{"id":"https://openalex.org/W2768024336","doi":"https://doi.org/10.1145/3132847.3132865","title":"Latency Reduction via Decision Tree Based Query Construction","display_name":"Latency Reduction via Decision Tree Based Query Construction","publication_year":2017,"publication_date":"2017-11-06","ids":{"openalex":"https://openalex.org/W2768024336","doi":"https://doi.org/10.1145/3132847.3132865","mag":"2768024336"},"language":"en","primary_location":{"id":"doi:10.1145/3132847.3132865","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3132847.3132865","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","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/A5022334611","display_name":"Aman Grover","orcid":null},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Aman Grover","raw_affiliation_strings":["Linkedin Corporation, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Linkedin Corporation, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025883250","display_name":"Dhruv Arya","orcid":null},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dhruv Arya","raw_affiliation_strings":["Linkedin, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Linkedin, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101577886","display_name":"Ganesh Venkataraman","orcid":"https://orcid.org/0009-0005-4007-8309"},"institutions":[{"id":"https://openalex.org/I106110158","display_name":"Bay Area Air Quality Management District","ror":"https://ror.org/04431t173","country_code":"US","type":"government","lineage":["https://openalex.org/I106110158"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ganesh Venkataraman","raw_affiliation_strings":["Airbnb, San Francisco, CA, USA"],"affiliations":[{"raw_affiliation_string":"Airbnb, San Francisco, CA, USA","institution_ids":["https://openalex.org/I106110158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5022334611"],"corresponding_institution_ids":["https://openalex.org/I1316064682"],"apc_list":null,"apc_paid":null,"fwci":1.4509,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.87442281,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1399","last_page":"1407"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9991999864578247,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9991999864578247,"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":0.9976000189781189,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9902999997138977,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.7919999361038208},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.6768823266029358},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5071964263916016},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.4256865978240967},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40605005621910095},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35184288024902344},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32695186138153076}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7919999361038208},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.6768823266029358},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5071964263916016},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.4256865978240967},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40605005621910095},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35184288024902344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32695186138153076},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3132847.3132865","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3132847.3132865","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.6399999856948853}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W410850256","https://openalex.org/W1551408939","https://openalex.org/W1978196964","https://openalex.org/W1991360400","https://openalex.org/W2066636486","https://openalex.org/W2080770071","https://openalex.org/W2091158010","https://openalex.org/W2093270409","https://openalex.org/W2108653447","https://openalex.org/W2112941141","https://openalex.org/W2120165387","https://openalex.org/W2135050452","https://openalex.org/W2149427297","https://openalex.org/W2149706766","https://openalex.org/W2151361488","https://openalex.org/W2154610494","https://openalex.org/W2512971201","https://openalex.org/W2515120505","https://openalex.org/W2515483217","https://openalex.org/W2516369484","https://openalex.org/W2597289420","https://openalex.org/W2963891150","https://openalex.org/W6614148910"],"related_works":["https://openalex.org/W3204641204","https://openalex.org/W1470425429","https://openalex.org/W4283016678","https://openalex.org/W4249746146","https://openalex.org/W4318350883","https://openalex.org/W4205958290","https://openalex.org/W3200719183","https://openalex.org/W4328134586","https://openalex.org/W4306321456","https://openalex.org/W4200196661"],"abstract_inverted_index":{"LinkedIn":[0,202],"as":[1],"a":[2,27,51,66,90,138],"professional":[3],"network":[4],"serves":[5],"the":[6,23,73,80,99,106,109,118,122,126,142,153,161,180,183,194,253],"career":[7],"needs":[8],"of":[9,16,29,95,133,182,251,257],"450":[10],"Million":[11],"plus":[12],"members.":[13],"The":[14,150],"task":[15],"job":[17,25,48,74,197,219,240,258],"recommendation":[18],"system":[19],"is":[20,114],"to":[21,88,97,140,168,188,244],"nd":[22],"suitable":[24,47],"among":[26],"corpus":[28,110],"several":[30,93],"million":[31],"jobs":[32,107],"and":[33,54,62,159,190,199,203,213,225,260],"serve":[34],"this":[35],"in":[36,72,79,85,108,210,237],"real":[37,169],"time":[38,170],"under":[39],"tight":[40],"latency":[41,119,189,221,228],"constraints.":[42,120],"Job":[43],"search":[44,61,198,220,259],"involves":[45,64],"nding":[46],"listings":[49],"given":[50,117],"user,":[52],"query":[53,157],"context.":[55],"Typical":[56],"scoring":[57],"function":[58,67,91,146],"for":[59,111],"both":[60],"recommendations":[63,200,227,241,261],"evaluating":[65,89,104],"that":[68],"matches":[69],"various":[70,77],"elds":[71,78],"description":[75],"with":[76,92,186],"member":[81,211],"pro":[82],"le.":[83],"This":[84],"turn":[86],"translates":[87],"thousands":[94],"features":[96],"get":[98],"right":[100],"ranking.":[101],"In":[102],"recommendations,":[103],"all":[105,112,256],"members":[113],"not":[115],"possible":[116],"On":[121],"other":[123],"hand,":[124],"reducing":[125],"candidate":[127],"set":[128],"could":[129],"potentially":[130],"involve":[131],"loss":[132],"relevant":[134],"jobs.":[135],"We":[136,172,192],"present":[137],"way":[139],"model":[141],"underlying":[143],"complex":[144],"ranking":[145],"via":[147],"decision":[148,154,162,184],"trees.":[149],"branches":[151],"within":[152],"trees":[155,163],"are":[156],"clauses":[158],"hence":[160],"can":[164],"be":[165],"mapped":[166],"on":[167,196,201,262],"queries.":[171],"developed":[173],"an":[174],"o":[175],"ine":[176],"framework":[177],"which":[178],"evaluates":[179],"quality":[181],"tree":[185],"respect":[187],"recall.":[191],"tested":[193],"approach":[195,254],"A/B":[204],"tests":[205],"show":[206,234],"signi":[207],"cant":[208],"improvements":[209],"engagement":[212],"latency.":[214],"Our":[215,232],"techniques":[216,233],"helped":[217],"reduce":[218],"by":[222,229],"over":[223,230],"67%":[224],"our":[226],"55%.":[231],"3.5%":[235],"improvement":[236],"applications":[238],"from":[239,247],"primarily":[242],"due":[243],"reduced":[245],"timeouts":[246],"upstream":[248],"services.":[249],"As":[250],"writing":[252],"powers":[255],"LinkedIn.":[263]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
