcids":["https://orcid.org/0000-0002-5463-7176","https://orcid.org/0009-0002-0327-320X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Xiaoze","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125741592","display_name":"Han Li","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Han","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130161178","display_name":"Zhiheng Qin","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qin, Zhiheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125767021","display_name":"Ao Xie","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Ao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125696824","display_name":"Yun Yu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Yun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5089477538","display_name":"Quanzhi Zhu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Quanzhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9810000061988831,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9810000061988831,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.010999999940395355,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.004900000058114529,"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/dense-retrieval","display_name":"dense retrieval","score":0.9739999771118164},{"id":"https://openalex.org/keywords/filter-bubbles","display_name":"filter bubbles","score":0.7559999823570251},{"id":"https://openalex.org/keywords/query-reformulation","display_name":"query rewriting","score":0.8920000195503235},{"id":"https://openalex.org/keywords/infonce-loss","display_name":"InfoNCE loss","score":0.9810000061988831}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7730000019073486},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.7160000205039978},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5612999796867371},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5105000138282776},{"id":"https://openalex.org/C99016210","wikidata":"https://www.wikidata.org/wiki/Q5488129","display_name":"Query expansion","level":2,"score":0.5001000165939331},{"id":"https://openalex.org/C97854310","wikidata":"https://www.wikidata.org/wiki/Q19541","display_name":"Search engine","level":2,"score":0.4724000096321106},{"id":"https://openalex.org/C164120249","wikidata":"https://www.wikidata.org/wiki/Q995982","display_name":"Web search query","level":3,"score":0.40380001068115234},{"id":"https://openalex.org/C192028432","wikidata":"https://www.wikidata.org/wiki/Q845739","display_name":"Query language","level":2,"score":0.4034000039100647},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.3734000027179718},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35929998755455017},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3587999939918518},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.34450000524520874},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.3411000072956085},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3127000033855438},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2549000084400177}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48448/r2nt-td25","doi":"https://doi.org/10.48448/r2nt-td25","is_oa":true,"landing_page_url":"https://doi.org/10.48448/r2nt-td25","pdf_url":null,"source":{"id":"https://openalex.org/S7407053148","display_name":"Underline Science Inc.","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Audiovisual"}],"best_oa_location":{"id":"doi:10.48448/r2nt-td25","doi":"https://doi.org/10.48448/r2nt-td25","is_oa":true,"landing_page_url":"https://doi.org/10.48448/r2nt-td25","pdf_url":null,"source":{"id":"https://openalex.org/S7407053148","display_name":"Underline Science Inc.","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Audiovisual"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7128697768","https://openalex.org/W4403707145","https://openalex.org/W7168976039","https://openalex.org/W7141607846","https://openalex.org/W4414198960","https://openalex.org/W4398147798","https://openalex.org/W3154898636","https://openalex.org/W7211867647","https://openalex.org/W4416355185","https://openalex.org/W6891754166"],"abstract_inverted_index":{"Dense":[0],"retrieval":[1,74,171],"has":[2],"become":[3],"a":[4,20,38,72,129,136,149,184],"foundational":[5],"paradigm":[6,34],"in":[7,12,108,165,183],"modern":[8],"search":[9,153],"systems,":[10],"especially":[11],"short-video":[13,152],"platforms.":[14],"However,":[15],"most":[16],"industrial":[17],"systems":[18],"adopt":[19],"self-reinforcing":[21],"training":[22,53,95],"pipeline":[23],"that":[24,140,156],"relies":[25],"on":[26,148],"historically":[27],"exposed":[28],"user":[29,101],"interactions":[30],"for":[31],"supervision.":[32],"This":[33],"inevitably":[35],"leads":[36],"to":[37,78],"filter":[39],"bubble":[40],"effect,":[41],"where":[42],"potentially":[43],"relevant":[44],"but":[45],"previously":[46],"unseen":[47],"content":[48],"is":[49,179],"excluded":[50],"from":[51,90,100],"the":[52,56],"signal,":[54],"biasing":[55],"model":[57],"toward":[58],"narrow":[59],"and":[60,85,112,135,164,173],"conservative":[61],"retrieval.":[62],"In":[63],"this":[64,80],"paper,":[65],"we":[66,127],"present":[67],"CroPS":[68,93,157,178],"(Cross-Perspective":[69],"Positive":[70],"Samples),":[71],"novel":[73],"data":[75,107],"engine":[76],"designed":[77],"alleviate":[79],"problem":[81],"by":[82,116],"introducing":[83],"diverse":[84],"semantically":[86],"meaningful":[87],"positive":[88,97],"examples":[89],"multiple":[91],"perspectives.":[92],"enhances":[94],"with":[96],"signals":[98],"derived":[99],"query":[102,175],"reformulation":[103,176],"behavior":[104],"(query-level),":[105],"engagement":[106],"recommendation":[109],"streams":[110],"(system-level),":[111],"world":[113],"knowledge":[114],"synthesized":[115],"large":[117],"language":[118],"models":[119],"(knowledge-level).":[120],"To":[121],"effectively":[122],"utilize":[123],"these":[124],"heterogeneous":[125],"signals,":[126],"introduce":[128],"Hierarchical":[130],"Label":[131],"Assignment":[132],"(HLA)":[133],"strategy":[134],"corresponding":[137],"H-InfoNCE":[138],"loss":[139],"together":[141],"enable":[142],"fine-grained,":[143],"relevance-aware":[144],"optimization.":[145],"Extensive":[146],"experiments":[147],"large-scale":[150],"commercial":[151],"platform":[154],"demonstrate":[155],"significantly":[158],"outperforms":[159],"strong":[160],"baselines":[161],"both":[162],"offline":[163],"live":[166],"A/B":[167],"tests,":[168],"achieving":[169],"superior":[170],"performance":[172],"reducing":[174],"rates.":[177],"now":[180],"fully":[181],"deployed":[182],"production":[185],"system,":[186],"serving":[187],"hundreds":[188],"of":[189,191],"millions":[190],"users":[192],"daily.":[193]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-02-13T00:00:00"}
