{"id":"https://openalex.org/W7151544878","doi":"https://doi.org/10.48550/arxiv.2604.04530","title":"SLSREC: Self-Supervised Contrastive Learning for Adaptive Fusion of Long- and Short-Term User Interests","display_name":"SLSREC: Self-Supervised Contrastive Learning for Adaptive Fusion of Long- and Short-Term User Interests","publication_year":2026,"publication_date":"2026-04-06","ids":{"openalex":"https://openalex.org/W7151544878","doi":"https://doi.org/10.48550/arxiv.2604.04530"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.04530","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04530","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.04530","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133145892","display_name":"Wei Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhou, Wei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133082959","display_name":"Yue Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Yue","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133083855","display_name":"Junkai Ji","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ji, Junkai","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063548093","display_name":"Yinglan Feng","orcid":"https://orcid.org/0000-0002-2420-6439"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Yinglan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133119615","display_name":"Xing Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Xing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133107393","display_name":"Xiuqiang He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Xiuqiang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133075640","display_name":"Liang Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Liang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133073845","display_name":"Zexuan Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Zexuan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5133145892"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.7843000292778015,"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":0.7843000292778015,"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/T12607","display_name":"Personal Information Management and User Behavior","score":0.02199999988079071,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.01850000023841858,"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/robustness","display_name":"Robustness (evolution)","score":0.7257000207901001},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5278000235557556},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.5273000001907349},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.3864000141620636},{"id":"https://openalex.org/keywords/user-modeling","display_name":"User modeling","score":0.36559998989105225},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.320499986410141},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.31209999322891235}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7943000197410583},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7257000207901001},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5374000072479248},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5278000235557556},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.5273000001907349},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4406000077724457},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3864000141620636},{"id":"https://openalex.org/C67712803","wikidata":"https://www.wikidata.org/wiki/Q7901853","display_name":"User modeling","level":3,"score":0.36559998989105225},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.32589998841285706},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.31209999322891235},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.30799999833106995},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25839999318122864},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.04530","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04530","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.04530","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04530","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"User":[0],"interests":[1,41,72,88],"typically":[2],"encompass":[3],"both":[4],"long-term":[5],"preferences":[6],"and":[7,61,85,121],"short-term":[8,86,122],"intentions,":[9],"reflecting":[10],"the":[11,28,57,67],"dynamic":[12],"nature":[13],"of":[14,24,31,59,70,107,119],"user":[15,25,71,87],"behaviors":[16,76],"across":[17,162],"different":[18],"timeframes.":[19],"The":[20],"uneven":[21],"temporal":[22,68],"distribution":[23],"interactions":[26],"highlights":[27],"evolving":[29],"patterns":[30],"interests,":[32],"making":[33],"it":[34],"challenging":[35],"to":[36,102,115,132,140],"accurately":[37],"capture":[38],"shifts":[39],"in":[40],"using":[42],"comprehensive":[43],"historical":[44,75],"behaviors.":[45],"To":[46],"address":[47],"this,":[48],"we":[49],"propose":[50],"SLSRec,":[51],"a":[52,90,98],"novel":[53],"Session-based":[54],"model":[55],"with":[56],"fusion":[58,128],"Long-":[60],"Short-term":[62],"Recommendations":[63],"that":[64,82,152],"effectively":[65],"captures":[66],"dynamics":[69],"by":[73],"segmenting":[74],"over":[77],"time.":[78],"Unlike":[79],"conventional":[80],"models":[81,157],"combine":[83],"long-":[84,120],"into":[89],"single":[91],"representation,":[92],"compromising":[93],"recommendation":[94,142],"accuracy,":[95],"SLSRec":[96,153],"utilizes":[97],"self-supervised":[99],"learning":[100,111],"framework":[101],"disentangle":[103],"these":[104],"two":[105],"types":[106],"interests.":[108],"A":[109],"contrastive":[110],"strategy":[112],"is":[113,130],"introduced":[114],"ensure":[116],"accurate":[117],"calibration":[118],"interest":[123,135],"representations.":[124],"Additionally,":[125],"an":[126],"attention-based":[127],"network":[129],"designed":[131],"adaptively":[133],"aggregate":[134],"representations,":[136],"optimizing":[137],"their":[138],"integration":[139],"enhance":[141],"performance.":[143],"Extensive":[144],"experiments":[145],"on":[146],"three":[147],"public":[148],"benchmark":[149],"datasets":[150],"demonstrate":[151],"consistently":[154],"outperforms":[155],"state-of-the-art":[156],"while":[158],"exhibiting":[159],"superior":[160],"robustness":[161],"various":[163],"scenarios.We":[164],"will":[165],"release":[166],"all":[167],"source":[168],"code":[169],"upon":[170],"acceptance.":[171]},"counts_by_year":[],"updated_date":"2026-04-08T06:07:18.267832","created_date":"2026-04-08T00:00:00"}
