{"id":"https://openalex.org/W4412877037","doi":"https://doi.org/10.1145/3711896.3737193","title":"Applying Large Language Model For Relevance Search In Tencent","display_name":"Applying Large Language Model For Relevance Search In Tencent","publication_year":2025,"publication_date":"2025-08-03","ids":{"openalex":"https://openalex.org/W4412877037","doi":"https://doi.org/10.1145/3711896.3737193"},"language":"en","primary_location":{"id":"doi:10.1145/3711896.3737193","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737193","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737193","source":null,"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","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/10.1145/3711896.3737193","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003404272","display_name":"Dezhi Ye","orcid":"https://orcid.org/0000-0001-9776-2404"},"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":"Dezhi Ye","raw_affiliation_strings":["Tencent, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9776-2404","affiliations":[{"raw_affiliation_string":"Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088157651","display_name":"J. Liu","orcid":"https://orcid.org/0009-0009-3791-5005"},"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":"Jie Liu","raw_affiliation_strings":["Tencent, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0009-3791-5005","affiliations":[{"raw_affiliation_string":"Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Junwei Hu","orcid":"https://orcid.org/0009-0005-9037-8537"},"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":"Junwei Hu","raw_affiliation_strings":["Tencent, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0005-9037-8537","affiliations":[{"raw_affiliation_string":"Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062052083","display_name":"Jiabin Fan","orcid":"https://orcid.org/0009-0004-8816-3767"},"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":"Jiabin Fan","raw_affiliation_strings":["Tencent, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0004-8816-3767","affiliations":[{"raw_affiliation_string":"Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103038516","display_name":"Bowen Tian","orcid":"https://orcid.org/0009-0006-8426-6448"},"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":"Bowen Tian","raw_affiliation_strings":["Tencent, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0006-8426-6448","affiliations":[{"raw_affiliation_string":"Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048582949","display_name":"Haijin Liang","orcid":"https://orcid.org/0009-0006-3464-9192"},"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":"Haijin Liang","raw_affiliation_strings":["Tencent, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0006-3464-9192","affiliations":[{"raw_affiliation_string":"Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100663186","display_name":"Jin Ma","orcid":"https://orcid.org/0009-0005-5837-6144"},"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":"Jin Ma","raw_affiliation_strings":["Tencent, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0005-5837-6144","affiliations":[{"raw_affiliation_string":"Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7588,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.87723374,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"5171","last_page":"5181"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9979000091552734,"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":0.9979000091552734,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9957000017166138,"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/T10215","display_name":"Semantic Web and Ontologies","score":0.9921000003814697,"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/relevance","display_name":"Relevance (law)","score":0.7691240906715393},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7290320992469788},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4569774568080902},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.44691193103790283},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4400065243244171},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.372550904750824},{"id":"https://openalex.org/keywords/political-science","display_name":"Political science","score":0.10826200246810913}],"concepts":[{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.7691240906715393},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7290320992469788},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4569774568080902},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.44691193103790283},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4400065243244171},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.372550904750824},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.10826200246810913},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3711896.3737193","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737193","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737193","source":null,"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","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:10.1145/3711896.3737193","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737193","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737193","source":null,"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","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":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.6399999856948853}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412877037.pdf","grobid_xml":"https://content.openalex.org/works/W4412877037.