{"id":"https://openalex.org/W4381741406","doi":"https://doi.org/10.1145/3604436","title":"RSS: Effective and Efficient Training for Sequential Recommendation Using Recency Sampling","display_name":"RSS: Effective and Efficient Training for Sequential Recommendation Using Recency Sampling","publication_year":2023,"publication_date":"2023-06-23","ids":{"openalex":"https://openalex.org/W4381741406","doi":"https://doi.org/10.1145/3604436"},"language":"en","primary_location":{"id":"doi:10.1145/3604436","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3604436","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3604436","source":{"id":"https://openalex.org/S4387284325","display_name":"ACM Transactions on Recommender Systems","issn_l":"2770-6699","issn":["2770-6699"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Recommender Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3604436","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100748224","display_name":"Aleksandr V. Petrov","orcid":"https://orcid.org/0000-0002-0911-3605"},"institutions":[{"id":"https://openalex.org/I7882870","display_name":"University of Glasgow","ror":"https://ror.org/00vtgdb53","country_code":"GB","type":"education","lineage":["https://openalex.org/I7882870"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Aleksandr Petrov","raw_affiliation_strings":["University of Glasgow, Glasgow, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0002-0911-3605","affiliations":[{"raw_affiliation_string":"University of Glasgow, Glasgow, United Kingdom","institution_ids":["https://openalex.org/I7882870"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057643560","display_name":"Craig Macdonald","orcid":"https://orcid.org/0000-0003-3143-279X"},"institutions":[{"id":"https://openalex.org/I7882870","display_name":"University of Glasgow","ror":"https://ror.org/00vtgdb53","country_code":"GB","type":"education","lineage":["https://openalex.org/I7882870"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Craig Macdonald","raw_affiliation_strings":["University of Glasgow, Glasgow, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0003-3143-279X","affiliations":[{"raw_affiliation_string":"University of Glasgow, Glasgow, United Kingdom","institution_ids":["https://openalex.org/I7882870"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I7882870"],"apc_list":null,"apc_paid":null,"fwci":6.5284,"has_fulltext":true,"cited_by_count":15,"citation_normalized_percentile":{"value":0.96547033,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"3","issue":"1","first_page":"1","last_page":"32"},"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/T10028","display_name":"Topic Modeling","score":0.9866999983787537,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9778000116348267,"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/computer-science","display_name":"Computer science","score":0.8755102157592773},{"id":"https://openalex.org/keywords/rss","display_name":"RSS","score":0.7897622585296631},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.6748949289321899},{"id":"https://openalex.org/keywords/movielens","display_name":"MovieLens","score":0.622231125831604},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5974470973014832},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5757061243057251},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.47371357679367065},{"id":"https://openalex.org/keywords/thompson-sampling","display_name":"Thompson sampling","score":0.4173925518989563},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3464229702949524},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.314738392829895},{"id":"https://openalex.org/keywords/regret","display_name":"Regret","score":0.2127588391304016},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.08751583099365234}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8755102157592773},{"id":"https://openalex.org/C2385561","wikidata":"https://www.wikidata.org/wiki/Q45432","display_name":"RSS","level":2,"score":0.7897622585296631},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.6748949289321899},{"id":"https://openalex.org/C2776156558","wikidata":"https://www.wikidata.org/wiki/Q4353746","display_name":"MovieLens","level":4,"score":0.622231125831604},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5974470973014832},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5757061243057251},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.47371357679367065},{"id":"https://openalex.org/C73602740","wikidata":"https://www.wikidata.org/wiki/Q7795822","display_name":"Thompson sampling","level":3,"score":0.4173925518989563},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3464229702949524},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.314738392829895},{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.2127588391304016},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.08751583099365234},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3604436","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3604436","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3604436","source":{"id":"https://openalex.org/S4387284325","display_name":"ACM Transactions on Recommender Systems","issn_l":"2770-6699","issn":["2770-6699"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Recommender Systems","raw_type":"journal-article"},{"id":"pmh:oai:eprints.gla.ac.uk:299238","is_oa":true,"landing_page_url":"https://eprints.gla.ac.uk/view/author/62761.html>","pdf_url":"https://eprints.gla.ac.uk/299238/2/299238.pdf","source":{"id":"https://openalex.org/S4210235606","display_name":"ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam)","issn_l":"2622-8912","issn":["2622-8912","2622-8920"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Articles"}],"best_oa_location":{"id":"doi:10.1145/3604436","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3604436","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3604436","source":{"id":"https://openalex.org/S4387284325","display_name":"ACM Transactions on Recommender