{"id":"https://openalex.org/W7160825131","doi":"https://doi.org/10.48550/arxiv.2605.07125","title":"An Embarrassingly Simple Graph Heuristic Reveals Shortcut-Solvable Benchmarks for Sequential Recommendation","display_name":"An Embarrassingly Simple Graph Heuristic Reveals Shortcut-Solvable Benchmarks for Sequential Recommendation","publication_year":2026,"publication_date":"2026-05-08","ids":{"openalex":"https://openalex.org/W7160825131","doi":"https://doi.org/10.48550/arxiv.2605.07125"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.07125","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07125","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.2605.07125","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135893892","display_name":"Haoyu Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Haoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135878623","display_name":"Li Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135856973","display_name":"Hanbing Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Hanbing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135861352","display_name":"Bingheng Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Bingheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058071176","display_name":"Daochen Zha","orcid":"https://orcid.org/0000-0002-6677-7504"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zha, Daochen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024389077","display_name":"Chun How Tan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan, Chun How","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068625546","display_name":"Huiji Gao","orcid":"https://orcid.org/0009-0006-0424-248X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Huiji","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135838025","display_name":"Xin Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120301349","display_name":"Stephanie Moyerman","orcid":"https://orcid.org/0000-0003-0526-1077"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Moyerman, Stephanie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087282245","display_name":"Sanjeev Katariya","orcid":"https://orcid.org/0009-0008-1519-174X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Katariya, Sanjeev","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135877210","display_name":"Hui Liu","orcid":"https://orcid.org/0000-0002-0415-722X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Hui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135874447","display_name":"Jiliang Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Jiliang","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":0.7843000292778015,"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":0.7843000292778015,"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":0.0877000018954277,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.01810000091791153,"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/generative-grammar","display_name":"Generative grammar","score":0.6868000030517578},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.5777000188827515},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5636000037193298},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5109999775886536},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5002999901771545},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.45419999957084656},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.3675000071525574},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.3472999930381775},{"id":"https://openalex.org/keywords/decipher","display_name":"DECIPHER","score":0.3452000021934509}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7551000118255615},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6868000030517578},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.5777000188827515},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5636000037193298},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5473999977111816},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5109999775886536},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5002999901771545},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48919999599456787},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.45419999957084656},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3675000071525574},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36550000309944153},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3472999930381775},{"id":"https://openalex.org/C164614171","wikidata":"https://www.wikidata.org/wiki/Q5204775","display_name":"DECIPHER","level":2,"score":0.3452000021934509},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.33169999718666077},{"id":"https://openalex.org/C199521495","wikidata":"https://www.wikidata.org/wiki/Q181487","display_name":"Audit","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2906999886035919},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C2779010991","wikidata":"https://www.wikidata.org/wiki/Q2720909","display_name":"Artifact (error)","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.25380000472068787},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.07125","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07125","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.2605.07125","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07125","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":[],"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":[1,249],"has":[2],"increasingly":[3],"shifted":[4],"toward":[5],"generative":[6,45,90,227],"recommenders":[7,46],"that":[8,43,123,141,213],"combine":[9],"sequential":[10],"patterns":[11],"with":[12,55,102,198],"semantic":[13],"item":[14],"information.":[15],"Yet":[16],"these":[17,35],"methods":[18],"are":[19],"often":[20],"evaluated":[21],"on":[22,115,157,206,216],"a":[23,31,52,75],"small":[24],"set":[25],"of":[26,106,133,186,208],"widely":[27],"used":[28],"benchmarks,":[29],"raising":[30],"key":[32],"question:":[33],"do":[34],"benchmarks":[36,218,243],"actually":[37],"require":[38],"the":[39,64,111,184,202],"advanced":[40,223],"modeling":[41,228],"capabilities":[42],"modern":[44,100],"claim":[47],"to":[48,244],"provide?":[49],"We":[50,121,136,230],"conduct":[51],"benchmark":[53],"audit":[54],"an":[56,131],"intentionally":[57],"simple":[58,175],"graph":[59,78],"heuristic.":[60,135],"Starting":[61],"from":[62,74],"only":[63],"last":[65],"one":[66,134,168],"or":[67,92,97,169,226],"two":[68,170],"interacted":[69],"items,":[70],"it":[71],"retrieves":[72],"candidates":[73],"few-hop":[76],"item-transition":[77],"and":[79,108,119,154,237],"ranks":[80],"them":[81,182],"by":[82],"item-feature":[83],"similarity.":[84],"Despite":[85],"using":[86,242],"no":[87],"sequence":[88],"encoder,":[89],"objective,":[91],"training,":[93],"this":[94,124],"heuristic":[95,203],"matches":[96],"outperforms":[98],"many":[99],"baselines,":[101],"relative":[103],"NDCG@10":[104],"improvements":[105],"38.10%":[107],"44.18%":[109],"over":[110],"best":[112],"competing":[113],"baseline":[114],"Amazon":[116],"Review":[117],"Sports":[118],"CDs.":[120],"show":[122],"behavior":[125],"reflects":[126],"shortcut":[127,139],"solvability":[128],"rather":[129],"than":[130,147],"artifact":[132],"identify":[137],"three":[138],"structures":[140],"can":[142,173],"make":[143,174],"next-item":[144],"prediction":[145],"easier":[146],"expected:":[148],"low-branching":[149],"local":[150,176],"transitions,":[151,153],"feature-smooth":[152],"limited":[155],"dependence":[156],"long":[158],"user":[159],"histories.":[160],"These":[161],"shortcuts":[162],"need":[163],"not":[164,220],"appear":[165],"together;":[166],"even":[167],"strong":[171,214],"signals":[172],"retrieval":[177],"highly":[178],"competitive,":[179],"while":[180],"weakening":[181],"makes":[183],"benefits":[185],"more":[187,233],"sophisticated":[188],"models":[189],"clearer.":[190],"Across":[191],"14":[192],"datasets,":[193],"model":[194],"rankings":[195],"vary":[196],"substantially":[197],"dataset":[199,235],"properties,":[200],"yet":[201],"remains":[204],"competitive":[205],"10":[207],"them.":[209],"Our":[210],"findings":[211],"suggest":[212],"performance":[215],"standard":[217],"does":[219],"always":[221],"demonstrate":[222],"sequential,":[224],"semantic,":[225],"ability.":[229],"call":[231],"for":[232],"careful":[234],"selection":[236],"dataset-level":[238],"diagnostic":[239],"analysis":[240],"when":[241],"support":[245],"claims":[246],"about":[247],"new":[248],"models.":[250]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-12T00:00:00"}