{"id":"https://openalex.org/W3012754345","doi":"https://doi.org/10.1145/3366423.3380151","title":"LightRec: A Memory and Search-Efficient Recommender System","display_name":"LightRec: A Memory and Search-Efficient Recommender System","publication_year":2020,"publication_date":"2020-04-20","ids":{"openalex":"https://openalex.org/W3012754345","doi":"https://doi.org/10.1145/3366423.3380151","mag":"3012754345"},"language":"en","primary_location":{"id":"doi:10.1145/3366423.3380151","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380151","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3366423.3380151","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085254654","display_name":"Defu Lian","orcid":"https://orcid.org/0000-0002-3507-9607"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Defu Lian","raw_affiliation_strings":["University of Science and Technology of China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115695530","display_name":"Haoyu Wang","orcid":"https://orcid.org/0000-0003-1100-8633"},"institutions":[{"id":"https://openalex.org/I63190737","display_name":"University at Buffalo, State University of New York","ror":"https://ror.org/01y64my43","country_code":"US","type":"education","lineage":["https://openalex.org/I63190737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haoyu Wang","raw_affiliation_strings":["University at Buffalo"],"affiliations":[{"raw_affiliation_string":"University at Buffalo","institution_ids":["https://openalex.org/I63190737"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100423656","display_name":"Zheng Liu","orcid":"https://orcid.org/0000-0001-7765-8466"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng Liu","raw_affiliation_strings":["MSRA"],"affiliations":[{"raw_affiliation_string":"MSRA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087106517","display_name":"Jianxun Lian","orcid":"https://orcid.org/0000-0003-3108-5601"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jianxun Lian","raw_affiliation_strings":["MSRA"],"affiliations":[{"raw_affiliation_string":"MSRA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048237545","display_name":"Enhong Chen","orcid":"https://orcid.org/0000-0002-4835-4102"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Enhong Chen","raw_affiliation_strings":["University of Science and Technology of China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044651577","display_name":"Xing Xie","orcid":"https://orcid.org/0000-0002-8608-8482"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xing Xie","raw_affiliation_strings":["MSRA"],"affiliations":[{"raw_affiliation_string":"MSRA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5085254654"],"corresponding_institution_ids":["https://openalex.org/I126520041"],"apc_list":null,"apc_paid":null,"fwci":13.63,"has_fulltext":false,"cited_by_count":75,"citation_normalized_percentile":{"value":0.98777331,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"695","last_page":"705"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.998199999332428,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9973000288009644,"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":0.8414734601974487},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.6988105773925781},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5474392771720886},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.532959520816803},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.517899751663208},{"id":"https://openalex.org/keywords/codebook","display_name":"Codebook","score":0.4730719029903412},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4652370512485504},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.45959290862083435},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45250049233436584},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.43915510177612305},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.4202215075492859},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4197657108306885},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.40323910117149353},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3243185877799988},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.21485385298728943},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.16891691088676453}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8414734601974487},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.6988105773925781},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5474392771720886},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.532959520816803},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.517899751663208},{"id":"https://openalex.org/C127759330","wikidata":"https://www.wikidata.org/wiki/Q637416","display_name":"Codebook","level":2,"score":0.4730719029903412},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4652370512485504},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.45959290862083435},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45250049233436584},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.43915510177612305},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.4202215075492859},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4197657108306885},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.40323910117149353},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3243185877799988},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.21485385298728943},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.16891691088676453},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3366423.3380151","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380151","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"},{"id":"mag:3170772703","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202002216757218294","pdf_url":null,"source":{"id":"https://openalex.org/S4306500161","display_name":"ACM Proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"ACM Proceedings","raw_type":null}],"best_oa_location":{"id":"doi:10.1145/3366423.3380151","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380151","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1599364940","https://openalex.org/W1971187657","https://openalex.org/W1974627246","https://openalex.org/W1977182282","https://openalex.org/W1978475816","https://openalex.org/W2010416066","https://openalex.org/W2059851891","https://openalex.org/W2077815765","https://openalex.org/W2119913432","https://openalex.org/W2122196799","https://openalex.org/W2123427850","https://openalex.org/W2124509324","https://openalex.org/W2126754439","https://openalex.org/W2136189984","https://openalex.org/W2157881433","https://openalex.org/W2336920772","https://openalex.org/W2475334473","https://openalex.org/W2509893387","https://openalex.org/W2547875792","https://openalex.org/W2548570154","https://openalex.org/W2583674722","https://openalex.org/W2604433096","https://openalex.org/W2604662567","https://openalex.org/W2605350416","https://openalex.org/W2723293840","https://openalex.org/W2742528903","https://openalex.org/W2783666221","https://openalex.org/W2792839191","https://openalex.org/W2793768763","https://openalex.org/W2808891190","https://openalex.org/W2884134047","https://openalex.org/W2892102150","https://openalex.org/W2946829651","https://openalex.org/W2950416834","https://openalex.org/W2963323306","https://openalex.org/W2963655167","https://openalex.org/W2964182926","https://openalex.org/W2964258748","https://openalex.org/W2983848182","https://openalex.org/W3098087397","https://openalex.org/W3101397996","https://openalex.org/W3104030692","https://openalex.org/W3106181667"],"related_works":["https://openalex.org/W2293149949","https://openalex.org/W2026099691","https://openalex.org/W4284672201","https://openalex.org/W2377486419","https://openalex.org/W2943202426","https://openalex.org/W2950156284","https://openalex.org/W2736714427","https://openalex.org/W2163679795","https://openalex.org/W2137816434","https://openalex.org/W2017956276"],"abstract_inverted_index":{"Deep":[0],"recommender":[1,38],"systems":[2],"have":[3,79],"achieved":[4],"remarkable":[5],"improvements":[6,176],"in":[7,27,177,201],"recent":[8],"years.":[9],"Despite":[10],"its":[11],"superior":[12],"ranking":[13,146],"precision,":[14],"the":[15,96,101,114,122,130,167],"running":[16],"efficiency":[17],"and":[18,45,117,144],"memory":[19,47],"consumption":[20],"turn":[21],"out":[22],"to":[23,127,159,184,196],"be":[24],"severe":[25],"bottlenecks":[26],"reality.":[28],"To":[29,98],"overcome":[30],"both":[31],"limitations,":[32],"we":[33,105],"propose":[34],"LightRec,":[35],"a":[36,54,75],"lightweight":[37,169],"system":[39],"which":[40,61,89,138,156],"enjoys":[41],"fast":[42],"online":[43],"inference":[44],"economic":[46],"consumption.":[48],"The":[49],"backbone":[50],"of":[51,56,60,64,73,86,95,179],"LightRec":[52,77,148,171,188],"is":[53,62,149],"total":[55],"B":[57,87],"codebooks,":[58],"each":[59,94],"composed":[63],"W":[65],"latent":[66],"vectors,":[67],"known":[68],"as":[69,83],"codewords.":[70],"On":[71],"top":[72],"such":[74],"structure,":[76],"will":[78],"an":[80,107],"item":[81],"represented":[82],"additive":[84],"composition":[85],"codewords,":[88],"are":[90,119,136],"optimally":[91],"selected":[92],"from":[93,103],"codebooks.":[97],"effectively":[99],"learn":[100],"codebooks":[102],"data,":[104],"devise":[106],"end-to-end":[108],"learning":[109],"workflow,":[110],"where":[111],"challenges":[112],"on":[113],"inherent":[115],"differentiability":[116],"diversity":[118],"conquered":[120],"by":[121],"proposed":[123],"techniques.":[124],"In":[125],"addition,":[126],"further":[128],"improve":[129],"representation":[131],"quality,":[132],"several":[133],"distillation":[134],"strategies":[135],"employed,":[137],"better":[139],"preserves":[140],"user-item":[141],"relevance":[142],"scores":[143],"relative":[145,175],"orders.":[147],"extensively":[150],"evaluated":[151],"with":[152,165],"four":[153],"real-world":[154],"datasets,":[155],"gives":[157],"rise":[158],"two":[160],"empirical":[161],"findings:":[162],"1)":[163],"compared":[164,183],"those":[166],"state-of-the-art":[168],"baselines,":[170],"achieves":[172],"over":[173],"11%":[174],"terms":[178],"recall":[180],"performance;":[181],"2)":[182],"conventional":[185],"recommendation":[186],"algorithms,":[187],"merely":[189],"incurs":[190],"negligible":[191],"accuracy":[192],"degradation":[193],"while":[194],"leads":[195],"more":[197],"than":[198],"27x":[199],"speedup":[200],"top-k":[202],"recommendation.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":19},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":15},{"year":2020,"cited_by_count":6}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
