{"id":"https://openalex.org/W7160620406","doi":"https://doi.org/10.48550/arxiv.2605.06441","title":"Light-FMP: Lightweight Feature and Model Pruning for Enhanced Deep Recommender Systems","display_name":"Light-FMP: Lightweight Feature and Model Pruning for Enhanced Deep Recommender Systems","publication_year":2026,"publication_date":"2026-05-07","ids":{"openalex":"https://openalex.org/W7160620406","doi":"https://doi.org/10.48550/arxiv.2605.06441"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.06441","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06441","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.2605.06441","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134995991","display_name":"Nghia Bui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bui, Nghia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135640160","display_name":"Yue Ning","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ning, Yue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135681637","display_name":"Lijing Wang","orcid":"https://orcid.org/0000-0002-7300-9271"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Lijing","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":0.3328000009059906,"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":0.3328000009059906,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.08839999884366989,"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"}},{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.08139999955892563,"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/pruning","display_name":"Pruning","score":0.7570000290870667},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7232000231742859},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.6466000080108643},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6265000104904175},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5964000225067139},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5867999792098999},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5659000277519226},{"id":"https://openalex.org/keywords/masking","display_name":"Masking (illustration)","score":0.42890000343322754}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8532999753952026},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7570000290870667},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7232000231742859},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.6466000080108643},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6265000104904175},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6116999983787537},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5964000225067139},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5867999792098999},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5748000144958496},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5659000277519226},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4909000098705292},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.42890000343322754},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.40400001406669617},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4004000127315521},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.388700008392334},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.3407000005245209},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.31060001254081726},{"id":"https://openalex.org/C2780757406","wikidata":"https://www.wikidata.org/wiki/Q465837","display_name":"Skyline","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2578999996185303}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.06441","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06441","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.2605.06441","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06441","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":{"Deep":[0],"recommender":[1,112],"systems":[2,113],"(DRS)":[3],"often":[4],"face":[5],"challenges":[6,59],"in":[7,120],"balancing":[8],"computational":[9],"efficiency":[10,30,33,122],"and":[11,47,66,91,96,123,128],"model":[12,90],"accuracy,":[13],"especially":[14],"when":[15],"handling":[16],"high-dimensional":[17],"input":[18],"features.":[19,88],"Existing":[20],"methods":[21,119],"either":[22],"focus":[23],"on":[24,80,99,107],"improving":[25],"accuracy":[26,39,124],"while":[27,125],"neglecting":[28],"training":[29,97],"or":[31],"prioritize":[32],"at":[34],"the":[35,58,100],"cost":[36],"of":[37],"suboptimal":[38],"across":[40],"tasks.":[41],"We":[42],"propose":[43],"Light-FMP:":[44],"Lightweight":[45],"Feature":[46],"Model":[48],"Pruning":[49],"for":[50],"Enhanced":[51],"DRS,":[52],"a":[53,70,74,81],"lightweight":[54],"framework":[55],"that":[56,115],"addresses":[57],"through":[60],"three":[61],"key":[62],"phases:":[63],"\\textit{pretraining},":[64],"\\textit{pruning},":[65],"\\textit{continued":[67],"training}.":[68],"Using":[69],"hard":[71],"concrete":[72],"distribution,":[73],"masking":[75],"layer":[76],"is":[77],"efficiently":[78],"pretrained":[79],"small":[82],"data":[83],"subset":[84],"to":[85],"identify":[86],"important":[87],"The":[89],"features":[92],"are":[93],"then":[94],"pruned,":[95],"continues":[98],"remaining":[101],"dataset":[102],"with":[103],"domain-adapted":[104],"parameters.":[105],"Experiments":[106],"benchmark":[108],"datasets":[109],"from":[110],"real-world":[111],"demonstrate":[114],"Light-FMP":[116],"outperforms":[117],"existing":[118],"both":[121],"maintaining":[126],"scalability":[127],"robustness.":[129]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-09T00:00:00"}
