{"id":"https://openalex.org/W7164892904","doi":"https://doi.org/10.48550/arxiv.2606.15252","title":"Beyond Positive Signals: Unlocking Implicit Negative Behaviors for Enhanced Sequential User Modeling","display_name":"Beyond Positive Signals: Unlocking Implicit Negative Behaviors for Enhanced Sequential User Modeling","publication_year":2026,"publication_date":"2026-06-13","ids":{"openalex":"https://openalex.org/W7164892904","doi":"https://doi.org/10.48550/arxiv.2606.15252"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.15252","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.15252","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.2606.15252","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138702286","display_name":"Zexuan Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Zexuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138712498","display_name":"Yue Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138721751","display_name":"Jun Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138734828","display_name":"Jie Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Jie","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":0.7502999901771545,"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":0.7502999901771545,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.026900000870227814,"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"}},{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.02500000037252903,"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/polarity","display_name":"Polarity (international relations)","score":0.5735999941825867},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5550000071525574},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5141000151634216},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.45159998536109924},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4383000135421753},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.39010000228881836},{"id":"https://openalex.org/keywords/mechanism","display_name":"Mechanism (biology)","score":0.38690000772476196},{"id":"https://openalex.org/keywords/unified-model","display_name":"Unified Model","score":0.3828999996185303}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7282000184059143},{"id":"https://openalex.org/C2777361361","wikidata":"https://www.wikidata.org/wiki/Q1112585","display_name":"Polarity (international relations)","level":3,"score":0.5735999941825867},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5550000071525574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5152999758720398},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5141000151634216},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.45159998536109924},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4383000135421753},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.42260000109672546},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.39010000228881836},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.38690000772476196},{"id":"https://openalex.org/C45493050","wikidata":"https://www.wikidata.org/wiki/Q7884934","display_name":"Unified Model","level":2,"score":0.3828999996185303},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3582000136375427},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.3418000042438507},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.3400999903678894},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.310699999332428},{"id":"https://openalex.org/C78639753","wikidata":"https://www.wikidata.org/wiki/Q3318160","display_name":"Behavioral modeling","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.28060001134872437},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2721000015735626},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2703000009059906},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2696000039577484},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C80478641","wikidata":"https://www.wikidata.org/wiki/Q195771","display_name":"Sequential analysis","level":2,"score":0.26249998807907104}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.15252","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.15252","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.2606.15252","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.15252","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"User":[0],"behavior":[1,59,114],"sequence":[2],"modeling":[3,105],"has":[4,21],"become":[5],"a":[6,49,124,144,159],"central":[7],"component":[8],"in":[9],"modern":[10],"click-through":[11],"rate":[12],"(CTR)":[13],"prediction.":[14],"Over":[15],"the":[16,19,58,73,103,174,180,186,190,214],"past":[17],"years,":[18],"community":[20],"invested":[22],"substantial":[23],"effort":[24],"into":[25],"improving":[26],"how":[27],"sequences":[28,64,92,131],"are":[29,84],"encoded,":[30],"from":[31,66,89],"target-aware":[32],"attention":[33],"and":[34,45,120,153],"interest":[35],"evolution":[36],"networks":[37],"to":[38,149,205],"unified":[39],"architectures":[40,135],"that":[41,112,164,185],"jointly":[42],"process":[43],"sequential":[44,104],"non-sequential":[46],"features.":[47],"However,":[48],"more":[50,75],"fundamental":[51],"question":[52],"remains":[53],"under-explored:":[54],"what":[55],"should":[56],"constitute":[57],"sequence?":[60],"Current":[61],"practice":[62],"constructs":[63],"exclusively":[65],"positive":[67,91,119],"interactions":[68],"(clicks,":[69],"purchases,":[70],"completions),":[71],"while":[72],"far":[74],"abundant":[76],"implicit":[77],"negative":[78,121],"behaviors":[79],"(skips,":[80],"low":[81],"engagement,":[82],"scroll-past)":[83],"largely":[85],"underutilized.":[86],"As":[87],"gains":[88,167],"longer":[90],"approach":[93],"diminishing":[94],"returns,":[95],"we":[96,110],"revisit":[97],"this":[98,108],"underutilized":[99],"data":[100,192],"source":[101],"within":[102,123],"framework.":[106],"In":[107],"paper,":[109],"demonstrate":[111,200],"mixed-polarity":[113,191],"sequences,":[115],"which":[116,212],"chronologically":[117],"interleave":[118],"tokens":[122],"fixed":[125],"length":[126],"budget,":[127],"consistently":[128],"outperform":[129],"positive-only":[130],"across":[132,209],"diverse":[133],"model":[134],"with":[136],"negligible":[137],"additional":[138,166],"computational":[139],"overhead.":[140],"We":[141],"further":[142],"identify":[143],"semantic":[145],"indistinguishability":[146],"problem":[147],"inherent":[148],"naive":[150],"polarity":[151,176],"embeddings":[152],"propose":[154],"Target-Aware":[155],"Polarity":[156],"Fusion":[157],"(TAPF),":[158],"lightweight":[160],"target-conditioned":[161],"gating":[162],"mechanism":[163],"provides":[165],"by":[168],"differentiating":[169],"behavioral":[170],"evidence.":[171],"Notably,":[172],"even":[173],"simpler":[175],"bias":[177],"baseline":[178],"captures":[179],"majority":[181],"of":[182,203,217],"improvement,":[183],"underscoring":[184],"primary":[187],"contribution":[188],"is":[189],"paradigm":[193],"itself.":[194],"Experiments":[195],"on":[196],"three":[197],"public":[198],"benchmarks":[199],"consistent":[201],"improvements":[202],"+1.9%":[204],"+9.6%":[206],"relative":[207],"AUC":[208],"five":[210],"architectures,":[211],"validate":[213],"practical":[215],"value":[216],"our":[218],"approach.":[219]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-17T00:00:00"}
