{"id":"https://openalex.org/W7154715148","doi":"https://doi.org/10.48550/arxiv.2604.14581","title":"Behavior-Aware Dual-Channel Preference Learning for Heterogeneous Sequential Recommendation","display_name":"Behavior-Aware Dual-Channel Preference Learning for Heterogeneous Sequential Recommendation","publication_year":2026,"publication_date":"2026-04-16","ids":{"openalex":"https://openalex.org/W7154715148","doi":"https://doi.org/10.48550/arxiv.2604.14581"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.14581","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.14581","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2604.14581","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133868167","display_name":"Jing Xiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xiao, Jing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133906342","display_name":"Dongqi Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Dongqi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133850290","display_name":"Liwei Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Liwei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133845343","display_name":"Yawen Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Yawen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133856097","display_name":"Weike Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Weike","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133845836","display_name":"Zhong Ming","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ming, Zhong","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5133868167"],"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":0.954200029373169,"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":0.954200029373169,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.012900000438094139,"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"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.0031999999191612005,"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/preference","display_name":"Preference","score":0.527899980545044},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.5092999935150146},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4982999861240387},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4699000120162964},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.413100004196167},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.4027000069618225},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.34119999408721924},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.33219999074935913},{"id":"https://openalex.org/keywords/user-modeling","display_name":"User modeling","score":0.32820001244544983}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8066999912261963},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6100999712944031},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5351999998092651},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.527899980545044},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.5092999935150146},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4982999861240387},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4699000120162964},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.413100004196167},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.4027000069618225},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.34119999408721924},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3368000090122223},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33219999074935913},{"id":"https://openalex.org/C67712803","wikidata":"https://www.wikidata.org/wiki/Q7901853","display_name":"User modeling","level":3,"score":0.32820001244544983},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3271999955177307},{"id":"https://openalex.org/C181204326","wikidata":"https://www.wikidata.org/wiki/Q7239820","display_name":"Preference learning","level":3,"score":0.3174000084400177},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.31189998984336853},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.28679999709129333},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.28679999709129333},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C173414695","wikidata":"https://www.wikidata.org/wiki/Q5510276","display_name":"Fusion mechanism","level":4,"score":0.27619999647140503},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.2671000063419342}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.14581","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.14581","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.14581","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.14581","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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":{"Heterogeneous":[0],"sequential":[1,20],"recommendation":[2,52],"(HSR)":[3],"aims":[4],"to":[5,17,126,140,176],"learn":[6],"dynamic":[7],"behavior":[8,35,74,91,129],"dependencies":[9],"from":[10],"the":[11,41,51,69,72,120,167,178,181,187,197,203],"diverse":[12],"behaviors":[13,56],"of":[14,44,71,122,199],"user-item":[15],"interactions":[16],"facilitate":[18],"precise":[19],"recommendation.":[21],"Despite":[22],"many":[23],"efforts":[24],"yielding":[25],"promising":[26],"achievements,":[27],"there":[28],"are":[29],"still":[30],"challenges":[31],"in":[32,83],"modeling":[33],"heterogeneous":[34],"data.":[36],"One":[37],"significant":[38],"issue":[39],"is":[40],"inherent":[42],"sparsity":[43,70],"a":[45,89,109,134,153],"real-world":[46,194],"data,":[47],"which":[48],"can":[49],"weaken":[50],"performance.":[53],"Although":[54],"auxiliary":[55],"(e.g.,":[57,75],"clicks)":[58],"partially":[59],"address":[60,102],"this":[61],"problem,":[62],"they":[63],"inevitably":[64],"introduce":[65],"some":[66],"noise,":[67],"and":[68,97,148,161],"target":[73,188],"purchases)":[76],"remains":[77],"unresolved.":[78],"Additionally,":[79],"contrastive":[80,155],"learning-based":[81],"augmentation":[82],"existing":[84],"methods":[85],"often":[86],"focuses":[87],"on":[88,192],"single":[90],"type,":[92],"overlooking":[93],"fine-grained":[94],"user":[95,150,182],"preferences":[96],"losing":[98],"valuable":[99],"information.":[100,144],"To":[101],"these":[103],"challenges,":[104],"we":[105,165],"have":[106],"meticulously":[107],"designed":[108],"behavior-aware":[110,124],"dual-channel":[111],"preference":[112,169],"learning":[113,156],"framework":[114,117],"(BDPL).":[115],"This":[116],"begins":[118],"with":[119,185],"construction":[121],"customized":[123],"subgraphs":[125],"capture":[127],"personalized":[128],"transition":[130],"relationships,":[131],"followed":[132],"by":[133],"novel":[135],"cascade-structured":[136],"graph":[137],"neural":[138],"network":[139],"aggregate":[141],"node":[142],"context":[143],"We":[145],"then":[146],"model":[147],"enhance":[149],"representations":[151],"through":[152],"preference-level":[154],"paradigm,":[157],"considering":[158],"both":[159],"long-term":[160],"short-term":[162],"preferences.":[163],"Finally,":[164],"fuse":[166],"overall":[168],"information":[170],"using":[171],"an":[172],"adaptive":[173],"gating":[174],"mechanism":[175],"predict":[177],"next":[179],"item":[180],"will":[183],"interact":[184],"under":[186],"behavior.":[189],"Extensive":[190],"experiments":[191],"three":[193],"datasets":[195],"demonstrate":[196],"superiority":[198],"our":[200],"BDPL":[201],"over":[202],"state-of-the-art":[204],"models.":[205]},"counts_by_year":[],"updated_date":"2026-04-18T06:05:20.339008","created_date":"2026-04-18T00:00:00"}