{"id":"https://openalex.org/W4387846361","doi":"https://doi.org/10.1145/3583780.3615499","title":"SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems","display_name":"SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems","publication_year":2023,"publication_date":"2023-10-21","ids":{"openalex":"https://openalex.org/W4387846361","doi":"https://doi.org/10.1145/3583780.3615499"},"language":"en","primary_location":{"id":"doi:10.1145/3583780.3615499","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3615499","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International 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/A5041929848","display_name":"Beichuan Zhang","orcid":"https://orcid.org/0000-0003-1163-4036"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Beichuan Zhang","raw_affiliation_strings":["Kuaishou, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Kuaishou, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101566951","display_name":"Chenggen Sun","orcid":"https://orcid.org/0000-0003-0477-2385"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chenggen Sun","raw_affiliation_strings":["Kuaishou, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Kuaishou, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081629672","display_name":"Jianchao Tan","orcid":"https://orcid.org/0000-0002-9862-2654"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jianchao Tan","raw_affiliation_strings":["Kuaishou, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Kuaishou, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101402153","display_name":"Xinjun Cai","orcid":"https://orcid.org/0000-0002-6723-0367"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinjun Cai","raw_affiliation_strings":["Kuaishou, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Kuaishou, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100694877","display_name":"Jun Zhao","orcid":"https://orcid.org/0009-0008-2501-6099"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jun Zhao","raw_affiliation_strings":["Kuaishou, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Kuaishou, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093105916","display_name":"Mengqi Miao","orcid":"https://orcid.org/0009-0004-7672-3448"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mengqi Miao","raw_affiliation_strings":["Kuaishou, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Kuaishou, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093105917","display_name":"Kang Yin","orcid":"https://orcid.org/0009-0008-3107-8210"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kang Yin","raw_affiliation_strings":["Kuaishou, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Kuaishou, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070531404","display_name":"Chengru Song","orcid":"https://orcid.org/0009-0009-3826-8436"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chengru Song","raw_affiliation_strings":["Kuaishou, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Kuaishou, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076034547","display_name":"Na Mou","orcid":"https://orcid.org/0009-0006-0125-1997"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Na Mou","raw_affiliation_strings":["Kuaishou, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Kuaishou, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083873109","display_name":"Yang Song","orcid":"https://orcid.org/0000-0002-1714-5527"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang Song","raw_affiliation_strings":["Kuaishou, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Kuaishou, Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":10,"corresponding_author_ids":["https://openalex.org/A5041929848"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.0995,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.94666526,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4930","last_page":"4937"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998000264167786,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9998000264167786,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9943000078201294,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9901999831199646,"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":0.8286440968513489},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.7086138725280762},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6054767370223999},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.5825360417366028},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.4304351508617401},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4235125184059143},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22534504532814026},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.2230665385723114},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1798464059829712},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.0853244960308075}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8286440968513489},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.7086138725280762},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6054767370223999},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.5825360417366028},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.4304351508617401},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4235125184059143},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22534504532814026},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2230665385723114},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1798464059829712},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0853244960308075},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3583780.3615499","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3615499","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.47999998927116394}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2070996757","https://openalex.org/W2253609413","https://openalex.org/W2295739661","https://openalex.org/W2747329762","https://openalex.org/W2752782242","https://openalex.org/W2809290718","https://openalex.org/W2945772520","https://openalex.org/W2962745591","https://openalex.org/W2964182926","https://openalex.org/W2969476445","https://openalex.org/W3034552531","https://openalex.org/W3088463703","https://openalex.org/W3106252282","https://openalex.org/W3154430477","https://openalex.org/W3155298021","https://openalex.org/W3160344148","https://openalex.org/W3173839890","https://openalex.org/W4224227049","https://openalex.org/W4226337336","https://openalex.org/W4234592442","https://openalex.org/W4235880465","https://openalex.org/W4281256372","https://openalex.org/W4296415418","https://openalex.org/W4311430480"],"related_works":["https://openalex.org/W4390273403","https://openalex.org/W4386781444","https://openalex.org/W3092950680","https://openalex.org/W4246980185","https://openalex.org/W2150182025","https://openalex.org/W3197542405","https://openalex.org/W2418190244","https://openalex.org/W4238861846","https://openalex.org/W3125580266","https://openalex.org/W2928062709"],"abstract_inverted_index":{"Increasing":[0],"the":[1,13,32,49,57,72,84,150,159,163,167,170],"size":[2],"of":[3,15,34,60,65,76,86,121,149,162,183,198,200,205,207],"embedding":[4,87],"layers":[5],"has":[6,192],"shown":[7],"to":[8,23,82,99,104,195],"be":[9],"effective":[10],"in":[11,26,56,133],"improving":[12],"performance":[14,175],"recommendation":[16,145],"models,":[17],"yet":[18],"gradually":[19],"causing":[20],"their":[21],"sizes":[22],"exceed":[24],"terabytes":[25],"industrial":[27,61,115],"recommender":[28,58],"systems,":[29],"and":[30,36,114,143,181,191,202],"hence":[31],"increase":[33],"computing":[35],"storage":[37,180],"costs.":[38],"To":[39],"save":[40],"resources":[41],"while":[42],"maintaining":[43],"model":[44,50,172],"performances,":[45],"we":[46,53,70,92],"propose":[47],"SHARK,":[48],"compression":[51],"practice":[52],"have":[54],"summarized":[55],"system":[59],"scenarios.":[62],"SHARK":[63,124,155],"consists":[64],"two":[66],"main":[67],"components.":[68],"First,":[69],"use":[71],"novel":[73],"first-order":[74],"component":[75,120],"Taylor":[77],"expansion":[78],"as":[79,139],"importance":[80],"scores":[81],"prune":[83],"number":[85],"tables":[88],"(feature":[89],"fields).":[90],"Second,":[91],"introduce":[93],"a":[94],"new":[95],"row-wise":[96],"quantization":[97,102],"method":[98],"apply":[100],"different":[101],"strategies":[103],"each":[105,119],"embedding.":[106],"We":[107,129],"conduct":[108,130],"extensive":[109],"experiments":[110],"on":[111,136],"both":[112],"public":[113],"datasets,":[116],"demonstrating":[117],"that":[118],"our":[122],"proposed":[123],"framework":[125],"outperforms":[126],"previous":[127],"approaches.":[128],"A/B":[131,152],"tests":[132],"multiple":[134],"models":[135],"Kuaishou,":[137],"such":[138],"short":[140],"video,":[141],"e-commerce,":[142],"advertising":[144],"models.":[146],"The":[147],"results":[148],"online":[151],"test":[153],"showed":[154],"can":[156],"effectively":[157],"reduce":[158],"memory":[160],"footprint":[161],"embedded":[164],"layer.":[165],"For":[166],"short-video":[168],"scenarios,":[169],"compressed":[171],"without":[173],"any":[174],"drop":[176],"significantly":[177],"saves":[178],"70%":[179],"thousands":[182],"machines,":[184],"improves":[185],"30%":[186],"queries":[187],"per":[188],"second":[189],"(QPS),":[190],"been":[193],"deployed":[194],"serve":[196],"hundreds":[197],"millions":[199],"users":[201],"process":[203],"tens":[204],"billions":[206],"requests":[208],"every":[209],"day.":[210]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}