{"id":"https://openalex.org/W7151878218","doi":"https://doi.org/10.48550/arxiv.2604.05309","title":"Pay Attention to Sequence Split: Uncovering the Impacts of Sub-Sequence Splitting on Sequential Recommendation Models","display_name":"Pay Attention to Sequence Split: Uncovering the Impacts of Sub-Sequence Splitting on Sequential Recommendation Models","publication_year":2026,"publication_date":"2026-04-07","ids":{"openalex":"https://openalex.org/W7151878218","doi":"https://doi.org/10.48550/arxiv.2604.05309"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.05309","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05309","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.05309","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133148343","display_name":"Yizhou Dang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dang, Yizhou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133223513","display_name":"Yifan Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052268974","display_name":"Minhan Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Minhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133203548","display_name":"Chuang Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Chuang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133201260","display_name":"Lianbo Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Lianbo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133206468","display_name":"Guibing Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Guibing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133209844","display_name":"Xingwei Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xingwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133184271","display_name":"Zhu Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Zhu","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":0.9434000253677368,"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":0.9434000253677368,"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":0.014100000262260437,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.009999999776482582,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/sss*","display_name":"SSS*","score":0.7847999930381775},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.6172999739646912},{"id":"https://openalex.org/keywords/harm","display_name":"Harm","score":0.438400000333786},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.4372999966144562},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.42829999327659607},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.42480000853538513}],"concepts":[{"id":"https://openalex.org/C148699463","wikidata":"https://www.wikidata.org/wiki/Q3492668","display_name":"SSS*","level":2,"score":0.7847999930381775},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6794000267982483},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.6172999739646912},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45829999446868896},{"id":"https://openalex.org/C2777363581","wikidata":"https://www.wikidata.org/wiki/Q15098235","display_name":"Harm","level":2,"score":0.438400000333786},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4372999966144562},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.42829999327659607},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.42480000853538513},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3109999895095825},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.28929999470710754},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.2892000079154968},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2732999920845032},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2644999921321869}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.05309","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05309","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.05309","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05309","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":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.8265824913978577}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Sub-sequence":[0],"splitting":[1,19,106,131,152,213],"(SSS)":[2],"has":[3],"been":[4],"demonstrated":[5,31],"as":[6],"an":[7],"effective":[8],"approach":[9],"to":[10,34,113,207],"mitigate":[11],"data":[12,73,169,192,212,227],"sparsity":[13],"in":[14,43,78],"sequential":[15],"recommendation":[16],"(SR)":[17],"by":[18,102],"a":[20,189,236],"raw":[21],"user":[22],"interaction":[23],"sequence":[24],"into":[25],"multiple":[26],"sub-sequences.":[27],"Previous":[28],"studies":[29],"have":[30],"its":[32],"ability":[33],"enhance":[35],"the":[36,54,57,72,79,165,172,204,209],"performance":[37,86,156],"of":[38,56,93,167,211],"SR":[39,67,96,215],"models":[40,68,110],"significantly.":[41],"However,":[42],"this":[44,84,200],"work,":[45],"we":[46,82,148,180],"discover":[47],"that":[48,63,92,122,160,174],"\\textbf{(i).":[49],"SSS":[50,70,103,117,124,185],"may":[51,143],"interfere":[52],"with":[53,188],"evaluation":[55],"model's":[58],"actual":[59],"performance.}":[60],"We":[61,120,198,234],"observed":[62],"many":[64],"recent":[65],"state-of-the-art":[66],"employ":[69],"during":[71],"reading":[74],"stage":[75],"(not":[76],"mentioned":[77],"papers).":[80],"When":[81],"removed":[83],"operation,":[85],"significantly":[87],"declined,":[88],"even":[89,144],"falling":[90],"below":[91],"earlier":[94],"classical":[95],"models.":[97],"The":[98],"varying":[99],"improvements":[100],"achieved":[101],"and":[104,135,158,195,216,226],"different":[105,109,175],"methods":[107,194],"across":[108],"prompt":[111,203],"us":[112],"analyze":[114,149],"further":[115],"when":[116,129],"proves":[118],"effective.":[119],"find":[121,159],"\\textbf{(ii).":[123],"demonstrates":[125],"strong":[126],"capabilities":[127],"only":[128],"specific":[130],"methods,":[132],"target":[133],"strategies,":[134],"loss":[136],"functions":[137],"are":[138,177],"used":[139],"together.}":[140],"Inappropriate":[141],"combinations":[142],"harm":[145],"performance.":[146],"Furthermore,":[147],"why":[150],"sub-sequence":[151],"yields":[153],"such":[154],"remarkable":[155],"gains":[157],"\\textbf{(iii).":[161],"it":[162],"evens":[163],"out":[164],"distribution":[166],"training":[168],"while":[170],"increasing":[171],"likelihood":[173],"items":[176],"targeted.}":[178],"Finally,":[179],"provide":[181,235],"suggestions":[182],"for":[183],"overcoming":[184],"interference,":[186],"along":[187],"discussion":[190],"on":[191,214],"augmentation":[193],"future":[196],"directions.":[197],"hope":[199],"work":[201],"will":[202,228],"broader":[205],"community":[206],"re-examine":[208],"impact":[210],"promote":[217],"fairer,":[218],"more":[219],"rigorous":[220],"model":[221],"evaluation.":[222],"All":[223],"analysis":[224],"code":[225],"be":[229],"made":[230],"available":[231],"upon":[232],"acceptance.":[233],"simple,":[237],"anonymous":[238],"implementation":[239],"at":[240],"https://github.com/KingGugu/SSS4SR.":[241]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-09T00:00:00"}