{"id":"https://openalex.org/W3034548041","doi":"https://doi.org/10.1145/3397271.3401124","title":"Dual Sequential Network for Temporal Sets Prediction","display_name":"Dual Sequential Network for Temporal Sets Prediction","publication_year":2020,"publication_date":"2020-07-25","ids":{"openalex":"https://openalex.org/W3034548041","doi":"https://doi.org/10.1145/3397271.3401124","mag":"3034548041"},"language":"en","primary_location":{"id":"doi:10.1145/3397271.3401124","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397271.3401124","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 43rd 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/A5081275566","display_name":"Leilei Sun","orcid":"https://orcid.org/0000-0002-0157-1716"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Leilei Sun","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016981484","display_name":"Yansong Bai","orcid":"https://orcid.org/0009-0005-4856-5814"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yansong Bai","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053487836","display_name":"Bowen Du","orcid":"https://orcid.org/0000-0003-0975-2367"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bowen Du","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033864788","display_name":"Chuanren Liu","orcid":"https://orcid.org/0000-0001-9030-8495"},"institutions":[{"id":"https://openalex.org/I75027704","display_name":"University of Tennessee at Knoxville","ror":"https://ror.org/020f3ap87","country_code":"US","type":"education","lineage":["https://openalex.org/I75027704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chuanren Liu","raw_affiliation_strings":["University of Tennessee, Knoxville, TN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Tennessee, Knoxville, TN, USA","institution_ids":["https://openalex.org/I75027704"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101862104","display_name":"Hui Xiong","orcid":"https://orcid.org/0000-0001-6016-6465"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hui Xiong","raw_affiliation_strings":["Rutgers University, Newark, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rutgers University, Newark, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109299440","display_name":"Weifeng Lv","orcid":"https://orcid.org/0000-0002-7061-9509"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weifeng Lv","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.8626,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.94538659,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1439","last_page":"1448"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.998199999332428,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.998199999332428,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9968000054359436,"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/T11106","display_name":"Data Management and Algorithms","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.7581042051315308},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6093611717224121},{"id":"https://openalex.org/keywords/temporal-database","display_name":"Temporal database","score":0.5580868721008301},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5209925770759583},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.49136438965797424},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.48094838857650757},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.4613751173019409},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4230843782424927},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.41898152232170105},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4024735391139984}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7581042051315308},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6093611717224121},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.5580868721008301},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5209925770759583},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.49136438965797424},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.48094838857650757},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.4613751173019409},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4230843782424927},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.41898152232170105},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4024735391139984},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3397271.3401124","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397271.3401124","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G7625076387","display_name":null,"funder_award_id":"71901011,51778033,51822802,U1811463","funder_id":"https://openalex.org/F4320327720","funder_display_name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320327720","display_name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1998619381","https://openalex.org/W2054141820","https://openalex.org/W2064675550","https://openalex.org/W2171279286","https://openalex.org/W2474909202","https://openalex.org/W2584122106","https://openalex.org/W2613228905","https://openalex.org/W2723293840","https://openalex.org/W2809425754","https://openalex.org/W2951227353","https://openalex.org/W2951527505","https://openalex.org/W2963981376","https://openalex.org/W2964189376","https://openalex.org/W2964308564","https://openalex.org/W2964316331","https://openalex.org/W2966123616","https://openalex.org/W2984100107","https://openalex.org/W2987999026","https://openalex.org/W3096591391","https://openalex.org/W4299128829","https://openalex.org/W6610928041"],"related_works":["https://openalex.org/W2317351040","https://openalex.org/W2952466936","https://openalex.org/W1988622314","https://openalex.org/W2393949104","https://openalex.org/W3046201198","https://openalex.org/W4293061921","https://openalex.org/W2391251536","https://openalex.org/W2807441102","https://openalex.org/W2009424630","https://openalex.org/W3146111732"],"abstract_inverted_index":{"Many":[0],"sequential":[1,24,98],"behaviors":[2],"such":[3],"as":[4,23,199],"purchasing":[5],"items":[6,86,153,179],"from":[7],"time":[8,63],"to":[9,38,79,146,163,194,201],"time,":[10],"selecting":[11],"courses":[12],"in":[13],"different":[14],"terms,":[15],"collecting":[16],"event":[17],"logs":[18],"periodically":[19],"could":[20,43,69],"be":[21,71],"formalized":[22],"sets":[25,42,76,109,188],"of":[26,41,55,82,85,92,97,131,152,178],"actions":[27],"or":[28,50,65],"elements,":[29],"namely":[30],"temporal":[31,66,75,90,150,176],"sets.":[32,155,181],"Predicting":[33],"the":[34,56,80,148,165,171],"subsequent":[35,166],"set":[36,132,167],"according":[37],"historical":[39],"sequence":[40],"help":[44],"us":[45],"make":[46],"better":[47],"producing,":[48],"scheduling,":[49],"operating":[51],"decisions.":[52],"However,":[53],"most":[54],"existing":[57],"methods":[58,192],"were":[59],"designed":[60,162],"for":[61,74,117],"predicting":[62],"series":[64],"events,":[67],"which":[68],"not":[70],"directly":[72],"used":[73],"prediction":[77,110],"due":[78],"difficulties":[81],"multi-level":[83,172],"representations":[84,127,130],"and":[87,94,128,154,174,180,196],"sets,":[88,93],"complex":[89],"dependencies":[91,151,177],"evolving":[95],"dynamics":[96],"behaviors.":[99],"To":[100],"address":[101],"these":[102],"issues,":[103],"this":[104],"paper":[105],"provides":[106],"a":[107,137,141,157],"novel":[108],"method,":[111],"called":[112],"DSNTSP":[113],"(Dual":[114],"Sequential":[115],"Network":[116],"Temporal":[118],"Sets":[119],"Prediction).":[120],"Our":[121],"model":[122],"first":[123],"learns":[124],"both":[125],"item-level":[126],"set-level":[129],"sequences":[133],"separately":[134],"based":[135],"on":[136,185],"transformer":[138],"framework.":[139],"Then,":[140],"co-transformer":[142],"module":[143,160],"is":[144,161],"proposed":[145],"capture":[147],"multiple":[149,175],"Last,":[156],"gated":[158],"neural":[159],"predict":[164],"by":[168],"fusing":[169],"all":[170],"correlations":[173],"The":[182],"experimental":[183],"results":[184],"real-world":[186],"data":[187],"show":[189],"that":[190],"our":[191],"lead":[193],"significant":[195],"consistent":[197],"improvements":[198],"compared":[200],"other":[202],"methods.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}