{"id":"https://openalex.org/W7165851443","doi":"https://doi.org/10.48550/arxiv.2606.25415","title":"S2-CAR: Segmentation-Supervised Complexity-Adaptive Recommendation","display_name":"S2-CAR: Segmentation-Supervised Complexity-Adaptive Recommendation","publication_year":2026,"publication_date":"2026-06-24","ids":{"openalex":"https://openalex.org/W7165851443","doi":"https://doi.org/10.48550/arxiv.2606.25415"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.25415","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.25415","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.25415","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009365237","display_name":"Linjiang Guo","orcid":"https://orcid.org/0000-0002-1571-356X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Linjiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117112174","display_name":"Nitin Bisht","orcid":"https://orcid.org/0009-0004-5177-0213"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bisht, Nitin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139358156","display_name":"Shiqing Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Shiqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139329156","display_name":"Xianzhi Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xianzhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139365134","display_name":"Guandong Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Guandong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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":0.8450999855995178,"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":0.8450999855995178,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.018300000578165054,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.016100000590085983,"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/segmentation","display_name":"Segmentation","score":0.6545000076293945},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.609000027179718},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5672000050544739},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5515999794006348},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5275999903678894},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5144000053405762},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4498000144958496},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4361000061035156}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7567999958992004},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6545000076293945},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.609000027179718},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5672000050544739},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5515999794006348},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5275999903678894},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5144000053405762},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4498000144958496},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4361000061035156},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43230000138282776},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4284999966621399},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.38839998841285706},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.35370001196861267},{"id":"https://openalex.org/C66882249","wikidata":"https://www.wikidata.org/wiki/Q169336","display_name":"Homogeneous","level":2,"score":0.33079999685287476},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.32580000162124634},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3221000134944916},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.29809999465942383},{"id":"https://openalex.org/C150140777","wikidata":"https://www.wikidata.org/wiki/Q960648","display_name":"Point of interest","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.25415","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.25415","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.25415","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.25415","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":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Sequential":[0],"recommendation":[1,98,211],"aims":[2],"to":[3,79,119],"predict":[4],"user":[5,101],"preferences":[6],"from":[7],"interaction":[8,27],"histories,":[9],"yet":[10,41],"existing":[11,42,209],"models":[12,43,100],"often":[13],"struggle":[14],"when":[15,206],"behavior":[16],"patterns":[17],"become":[18],"complex":[19],"and":[20,93,172,188],"heterogeneous.":[21],"A":[22],"key":[23],"reason":[24],"is":[25],"that":[26,59,99,179,193],"histories":[28],"are":[29],"rarely":[30],"uniform:":[31],"users'":[32],"interests":[33],"shift":[34],"in":[35],"a":[36,50,91,104,145,155,200],"latent":[37,106],"way":[38],"over":[39],"time,":[40],"either":[44],"treat":[45],"the":[46,112,124,194],"full":[47],"sequence":[48,74],"as":[49,103,199],"homogeneous":[51],"context":[52],"or":[53],"rely":[54],"on":[55,81,163],"rigid":[56],"time-window":[57],"segmentation":[58,136,197],"misaligns":[60],"with":[61],"true":[62],"intent":[63,102,135],"boundaries.":[64],"This":[65],"mis-segmentation":[66],"not":[67],"only":[68],"introduces":[69],"cross-intent":[70],"interference":[71],"at":[72],"intermediate":[73],"positions":[75],"but":[76],"also":[77],"leads":[78],"over-reliance":[80],"short-term":[82],"interest":[83],"signals.":[84],"To":[85],"address":[86],"this,":[87],"we":[88],"propose":[89],"S2-CAR,":[90],"segmentation-supervised":[92],"complexity-adaptive":[94],"framework":[95],"for":[96],"sequential":[97,210],"continuous":[105],"energy":[107,129],"state.":[108],"Specifically,":[109],"it":[110],"uses":[111],"Context-Aware":[113],"Soft":[114],"Temporal":[115],"Point":[116],"Process":[117],"(Soft-TPP)":[118],"segment":[120],"boundaries":[121],"triggered":[122],"by":[123],"natural":[125],"decay":[126],"of":[127,158],"latent-state":[128],"rather":[130],"than":[131],"fixed":[132,138],"intervals,":[133],"enabling":[134],"without":[137],"time-gap":[139],"rules.":[140],"Next,":[141],"upon":[142],"this":[143],"segmentation,":[144],"Segment-Count-Adaptive":[146],"Multi-Intent":[147],"Extraction":[148],"module":[149],"hierarchically":[150],"aggregates":[151],"intent-coherent":[152],"segments":[153],"into":[154,208],"compact":[156],"set":[157],"multi-interest":[159],"representations.":[160],"Extensive":[161],"experiments":[162],"3":[164],"representative":[165],"public":[166],"benchmark":[167],"datasets":[168,187],"spanning":[169],"movie,":[170],"e-commerce,":[171],"gaming":[173],"domains":[174],"across":[175,185],"13":[176],"baselines":[177],"demonstrate":[178],"S2-CAR":[180],"consistently":[181],"outperforms":[182],"state-of-the-art":[183],"methods":[184],"all":[186],"metrics.":[189],"Further":[190],"analysis":[191],"shows":[192],"proposed":[195],"energy-based":[196],"serves":[198],"plug-and-play":[201],"module,":[202],"yielding":[203],"consistent":[204],"improvements":[205],"integrated":[207],"backbones.":[212]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-26T00:00:00"}