{"id":"https://openalex.org/W4206404363","doi":"https://doi.org/10.1109/bigdata52589.2021.9671546","title":"GLOW : Global Weighted Self-Attention Network for Web Search","display_name":"GLOW : Global Weighted Self-Attention Network for Web Search","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4206404363","doi":"https://doi.org/10.1109/bigdata52589.2021.9671546"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671546","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671546","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"article","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/A5086333675","display_name":"Xuan Shan","orcid":"https://orcid.org/0000-0002-9349-1341"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xuan Shan","raw_affiliation_strings":["Kuaishou Beijing, China"],"affiliations":[{"raw_affiliation_string":"Kuaishou Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116294179","display_name":"Chuanjie Liu","orcid":"https://orcid.org/0009-0004-5183-8150"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuanjie Liu","raw_affiliation_strings":["Microsoft Beijing, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068540488","display_name":"Yiqian Xia","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiqian Xia","raw_affiliation_strings":["Microsoft Beijing, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071396780","display_name":"Qi Chen","orcid":"https://orcid.org/0000-0003-3103-162X"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Chen","raw_affiliation_strings":["Microsoft Beijing, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101442304","display_name":"Yusi Zhang","orcid":"https://orcid.org/0000-0001-8525-0946"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yusi Zhang","raw_affiliation_strings":["Bytedance Beijing, China"],"affiliations":[{"raw_affiliation_string":"Bytedance Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044455276","display_name":"Kaize Ding","orcid":"https://orcid.org/0000-0001-6684-6752"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kaize Ding","raw_affiliation_strings":["Arizona State University Tempe, US"],"affiliations":[{"raw_affiliation_string":"Arizona State University Tempe, US","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100678419","display_name":"Yaobo Liang","orcid":"https://orcid.org/0000-0002-6595-5145"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaobo Liang","raw_affiliation_strings":["Microsoft Beijing, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110554894","display_name":"Angen Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Angen Luo","raw_affiliation_strings":["Microsoft Beijing, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101140673","display_name":"Yuxiang Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxiang Luo","raw_affiliation_strings":["Microsoft Beijing, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5086333675"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6283,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.71676949,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"519","last_page":"528"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9991999864578247,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9962000250816345,"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":0.8543436527252197},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5412854552268982},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5371462106704712},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5111287236213684},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.49556100368499756},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.49407437443733215},{"id":"https://openalex.org/keywords/semantic-search","display_name":"Semantic search","score":0.4899729788303375},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.46161919832229614},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.41661399602890015},{"id":"https://openalex.org/keywords/search-engine","display_name":"Search engine","score":0.3210611641407013},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.16639012098312378}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8543436527252197},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5412854552268982},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5371462106704712},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5111287236213684},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.49556100368499756},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.49407437443733215},{"id":"https://openalex.org/C166423231","wikidata":"https://www.wikidata.org/wiki/Q1891170","display_name":"Semantic search","level":3,"score":0.4899729788303375},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.46161919832229614},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.41661399602890015},{"id":"https://openalex.org/C97854310","wikidata":"https://www.wikidata.org/wiki/Q19541","display_name":"Search