{"id":"https://openalex.org/W4400531831","doi":"https://doi.org/10.1145/3626772.3657982","title":"Gen-IR @ SIGIR 2024: The Second Workshop on Generative Information Retrieval","display_name":"Gen-IR @ SIGIR 2024: The Second Workshop on Generative Information Retrieval","publication_year":2024,"publication_date":"2024-07-10","ids":{"openalex":"https://openalex.org/W4400531831","doi":"https://doi.org/10.1145/3626772.3657982"},"language":"en","primary_location":{"id":"doi:10.1145/3626772.3657982","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3626772.3657982","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval","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/A5031110596","display_name":"G. F. Benedict","orcid":"https://orcid.org/0000-0002-3596-0285"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Gabriel B\u00e9n\u00e9dict","raw_affiliation_strings":["Amazon, Madrid, Spain"],"affiliations":[{"raw_affiliation_string":"Amazon, Madrid, Spain","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009898523","display_name":"Ruqing Zhang","orcid":"https://orcid.org/0000-0003-4294-2541"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruqing Zhang","raw_affiliation_strings":["ICT, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"ICT, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000115067","display_name":"Donald Metzler","orcid":"https://orcid.org/0000-0003-4276-6269"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Donald Metzler","raw_affiliation_strings":["Google Research, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Google Research, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059489981","display_name":"Andrew Yates","orcid":"https://orcid.org/0000-0002-5970-880X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andrew Yates","raw_affiliation_strings":["University of Amsterdam, Amsterdam, USA"],"affiliations":[{"raw_affiliation_string":"University of Amsterdam, Amsterdam, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038982792","display_name":"Ziyan Jiang","orcid":"https://orcid.org/0009-0005-4955-0737"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]},{"id":"https://openalex.org/I4210108985","display_name":"Bellevue Hospital Center","ror":"https://ror.org/01ky34z31","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1283621791","https://openalex.org/I4210086933","https://openalex.org/I4210108985"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ziyan Jiang","raw_affiliation_strings":["Amazon, Bellevue, WA, USA"],"affiliations":[{"raw_affiliation_string":"Amazon, Bellevue, WA, USA","institution_ids":["https://openalex.org/I1311688040","https://openalex.org/I4210108985"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5031110596"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3475,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.64158996,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"3029","last_page":"3032"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.996399998664856,"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":0.996399998664856,"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/T11269","display_name":"Algorithms and Data Compression","score":0.9912999868392944,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9883000254631042,"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":0.7845859527587891},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.6164831519126892},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5683212876319885},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4350985288619995},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34391453862190247}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7845859527587891},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.6164831519126892},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5683212876319885},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4350985288619995},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34391453862190247}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3626772.3657982","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3626772.3657982","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2981852735","https://openalex.org/W2983537304","https://openalex.org/W3185146124","https://openalex.org/W4285818639","https://openalex.org/W4288089799","https://openalex.org/W4292215729","https://openalex.org/W4311731003","https://openalex.org/W4382540082","https://openalex.org/W4384636838","https://openalex.org/W4384644330","https://openalex.org/W4389520743","https://openalex.org/W4390412407","https://openalex.org/W4401043313","https://openalex.org/W6600339457"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2380075625","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Generative":[0,99],"information":[1,23,32],"retrieval":[2,24],"(Gen-IR)":[3],"is":[4,74,106],"a":[5,114],"fast-growing":[6],"interdisciplinary":[7],"research":[8,83],"area":[9,69],"that":[10,58],"investigates":[11],"how":[12],"to":[13,21,77,79,107],"leverage":[14],"advances":[15],"in":[16,67],"generative":[17,131,137,140],"Artificial":[18],"Intelligence":[19],"(AI)":[20],"improve":[22],"systems.":[25],"Gen-IR":[26,47,56,121],"has":[27,51],"attracted":[28],"interest":[29],"from":[30],"the":[31,44,145],"retrieval,":[33,133],"natural":[34],"language":[35],"processing,":[36],"and":[37,61,72,85,119,139,152,166,172],"machine":[38],"learning":[39],"communities,":[40],"among":[41],"others.":[42],"Since":[43],"dawn":[45],"of":[46,55,91,117,147],"last":[48],"year,":[49],"there":[50],"been":[52],"an":[53,109],"explosion":[54],"systems":[57],"have":[59],"launched":[60],"are":[62],"now":[63],"widely":[64],"used.":[65],"Interest":[66],"this":[68,92],"across":[70],"academia":[71],"industry":[73],"only":[75],"expected":[76],"continue":[78],"grow":[80],"as":[81,130],"new":[82],"challenges":[84],"application":[86],"opportunities":[87],"arise.":[88],"The":[89,95,123,155],"goal":[90],"proposed":[93],"workshop,":[94],"Second":[96],"Workshop":[97],"on":[98,127],"Information":[100],"Retrieval":[101],"(Gen-IR":[102],"@":[103],"SIGIR":[104],"2024)":[105],"provide":[108],"interactive":[110],"venue":[111],"for":[112],"exploring":[113],"broad":[115],"range":[116],"foundational":[118],"applied":[120],"research.":[122],"workshop":[124,156],"will":[125,157],"focus":[126],"tasks":[128],"such":[129],"document":[132],"grounded":[134],"answer":[135],"generation,":[136],"recommendation,":[138],"knowledge":[141],"graphs,":[142],"all":[143],"through":[144],"lens":[146],"model":[148,150],"training,":[149],"behavior,":[151],"broader":[153],"issues.":[154],"be":[158],"highly":[159],"interactive,":[160],"favoring":[161],"panel":[162],"discussions,":[163],"poster":[164],"sessions,":[165],"roundtable":[167],"discussions":[168],"over":[169],"one-sided":[170],"keynotes":[171],"paper":[173],"talks.":[174]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}