{"id":"https://openalex.org/W7147652271","doi":"https://doi.org/10.48550/arxiv.2603.27952","title":"On the Accuracy Limits of Sequential Recommender Systems: An Entropy-Based Approach","display_name":"On the Accuracy Limits of Sequential Recommender Systems: An Entropy-Based Approach","publication_year":2026,"publication_date":"2026-03-30","ids":{"openalex":"https://openalex.org/W7147652271","doi":"https://doi.org/10.48550/arxiv.2603.27952"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.27952","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27952","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.2603.27952","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033488612","display_name":"En Xu","orcid":"https://orcid.org/0000-0002-8654-2788"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, En","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132589881","display_name":"Jingtao Ding","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ding, Jingtao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132541734","display_name":"Yong Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yong","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":0.7281000018119812,"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":0.7281000018119812,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.17720000445842743,"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"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.00989999994635582,"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/predictability","display_name":"Predictability","score":0.6992999911308289},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5877000093460083},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.48969998955726624},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.48420000076293945},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.43220001459121704},{"id":"https://openalex.org/keywords/novelty","display_name":"Novelty","score":0.39320001006126404},{"id":"https://openalex.org/keywords/randomness","display_name":"Randomness","score":0.3634999990463257},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.3418999910354614}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7634999752044678},{"id":"https://openalex.org/C197640229","wikidata":"https://www.wikidata.org/wiki/Q2534066","display_name":"Predictability","level":2,"score":0.6992999911308289},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5877000093460083},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5250999927520752},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5185999870300293},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.48969998955726624},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.48420000076293945},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.43220001459121704},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4081000089645386},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.39320001006126404},{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.3634999990463257},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.32600000500679016},{"id":"https://openalex.org/C80478641","wikidata":"https://www.wikidata.org/wiki/Q195771","display_name":"Sequential analysis","level":2,"score":0.31139999628067017},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.30379998683929443},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C132459708","wikidata":"https://www.wikidata.org/wiki/Q744069","display_name":"Extrapolation","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.27469998598098755},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.26350000500679016},{"id":"https://openalex.org/C171752962","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Kullback\u2013Leibler divergence","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.2572000026702881},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.27952","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27952","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.2603.27952","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27952","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],"recommender":[1],"systems":[2],"have":[3],"achieved":[4],"steady":[5],"gains":[6],"in":[7,59,74,87,197],"offline":[8,126],"accuracy,":[9],"yet":[10],"it":[11],"remains":[12,114],"unclear":[13],"how":[14],"close":[15],"current":[16],"models":[17],"are":[18,62],"to":[19,66,116,135],"the":[20,26,105,178],"intrinsic":[21],"accuracy":[22,85,127,188],"limit":[23],"imposed":[24],"by":[25,64,142,145],"data.":[27],"A":[28],"reliable,":[29],"model-agnostic":[30],"estimate":[31],"of":[32],"this":[33],"ceiling":[34],"would":[35],"enable":[36],"principled":[37],"difficulty":[38,109],"assessment":[39],"and":[40,69,99,119,150,193],"headroom":[41],"estimation":[42,53],"before":[43],"costly":[44],"model":[45],"development.":[46],"Existing":[47],"predictability":[48,154,157],"analyses":[49],"typically":[50],"combine":[51],"entropy":[52],"with":[54,124],"Fano's":[55],"inequality":[56],"inversion;":[57],"however,":[58],"recommendation":[60],"they":[61],"hindered":[63],"sensitivity":[65],"candidate-space":[67],"specification":[68],"distortion":[70],"from":[71,166],"Fano-based":[72],"scaling":[73],"low-predictability":[75],"regimes.":[76],"We":[77],"develop":[78],"an":[79],"entropy-induced,":[80],"training-free":[81],"approach":[82],"for":[83,185],"quantifying":[84],"limits":[86],"sequential":[88,130,198],"recommendation,":[89],"yielding":[90],"a":[91,182],"candidate-size-agnostic":[92],"estimate.":[93],"Experiments":[94],"on":[95],"controlled":[96],"synthetic":[97],"generators":[98],"diverse":[100],"real-world":[101],"benchmarks":[102],"show":[103],"that":[104],"estimator":[106,180],"tracks":[107],"oracle-controlled":[108],"more":[110],"faithfully":[111],"than":[112],"baselines,":[113],"insensitive":[115],"candidate-set":[117],"size,":[118],"achieves":[120],"high":[121],"rank":[122],"consistency":[123],"best-achieved":[125],"across":[128],"state-of-the-art":[129],"recommenders":[131],"(Spearman":[132],"rho":[133],"up":[134],"0.914).":[136],"It":[137],"also":[138],"supports":[139],"user-group":[140,191],"diagnostics":[141],"stratifying":[143],"users":[144,168],"novelty":[146],"preference,":[147],"long-tail":[148],"exposure,":[149],"activity,":[151],"revealing":[152],"systematic":[153],"differences.":[155],"Furthermore,":[156],"can":[158],"guide":[159],"training":[160,163],"data":[161,175],"selection:":[162],"sets":[164],"constructed":[165],"high-predictability":[167],"yield":[169],"strong":[170],"downstream":[171],"performance":[172],"under":[173],"reduced":[174],"budgets.":[176],"Overall,":[177],"proposed":[179],"provides":[181],"practical":[183],"reference":[184],"assessing":[186],"attainable":[187],"limits,":[189],"supporting":[190],"diagnostics,":[192],"informing":[194],"data-centric":[195],"decisions":[196],"recommendation.":[199]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-02T00:00:00"}
