{"id":"https://openalex.org/W7163148268","doi":"https://doi.org/10.48550/arxiv.2606.01111","title":"LeAP: Learnable Adaptive Permutation for Feature Selection in Heterogeneous and Sparse Recommender Systems","display_name":"LeAP: Learnable Adaptive Permutation for Feature Selection in Heterogeneous and Sparse Recommender Systems","publication_year":2026,"publication_date":"2026-05-31","ids":{"openalex":"https://openalex.org/W7163148268","doi":"https://doi.org/10.48550/arxiv.2606.01111"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.01111","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01111","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.2606.01111","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137621561","display_name":"Yihong Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Yihong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137634239","display_name":"Chen Chu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chu, Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137643300","display_name":"Fei Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Fei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137702122","display_name":"Yu Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137677172","display_name":"Ruiduan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Ruiduan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137711328","display_name":"Zhihao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhihao","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":0.9200999736785889,"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":0.9200999736785889,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.009999999776482582,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.004900000058114529,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/feature","display_name":"Feature (linguistics)","score":0.691100001335144},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6600000262260437},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5511000156402588},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5493999719619751},{"id":"https://openalex.org/keywords/permutation","display_name":"Permutation (music)","score":0.5462999939918518},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.4593000113964081},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3995000123977661},{"id":"https://openalex.org/keywords/random-permutation","display_name":"Random permutation","score":0.3846000134944916}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.739300012588501},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.691100001335144},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6600000262260437},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5511000156402588},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5493999719619751},{"id":"https://openalex.org/C21308566","wikidata":"https://www.wikidata.org/wiki/Q7169365","display_name":"Permutation (music)","level":2,"score":0.5462999939918518},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5196999907493591},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.4593000113964081},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4519999921321869},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4000000059604645},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3995000123977661},{"id":"https://openalex.org/C200985842","wikidata":"https://www.wikidata.org/wiki/Q3375503","display_name":"Random permutation","level":3,"score":0.3846000134944916},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.35350000858306885},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.34040001034736633},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3343000113964081},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.3158000111579895},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C101814296","wikidata":"https://www.wikidata.org/wiki/Q5439685","display_name":"Feature model","level":3,"score":0.2879999876022339},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.01111","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01111","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.2606.01111","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01111","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":[{"id":"https://metadata.un.org/sdg/9","score":0.43317461013793945,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Modern":[0],"industrial":[1,175],"recommender":[2],"systems":[3],"rely":[4],"on":[5,156],"thousands":[6],"of":[7,75,127,220],"heterogeneous":[8,140],"features":[9,76],"--":[10,26],"ranging":[11],"from":[12],"low-dimensional":[13],"scalars":[14],"(e.g.,":[15,21,77],"statistical":[16],"value)":[17],"to":[18,27,62,215],"high-dimensional":[19],"embeddings":[20],"user-id":[22],"embeddings,":[23],"MLP":[24],"representations)":[25],"achieve":[28],"high-precision":[29],"predictions.":[30],"Given":[31],"the":[32,73,114,125,218],"immense":[33],"computational":[34],"costs":[35],"associated":[36],"with":[37,69,179],"training,":[38],"efficient":[39],"feature":[40,56,110,128,147,198],"selection":[41],"is":[42,213],"critical.":[43],"However,":[44],"existing":[45],"methods":[46],"encounter":[47],"three":[48],"primary":[49],"bottlenecks:":[50],"(1)":[51],"they":[52,67],"typically":[53],"assume":[54],"uniform":[55],"dimensions":[57,141,208],"or":[58],"require":[59],"costly":[60],"mapping":[61],"a":[63,104,120,173,181,186],"fixed":[64],"size;":[65],"(2)":[66],"struggle":[68],"extreme":[70,143],"sparsity,":[71,144],"where":[72],"majority":[74],"99%+)":[78],"remain":[79],"at":[80],"default":[81],"values;":[82],"and":[83,142,185,203],"(3)":[84],"traditional":[85],"permutation-based":[86],"approaches":[87],"are":[88],"computationally":[89],"prohibitive":[90],"in":[91,172],"large-scale":[92,174],"settings.":[93],"To":[94],"address":[95],"these":[96],"challenges,":[97],"we":[98,132],"propose":[99],"LeAP":[100,112,163,168,200],"(Learnable":[101],"Adaptive":[102],"Permutation),":[103],"novel,":[105],"model-agnostic":[106],"plug-in":[107],"module":[108],"for":[109,139],"selection.":[111],"transforms":[113],"inefficient":[115],"random":[116],"permutation":[117],"process":[118],"into":[119],"learnable":[121],"mechanism,":[122],"significantly":[123],"accelerating":[124],"evaluation":[126],"importance.":[129],"In":[130,191],"addition,":[131],"introduce":[133],"an":[134],"adaptive":[135],"regularization":[136],"strategy":[137],"tailored":[138],"enabling":[145],"superior":[146],"importance":[148],"ranking":[149,177],"results":[150],"across":[151],"asymmetric":[152],"input":[153],"spaces.":[154],"Experiments":[155],"four":[157],"public":[158],"recommendation":[159],"datasets":[160],"demonstrate":[161],"that":[162],"achieves":[164],"state-of-the-art":[165],"performance.":[166],"Furthermore,":[167],"has":[169],"been":[170],"deployed":[171],"search":[176],"model":[178,188],"over":[180,205],"billion":[182],"daily":[183],"requests":[184],"2TB":[187],"parameter":[189],"scale.":[190],"this":[192],"real-world":[193],"scenario":[194],"involving":[195],"12,000+":[196],"total":[197],"dimensions,":[199],"successfully":[201],"identified":[202],"removed":[204],"3,600":[206],"redundant":[207],"without":[209],"performance":[210],"degradation,":[211],"which":[212],"2":[214],"10":[216],"times":[217],"ability":[219],"compared":[221],"baseline":[222],"methods.":[223]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-03T00:00:00"}
