{"id":"https://openalex.org/W4387848554","doi":"https://doi.org/10.1145/3583780.3614788","title":"AutoSeqRec: Autoencoder for Efficient Sequential Recommendation","display_name":"AutoSeqRec: Autoencoder for Efficient Sequential Recommendation","publication_year":2023,"publication_date":"2023-10-21","ids":{"openalex":"https://openalex.org/W4387848554","doi":"https://doi.org/10.1145/3583780.3614788"},"language":"en","primary_location":{"id":"doi:10.1145/3583780.3614788","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3614788","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":"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/A5063912331","display_name":"S. B. Liu","orcid":"https://orcid.org/0009-0000-1213-6974"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sijia Liu","raw_affiliation_strings":["Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0000-1213-6974","affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022905969","display_name":"Jiahao Liu","orcid":"https://orcid.org/0000-0002-5654-5902"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiahao Liu","raw_affiliation_strings":["Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-5654-5902","affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071156485","display_name":"Hansu Gu","orcid":"https://orcid.org/0000-0002-1426-3210"},"institutions":[{"id":"https://openalex.org/I2802723755","display_name":"Independent Sector","ror":"https://ror.org/05vhwqa91","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I2802723755"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hansu Gu","raw_affiliation_strings":["Independent, Seattle, WA, USA"],"raw_orcid":"https://orcid.org/0000-0002-1426-3210","affiliations":[{"raw_affiliation_string":"Independent, Seattle, WA, USA","institution_ids":["https://openalex.org/I2802723755"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100440920","display_name":"Dongsheng Li","orcid":"https://orcid.org/0000-0003-3103-8442"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongsheng Li","raw_affiliation_strings":["Microsoft Research Asia, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-3103-8442","affiliations":[{"raw_affiliation_string":"Microsoft Research Asia, Shanghai, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004237040","display_name":"Tun Lu","orcid":"https://orcid.org/0000-0002-6633-4826"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tun Lu","raw_affiliation_strings":["Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-6633-4826","affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100364191","display_name":"Peng Zhang","orcid":"https://orcid.org/0000-0002-9109-4625"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Zhang","raw_affiliation_strings":["Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-9109-4625","affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091087409","display_name":"Ning Gu","orcid":"https://orcid.org/0000-0002-2915-974X"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Gu","raw_affiliation_strings":["Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-2915-974X","affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1493","last_page":"1502"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":1.0,"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":1.0,"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":0.9829000234603882,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.954800009727478,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8256044387817383},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.7543364763259888},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6731075048446655},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6730329990386963},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.6507948040962219},{"id":"https://openalex.org/keywords/row","display_name":"Row","score":0.6275509595870972},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.47907763719558716},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45383220911026},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.4505234658718109},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.45036008954048157},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43959909677505493},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4316543936729431},{"id":"https://openalex.org/keywords/stochastic-matrix","display_name":"Stochastic matrix","score":0.4249698221683502},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.38215136528015137},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3653147220611572},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.1673704981803894},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.09988826513290405}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8256044387817383},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.7543364763259888},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6731075048446655},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6730329990386963},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.6507948040962219},{"id":"https://openalex.org/C135598885","wikidata":"https://www.wikidata.org/wiki/Q1366302","display_name":"Row","level":2,"score":0.6275509595870972},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.47907763719558716},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45383220911026},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.4505234658718109},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.45036008954048157},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43959909677505493},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4316543936729431},{"id":"https://openalex.org/C49555168","wikidata":"https://www.wikidata.org/wiki/Q176583","display_name":"Stochastic