engine","level":2,"score":0.3210611641407013},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.16639012098312378},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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:10.1109/bigdata52589.2021.9671546","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671546","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W2069870183","https://openalex.org/W2085030399","https://openalex.org/W2097732278","https://openalex.org/W2136189984","https://openalex.org/W2165613971","https://openalex.org/W2186845332","https://openalex.org/W2194775991","https://openalex.org/W2341132943","https://openalex.org/W2536015822","https://openalex.org/W2757662681","https://openalex.org/W2768459074","https://openalex.org/W2794557536","https://openalex.org/W2896457183","https://openalex.org/W2911529999","https://openalex.org/W2923890923","https://openalex.org/W2937036051","https://openalex.org/W2938224028","https://openalex.org/W2951359136","https://openalex.org/W2951534261","https://openalex.org/W2952866723","https://openalex.org/W2965373594","https://openalex.org/W2970103342","https://openalex.org/W2970597249","https://openalex.org/W2982096936","https://openalex.org/W2982596739","https://openalex.org/W3016129867","https://openalex.org/W3021397474","https://openalex.org/W3118668786","https://openalex.org/W3216404684","https://openalex.org/W4244536387","https://openalex.org/W4300427681","https://openalex.org/W4385245566","https://openalex.org/W6628905179","https://openalex.org/W6631190155","https://openalex.org/W6674387193","https://openalex.org/W6685160515","https://openalex.org/W6739901393","https://openalex.org/W6749879876","https://openalex.org/W6755207826","https://openalex.org/W6758015726","https://openalex.org/W6758528834","https://openalex.org/W6760359991","https://openalex.org/W6761345506","https://openalex.org/W6761452495","https://openalex.org/W6763701032","https://openalex.org/W6764357534","https://openalex.org/W6766673545","https://openalex.org/W6769185743","https://openalex.org/W6769412140","https://openalex.org/W6770118208","https://openalex.org/W6779872132","https://openalex.org/W6780226713"],"related_works":["https://openalex.org/W2359166167","https://openalex.org/W3590553","https://openalex.org/W3110844189","https://openalex.org/W1976839151","https://openalex.org/W2336826532","https://openalex.org/W3040185272","https://openalex.org/W2373953901","https://openalex.org/W2214614887","https://openalex.org/W2348367558","https://openalex.org/W80083115"],"abstract_inverted_index":{"Deep":[0],"matching":[1,30,109],"models":[2,170],"aim":[3],"to":[4,51,62,73,148,161,172,182,200,223],"facilitate":[5],"search":[6,175],"engines":[7],"retrieving":[8],"more":[9,198],"relevant":[10],"documents":[11,16,184],"by":[12,240],"mapping":[13],"queries":[14,209],"and":[15,134,204,210,214,236],"into":[17,106,115,154],"semantic":[18,205],"vectors":[19],"in":[20,67,208],"the":[21,28,32,53,107,202,246],"first-stage":[22],"retrieval.":[23],"When":[24],"leveraging":[25],"BERT":[26,75,235],"as":[27],"deep":[29,108],"model,":[31],"attention":[33,78,86,116,127],"score":[34],"across":[35,79],"two":[36],"words":[37],"are":[38],"solely":[39],"built":[40],"upon":[41],"local":[42],"contextualized":[43],"word":[44,85,144,152,164],"embeddings.":[45],"It":[46,158,232],"lacks":[47],"prior":[48,113,150],"global":[49,103,119],"knowledge":[50,153],"distinguish":[52],"importance":[54],"of":[55,192],"different":[56],"words,":[57],"which":[58,82],"has":[59],"been":[60],"proved":[61],"play":[63],"a":[64,90,225,241],"critical":[65],"role":[66],"information":[68],"retrieval":[69],"tasks.":[70],"In":[71],"addition":[72],"this,":[74],"only":[76],"performs":[77],"sub-words":[80,155],"tokens":[81],"weakens":[83],"whole":[84,143,151,163],"representation.":[87],"We":[88,138,194],"propose":[89],"novel":[91],"Global":[92],"Weighted":[93],"Self-Attention":[94],"(GLOW)":[95],"network":[96],"for":[97,228],"web":[98,174],"document":[99,229],"search.":[100],"GLOW":[101,123,196,222],"fuses":[102],"corpus":[104],"statistics":[105],"model.":[110],"By":[111],"adding":[112],"weights":[114],"generation":[117],"from":[118],"information,":[120],"like":[121],"BM25,":[122],"successfully":[124],"learns":[125],"weighted":[126],"scores":[128],"jointly":[129],"with":[130,185,189,250],"query":[131],"matrix":[132,136],"Q":[133],"key":[135],"K.":[137],"also":[139],"present":[140],"an":[141],"efficient":[142,199],"weight":[145],"sharing":[146],"solution":[147],"bring":[149],"level":[156,165],"attention.":[157,166],"aids":[159],"Transformer":[160],"learn":[162],"To":[167],"make":[168],"our":[169],"applicable":[171],"complicated":[173],"scenarios,":[176],"we":[177],"introduce":[178],"combined":[179],"fields":[180,187],"representation":[181,206],"accommodate":[183],"multiple":[186],"even":[188],"variable":[190],"number":[191],"instances.":[193],"demonstrate":[195],"is":[197,255],"capture":[201],"topical":[203],"both":[207],"documents.":[211],"Intrinsic":[212],"evaluation":[213],"experiments":[215],"conducted":[216],"on":[217],"public":[218],"data":[219],"sets":[220],"reveal":[221],"be":[224],"general":[226],"framework":[227],"retrieve":[230],"task.":[231],"significantly":[233],"outperforms":[234],"other":[237],"competitive":[238],"baselines":[239],"large":[242],"margin":[243],"while":[244],"retaining":[245],"same":[247],"model":[248],"complexity":[249],"BERT.":[251],"The":[252],"source":[253],"code":[254],"available":[256],"at":[257],"https://github.com/GLOW-deep/GLOW.":[258]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}
