{"id":"https://openalex.org/W4400530380","doi":"https://doi.org/10.1145/3626772.3657906","title":"Faster Learned Sparse Retrieval with Block-Max Pruning","display_name":"Faster Learned Sparse Retrieval with Block-Max Pruning","publication_year":2024,"publication_date":"2024-07-10","ids":{"openalex":"https://openalex.org/W4400530380","doi":"https://doi.org/10.1145/3626772.3657906"},"language":"en","primary_location":{"id":"doi:10.1145/3626772.3657906","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3626772.3657906","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":"conference-paper","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/A5004258130","display_name":"Antonio Mallia","orcid":"https://orcid.org/0000-0002-7817-6140"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Antonio Mallia","raw_affiliation_strings":["Pinecone, New York, USA"],"raw_orcid":"https://orcid.org/0000-0002-7817-6140","affiliations":[{"raw_affiliation_string":"Pinecone, New York, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074323303","display_name":"Torsten Suel","orcid":"https://orcid.org/0000-0002-8324-980X"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Torsten Suel","raw_affiliation_strings":["New York University, Brooklyn, NY, USA"],"raw_orcid":"https://orcid.org/0000-0002-8324-980X","affiliations":[{"raw_affiliation_string":"New York University, Brooklyn, NY, USA","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018894843","display_name":"Nicola Tonellotto","orcid":"https://orcid.org/0000-0002-7427-1001"},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Nicola Tonellotto","raw_affiliation_strings":["University of Pisa, Pisa, Italy"],"raw_orcid":"https://orcid.org/0000-0002-7427-1001","affiliations":[{"raw_affiliation_string":"University of Pisa, Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2411","last_page":"2415"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9995999932289124,"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"}},{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9968000054359436,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.993399977684021,"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/pruning","display_name":"Pruning","score":0.7059057950973511},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6897098422050476},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.6290532350540161},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46223220229148865},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3521580696105957},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15771129727363586},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.0689007043838501},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.061139434576034546}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7059057950973511},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6897098422050476},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6290532350540161},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46223220229148865},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3521580696105957},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15771129727363586},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0689007043838501},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.061139434576034546},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3626772.3657906","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3626772.3657906","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"},{"id":"pmh:oai:arpi.unipi.it:11568/1264850","is_oa":false,"landing_page_url":"https://hdl.handle.net/11568/1264850","pdf_url":null,"source":{"id":"https://openalex.org/S4377196265","display_name":"CINECA IRIS Institutial research information system (University of Pisa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I108290504","host_organization_name":"University of Pisa","host_organization_lineage":["https://openalex.org/I108290504"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1980344365","https://openalex.org/W1982387198","https://openalex.org/W1991360400","https://openalex.org/W1994922945","https://openalex.org/W2065472179","https://openalex.org/W2292193262","https://openalex.org/W2740321901","https://openalex.org/W2740817677","https://openalex.org/W3021397474","https://openalex.org/W3034521898","https://openalex.org/W3093721172","https://openalex.org/W3101346938","https://openalex.org/W3114605047","https://openalex.org/W3152671171","https://openalex.org/W3154280800","https://openalex.org/W3154755316","https://openalex.org/W3155114168","https://openalex.org/W3155895380","https://openalex.org/W3172119680","https://openalex.org/W3188983256","https://openalex.org/W3197604682","https://openalex.org/W4205951122","https://openalex.org/W4252076394","https://openalex.org/W4284663260","https://openalex.org/W4284664419","https://openalex.org/W4284880643","https://openalex.org/W4311609640","https://openalex.org/W4367047387","https://openalex.org/W4385573371"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","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/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Learned":[0],"sparse":[1,104],"retrieval":[2,40,105,150,170,181],"systems":[3,30],"aim":[4],"to":[5,43,75,113],"combine":[6],"the":[7,14,25,35,47,115,130],"effectiveness":[8],"of":[9,16,49],"contextualized":[10],"language":[11],"models":[12],"with":[13],"scalability":[15],"conventional":[17],"data":[18],"structures":[19],"such":[20],"as":[21,136],"inverted":[22],"indexes.":[23],"Nevertheless,":[24],"indexes":[26,100],"generated":[27],"by":[28,139],"these":[29],"exhibit":[31],"significant":[32],"deviations":[33],"from":[34,63],"ones":[36],"that":[37,53,157],"use":[38],"traditional":[39,58],"models,":[41],"leading":[42,74],"a":[44,109,140],"discrepancy":[45],"in":[46,66,102,168,179],"performance":[48],"existing":[50,161],"query":[51,67],"optimizations":[52],"were":[54],"specifically":[55],"developed":[56],"for":[57,99],"structures.":[59],"These":[60],"disparities":[61],"arise":[62],"structural":[64],"variations":[65],"and":[68,80,127,132,172,177],"document":[69,116,121],"statistics,":[70],"including":[71],"sub-word":[72],"tokenization,":[73],"longer":[76],"queries,":[77],"smaller":[78],"vocabularies,":[79],"different":[81],"score":[82],"distributions":[83],"within":[84],"posting":[85],"lists.":[86],"This":[87],"paper":[88],"introduces":[89],"Block-Max":[90],"Pruning":[91],"(BMP),":[92],"an":[93],"innovative":[94],"dynamic":[95,162],"pruning":[96,163],"strategy":[97],"tailored":[98],"arising":[101],"learned":[103],"environments.":[106],"BMP":[107,158],"employs":[108],"block":[110],"filtering":[111],"mechanism":[112],"divide":[114],"space":[117],"into":[118],"small,":[119],"consecutive":[120],"ranges,":[122],"which":[123],"are":[124],"then":[125],"aggregated":[126],"sorted":[128],"on":[129,148],"fly,":[131],"fully":[133],"processed":[134],"only":[135],"necessary,":[137],"guided":[138],"defined":[141],"safe":[142,169],"early":[143],"termination":[144],"criterion":[145],"or":[146],"based":[147],"approximate":[149,180],"requirements.":[151],"Through":[152],"rigorous":[153],"experimentation,":[154],"we":[155],"show":[156],"substantially":[159],"outperforms":[160],"strategies,":[164],"offering":[165],"unparalleled":[166],"efficiency":[167,178],"contexts":[171],"improved":[173],"trade-offs":[174],"between":[175],"precision":[176],"tasks.":[182]},"counts_by_year":[{"year":2026,"cited_by_count":10},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-16T13:24:37.021932","created_date":"2024-07-12T00:00:00"}