matrix","level":3,"score":0.4249698221683502},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.38215136528015137},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3653147220611572},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.1673704981803894},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.09988826513290405},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3583780.3614788","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3614788","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":[],"awards":[{"id":"https://openalex.org/G266795963","display_name":null,"funder_award_id":"61932007","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4817111480","display_name":null,"funder_award_id":"62172106","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7070432672","display_name":null,"funder_award_id":"62172106,61932007","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1720514416","https://openalex.org/W1987431925","https://openalex.org/W1994389483","https://openalex.org/W2038585576","https://openalex.org/W2042281163","https://openalex.org/W2054141820","https://openalex.org/W2253995343","https://openalex.org/W2583674722","https://openalex.org/W2605350416","https://openalex.org/W2739805805","https://openalex.org/W2773640334","https://openalex.org/W2783272285","https://openalex.org/W2808908091","https://openalex.org/W2904012554","https://openalex.org/W2912745432","https://openalex.org/W2945827670","https://openalex.org/W2963085847","https://openalex.org/W2963367478","https://openalex.org/W2965683718","https://openalex.org/W2978624918","https://openalex.org/W2991829564","https://openalex.org/W3004847162","https://openalex.org/W3019863187","https://openalex.org/W3026076535","https://openalex.org/W3045200674","https://openalex.org/W3094559936","https://openalex.org/W3100278010","https://openalex.org/W3101708421","https://openalex.org/W3203585941","https://openalex.org/W3210512903","https://openalex.org/W4206423807","https://openalex.org/W4224314868","https://openalex.org/W4224323490","https://openalex.org/W4297794619","https://openalex.org/W4297971002","https://openalex.org/W4306815981","https://openalex.org/W4319452302","https://openalex.org/W6741729866"],"related_works":["https://openalex.org/W3109911900","https://openalex.org/W4312998587","https://openalex.org/W1575318294","https://openalex.org/W3080740766","https://openalex.org/W3166581859","https://openalex.org/W2909865466","https://openalex.org/W2032039661","https://openalex.org/W4386143129","https://openalex.org/W2908124738","https://openalex.org/W2000026009"],"abstract_inverted_index":{"Sequential":[0],"recommendation":[1,65,71],"demonstrates":[2],"the":[3,10,25,37,87,94,99,104,111,124,129,134,145,154,162],"capability":[4],"to":[5,158,164],"recommend":[6],"items":[7],"by":[8,35],"modeling":[9,144],"sequential":[11,70],"behavior":[12],"of":[13,22,49,80,103,110,128,182],"users.":[14],"Traditional":[15],"methods":[16,31,43,179],"typically":[17],"treat":[18],"users":[19],"as":[20],"sequences":[21],"items,":[23],"overlooking":[24],"collaborative":[26,33,120],"relationships":[27],"among":[28],"them.":[29],"Graph-based":[30],"incorporate":[32],"information":[34],"utilizing":[36],"user-item":[38,95,112],"interaction":[39,96,113],"graph.":[40],"However,":[41],"these":[42,57],"sometimes":[44],"face":[45],"challenges":[46],"in":[47,180],"terms":[48,181],"time":[50],"complexity":[51],"and":[52,78,83,98,101,126,137,188],"computational":[53],"efficiency.":[54,189],"To":[55],"address":[56],"limitations,":[58],"this":[59],"paper":[60],"presents":[61],"AutoSeqRec,":[62],"an":[63,81],"incremental":[64,151],"model":[66],"specifically":[67],"designed":[68],"for":[69,143],"tasks.":[72],"AutoSeqRec":[73,169,176],"is":[74],"based":[75],"on":[76],"autoencoders":[77],"consists":[79],"encoder":[82],"three":[84],"decoders":[85],"within":[86],"autoencoder":[88],"architecture.":[89],"These":[90],"components":[91],"consider":[92],"both":[93],"matrix":[97,114,132],"rows":[100,125],"columns":[102,127],"item":[105,130,135],"transition":[106,131],"matrix.":[107],"The":[108],"reconstruction":[109],"captures":[115],"user":[116],"long-term":[117],"preferences":[118],"through":[119],"filtering.":[121],"In":[122],"addition,":[123],"represent":[133],"out-degree":[136],"in-degree":[138],"hopping":[139],"behavior,":[140],"which":[141,167],"allows":[142],"user's":[146],"short-term":[147],"interests.":[148],"When":[149],"making":[150],"recommendations,":[152],"only":[153],"input":[155],"matrices":[156],"need":[157,163],"be":[159],"updated,":[160],"without":[161],"update":[165],"parameters,":[166],"makes":[168],"very":[170],"efficient.":[171],"Comprehensive":[172],"evaluations":[173],"demonstrate":[174],"that":[175],"outperforms":[177],"existing":[178],"accuracy,":[183],"while":[184],"showcasing":[185],"its":[186],"robustness":[187]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":10}],"updated_date":"2026-07-16T13:24:37.021932","created_date":"2025-10-10T00:00:00"}