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W1598033630","https://openalex.org/W2513853793","https://openalex.org/W3045033475","https://openalex.org/W3105107530","https://openalex.org/W3134665270","https://openalex.org/W3170841641","https://openalex.org/W3189117283","https://openalex.org/W4380353763","https://openalex.org/W4385688511","https://openalex.org/W4389523765","https://openalex.org/W4400526199","https://openalex.org/W4400527956","https://openalex.org/W4400528754","https://openalex.org/W4401043313","https://openalex.org/W4401044046","https://openalex.org/W4401863583","https://openalex.org/W4402670051","https://openalex.org/W4402670856","https://openalex.org/W4404782892","https://openalex.org/W4409670826","https://openalex.org/W6852874933"],"related_works":["https://openalex.org/W2085384747","https://openalex.org/W2088166309","https://openalex.org/W1891216533","https://openalex.org/W1967370444","https://openalex.org/W2150136235","https://openalex.org/W2085215424","https://openalex.org/W2088814244","https://openalex.org/W2157408137","https://openalex.org/W4312252109","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Relevance":[0],"plays":[1],"a":[2,104,148,156,162],"crucial":[3],"role":[4],"in":[5,81,115,124,155,171,189,200],"commercial":[6],"search":[7,19,51,83,204,225,242],"engines":[8],"by":[9,193],"identifying":[10],"documents":[11],"related":[12],"to":[13,32,49,108,122,165,175,179,184],"user":[14],"queries":[15],"and":[16,75,134,151,210,215],"fulfilling":[17],"their":[18,46],"needs.Traditional":[20],"approaches":[21],"employ":[22],"encoder-only":[23],"models":[24,39,178],"like":[25],"BERT,":[26],"which":[27,85],"process":[28],"concatenated":[29],"query-document":[30,116],"pairs":[31],"predict":[33],"relevance":[34,59,117,190],"scores.While":[35],"autoregressive":[36],"large":[37],"language":[38],"(LLMs)":[40],"have":[41,64],"revolutionized":[42],"numerous":[43],"NLP":[44],"domains,":[45],"direct":[47],"application":[48],"web-scale":[50],"systems":[52],"presents":[53],"significant":[54],"challenges.On":[55],"one":[56],"hand,":[57],"the":[58,69,71,111,135,142,167,172,187],"modeling":[60],"capabilities":[61,168],"of":[62,113,137,169],"LLMs":[63,80,114,123,154,170,199,239],"not":[65],"been":[66],"fully":[67,185],"explored.On":[68],"other,":[70],"high":[72],"computational":[73],"costs":[74],"inference":[76],"times":[77],"make":[78],"deploying":[79,182],"online":[82,216],"systems,":[84],"demand":[86],"extremely":[87],"low":[88],"latency,":[89],"nearly":[90],"impossible.In":[91],"this":[92],"work,":[93],"we":[94,102,140,160,195],"address":[95],"these":[96],"challenges":[97],"through":[98],"two":[99],"key":[100],"contributions.First,":[101],"develop":[103,152],"comprehensive":[105],"evaluation":[106],"framework":[107,164],"systematically":[109],"assess":[110],"effectiveness":[112],"ranking.By":[118],"conducting":[119],"assessment":[120],"experiments":[121],"four":[125],"perspectives:":[126],"ranking":[127,173,191],"objectives,":[128],"model":[129],"size,":[130],"domain-specific":[131],"continuous":[132],"pre-training,":[133],"integration":[136],"prior":[138],"knowledge,":[139],"identify":[141],"best":[143],"resource":[144],"allocation":[145],"strategy":[146],"given":[147],"restricted":[149],"budget":[150],"practical":[153,230],"more":[157],"efficient":[158],"way.Second,":[159],"propose":[161],"novel":[163],"transfer":[166],"aspect":[174],"existing":[176],"BERT":[177],"avoid":[180],"directly":[181],"LLMs.Finally,":[183],"leverage":[186],"improvements":[188],"brought":[192],"LLMs,":[194],"successfully":[196],"nearline":[197],"deploy":[198],"Tencent":[201],"QQ":[202],"Browser":[203],"engine":[205,226],"using":[206],"query-based":[207],"ondemand":[208],"computing":[209],"quantization.Experiments":[211],"on":[212],"real-world":[213],"datasets":[214],"A/B":[217],"tests":[218],"demonstrate":[219],"that":[220],"our":[221],"approach":[222],"significantly":[223],"enhances":[224],"performance":[227],"while":[228],"maintaining":[229],"operational":[231],"efficiency.Our":[232],"findings":[233],"provide":[234],"actionable":[235],"insights":[236],"for":[237],"integrating":[238],"into":[240],"production":[241],"engines.":[243]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}