Systems","issn_l":"2770-6699","issn":["2770-6699"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Recommender Systems","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4381741406.pdf","grobid_xml":"https://content.openalex.org/works/W4381741406.grobid-xml"},"referenced_works_count":53,"referenced_works":["https://openalex.org/W2110953678","https://openalex.org/W2115584760","https://openalex.org/W2149427297","https://openalex.org/W2171279286","https://openalex.org/W2219888463","https://openalex.org/W2539247542","https://openalex.org/W2626454364","https://openalex.org/W2783272285","https://openalex.org/W2795199972","https://openalex.org/W2798385737","https://openalex.org/W2886209086","https://openalex.org/W2896962583","https://openalex.org/W2902040508","https://openalex.org/W2912255075","https://openalex.org/W2951645301","https://openalex.org/W2963367478","https://openalex.org/W2981852735","https://openalex.org/W2984100107","https://openalex.org/W3031331881","https://openalex.org/W3038719422","https://openalex.org/W3065542300","https://openalex.org/W3066766382","https://openalex.org/W3080374445","https://openalex.org/W3081170586","https://openalex.org/W3086560018","https://openalex.org/W3089147479","https://openalex.org/W3100260481","https://openalex.org/W3101063193","https://openalex.org/W3102619277","https://openalex.org/W3111543999","https://openalex.org/W3119242082","https://openalex.org/W3152634418","https://openalex.org/W3153003256","https://openalex.org/W3156977692","https://openalex.org/W3168154995","https://openalex.org/W3180733313","https://openalex.org/W3185773347","https://openalex.org/W3190794503","https://openalex.org/W3195915598","https://openalex.org/W3196852145","https://openalex.org/W3201149665","https://openalex.org/W3206127589","https://openalex.org/W3209810427","https://openalex.org/W3210984903","https://openalex.org/W4205784171","https://openalex.org/W4214508080","https://openalex.org/W4220894025","https://openalex.org/W4226210383","https://openalex.org/W4226330269","https://openalex.org/W4288089799","https://openalex.org/W4296591831","https://openalex.org/W4296604567","https://openalex.org/W4297614008"],"related_works":["https://openalex.org/W2797500822","https://openalex.org/W2355698112","https://openalex.org/W2022984797","https://openalex.org/W2986679525","https://openalex.org/W4205822456","https://openalex.org/W4299358966","https://openalex.org/W2981634480","https://openalex.org/W2188396403","https://openalex.org/W2368078825","https://openalex.org/W4288082747"],"abstract_inverted_index":{"Many":[0],"modern":[1],"sequential":[2,54,115,305],"recommender":[3,104,306],"systems":[4],"use":[5],"deep":[6],"neural":[7],"networks,":[8],"which":[9,297],"can":[10,179,208],"effectively":[11],"estimate":[12],"the":[13,28,38,81,84,96,101,111,114,173,187,199,205,273,293,309],"relevance":[14],"of":[15,21,30,52,83,113,135,146,244],"items":[16],"but":[17,190],"require":[18],"a":[19,64,87,131,144,211,217,221,225,286],"lot":[20],"time":[22,122,235],"to":[23,44,46,62,110,123,158,186,204,263,292],"train.":[24],"Slow":[25],"training":[26,51,66,70,138,194,234,295,304,313],"increases":[27],"costs":[29],"training,":[31],"hinders":[32],"product":[33],"development":[34],"timescales,":[35],"and":[36,161,168,220,248,268,301],"prevents":[37],"model":[39,105,163,207,276,307],"from":[40],"being":[41],"regularly":[42],"updated":[43],"adapt":[45],"changing":[47],"user":[48,60],"preferences.":[49],"The":[50,68],"such":[53],"models":[55,174],"involves":[56],"appropriately":[57],"sampling":[58],"past":[59],"interactions":[61],"create":[63],"realistic":[65],"objective.":[67],"existing":[69,294],"objectives":[71],"have":[72],"limitations.":[73,153],"For":[74,196],"instance,":[75],"next":[76],"item":[77,97],"prediction":[78],"never":[79],"uses":[80],"beginning":[82],"sequence":[85],"as":[86,165],"learning":[88],"target,":[89],"thereby":[90],"potentially":[91],"discarding":[92],"valuable":[93],"data.":[94],"However,":[95],"masking":[98],"used":[99],"by":[100,143],"state-of-the-art":[102,162],"BERT4Rec":[103,228],"is":[106,141,285,298],"only":[107],"weakly":[108],"related":[109],"goal":[112],"recommendation;":[116],"therefore,":[117],"it":[118],"requires":[119],"much":[120,192],"more":[121],"obtain":[124],"an":[125],"effective":[126,188,300],"model.":[127],"Hence,":[128],"we":[129,281],"propose":[130],"novel":[132],"Recency-based":[133],"Sampling":[134],"Sequences":[136],"(RSS)":[137],"objective":[139],"(which":[140],"parameterized":[142],"choice":[145],"recency":[147,245],"importance":[148,246],"function)":[149],"that":[150,172,250,258,272,283],"addresses":[151],"both":[152,299],"We":[154,170,238,254],"apply":[155],"our":[156,177],"method":[157,178],"various":[159],"recent":[160,267],"architectures\u2014such":[164],"GRU4Rec,":[166],"Caser,":[167],"SASRec.":[169],"show":[171,249,282],"enhanced":[175],"with":[176,191,241],"achieve":[180],"performances":[181],"exceeding":[182],"or":[183],"very":[184],"close":[185],"BERT4Rec,":[189],"less":[193,233],"time.":[195],"example,":[197],"on":[198],"MovieLens-20M":[200],"dataset,":[201],"RSS":[202,284],"applied":[203],"SASRec":[206,260,275],"result":[209],"in":[210,214],"60%":[212],"improvement":[213,223],"NDCG":[215],"over":[216,224],"vanilla":[218],"SASRec,":[219],"16%":[222],"fully":[226],"trained":[227],"model,":[229],"despite":[230],"taking":[231],"93%":[232],"than":[236],"BERT4Rec.":[237],"also":[239],"experiment":[240],"two":[242],"families":[243],"functions":[247],"they":[251],"perform":[252],"similarly.":[253],"further":[255],"empirically":[256],"demonstrate":[257],"RSS-enhanced":[259],"successfully":[261],"learns":[262],"distinguish":[264],"differences":[265],"between":[266],"older":[269],"interactions\u2014a":[270],"property":[271],"original":[274],"does":[277],"not":[278],"exhibit.":[279],"Overall,":[280],"viable":[287],"(and":[288],"frequently":[289],"better)":[290],"alternative":[291],"objectives,":[296],"efficient":[302],"for":[303,312],"when":[308],"computational":[310],"resources":[311],"are":[314],"limited.":[315]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}