{"id":"https://openalex.org/W7155368426","doi":"https://doi.org/10.31449/inf.v50i11.10308","title":"Enhancing Library Recommendation Systems with Integrated PSO for Parameter Tuning in BERT-Derived Multimodal Contexts","display_name":"Enhancing Library Recommendation Systems with Integrated PSO for Parameter Tuning in BERT-Derived Multimodal Contexts","publication_year":2026,"publication_date":"2026-04-23","ids":{"openalex":"https://openalex.org/W7155368426","doi":"https://doi.org/10.31449/inf.v50i11.10308"},"language":null,"primary_location":{"id":"doi:10.31449/inf.v50i11.10308","is_oa":true,"landing_page_url":"https://doi.org/10.31449/inf.v50i11.10308","pdf_url":"https://www.informatica.si/index.php/informatica/article/download/10308/6646","source":{"id":"https://openalex.org/S4210173311","display_name":"Informatica","issn_l":"0350-5596","issn":["0350-5596","1854-3871"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310314525","host_organization_name":"Slovenian Society Informatika","host_organization_lineage":["https://openalex.org/P4310314525"],"host_organization_lineage_names":["Slovenian Society Informatika"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Informatica","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.informatica.si/index.php/informatica/article/download/10308/6646","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134443082","display_name":"Liu Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I40963666","display_name":"Central China Normal University","ror":"https://ror.org/03x1jna21","country_code":"CN","type":"education","lineage":["https://openalex.org/I40963666"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Liu Yan","raw_affiliation_strings":["Library of Central China Normal University, Wuhan, Hubei, 430079, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Library of Central China Normal University, Wuhan, Hubei, 430079, China","institution_ids":["https://openalex.org/I40963666"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5134443082"],"corresponding_institution_ids":["https://openalex.org/I40963666"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.52441503,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"50","issue":"11","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":0.6452999711036682,"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":0.6452999711036682,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.05460000038146973,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.017799999564886093,"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/recommender-system","display_name":"Recommender system","score":0.6025999784469604},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4318999946117401},{"id":"https://openalex.org/keywords/novelty","display_name":"Novelty","score":0.4106000065803528},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.4104999899864197},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.40380001068115234},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.38679999113082886},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3763999938964844},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.37070000171661377},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.3686000108718872}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7526999711990356},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.6025999784469604},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5145999789237976},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.451200008392334},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4318999946117401},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.4106000065803528},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.4104999899864197},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.40380001068115234},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.38679999113082886},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3763999938964844},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.37070000171661377},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3686000108718872},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3619000017642975},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.36149999499320984},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.36149999499320984},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3472000062465668},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.34209999442100525},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.328900009393692},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.3269999921321869},{"id":"https://openalex.org/C2780150774","wikidata":"https://www.wikidata.org/wiki/Q252500","display_name":"User profile","level":2,"score":0.32679998874664307},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.31869998574256897},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2971999943256378},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2969000041484833},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C67712803","wikidata":"https://www.wikidata.org/wiki/Q7901853","display_name":"User modeling","level":3,"score":0.2847000062465668},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.27480000257492065},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.26989999413490295}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.31449/inf.v50i11.10308","is_oa":true,"landing_page_url":"https://doi.org/10.31449/inf.v50i11.10308","pdf_url":"https://www.informatica.si/index.php/informatica/article/download/10308/6646","source":{"id":"https://openalex.org/S4210173311","display_name":"Informatica","issn_l":"0350-5596","issn":["0350-5596","1854-3871"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310314525","host_organization_name":"Slovenian Society Informatika","host_organization_lineage":["https://openalex.org/P4310314525"],"host_organization_lineage_names":["Slovenian Society Informatika"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Informatica","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.31449/inf.v50i11.10308","is_oa":true,"landing_page_url":"https://doi.org/10.31449/inf.v50i11.10308","pdf_url":"https://www.informatica.si/index.php/informatica/article/download/10308/6646","source":{"id":"https://openalex.org/S4210173311","display_name":"Informatica","issn_l":"0350-5596","issn":["0350-5596","1854-3871"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310314525","host_organization_name":"Slovenian Society Informatika","host_organization_lineage":["https://openalex.org/P4310314525"],"host_organization_lineage_names":["Slovenian Society Informatika"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Informatica","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.8824285268783569,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7155368426.pdf","grobid_xml":"https://content.openalex.org/works/W7155368426.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,85],"novel":[4],"multimodal":[5,40,74,149,167],"recommendation":[6,31,168,175],"system":[7,38,169],"that":[8],"integrates":[9],"Particle":[10],"Swarm":[11],"Optimization":[12],"(PSO)":[13],"with":[14,120,152],"Bidirectional":[15],"Encoder":[16],"Representations":[17],"from":[18,84],"Transformers":[19],"(BERT)":[20],"to":[21,138],"address":[22],"critical":[23,146],"challenges":[24],"in":[25,184],"library":[26,185],"reading":[27,186],"promotion,":[28],"including":[29,65],"low":[30],"accuracy":[32,99],"and":[33,47,70,109,122,127,134,180],"cold-start":[34,178],"problems.":[35],"The":[36,89,164],"proposed":[37],"leverages":[39],"data":[41,83],"encompassing":[42],"book":[43],"text,":[44],"cover":[45],"images,":[46],"user":[48,182],"behavior":[49],"patterns.":[50],"BERT":[51],"facilitates":[52],"deep":[53],"semantic":[54],"encoding":[55],"of":[56,132,148],"textual":[57],"information,":[58],"while":[59],"PSO":[60],"dynamically":[61],"optimizes":[62],"key":[63],"hyperparameters":[64],"learning":[66],"rate,":[67,72],"batch":[68],"size,":[69],"dropout":[71],"alongside":[73],"fusion":[75],"weights.":[76],"Experimental":[77],"validation":[78],"was":[79],"conducted":[80],"using":[81],"real-world":[82],"provincial":[86],"public":[87],"library.":[88],"PSO-BERT":[90,165],"model":[91,155],"demonstrated":[92],"superior":[93],"performance":[94],"across":[95],"all":[96],"evaluated":[97],"metrics:":[98],"(0.831,":[100],"+21.8%":[101],"vs.":[102],"collaborative":[103,140],"filtering),":[104],"recall":[105],"(0.805),":[106],"F1-score":[107],"(0.818),":[108],"hit":[110],"rate":[111],"(0.852).":[112],"User":[113],"satisfaction":[114,183],"surveys":[115],"further":[116],"confirmed":[117],"significant":[118],"improvements,":[119],"relevance":[121],"novelty":[123],"scores":[124],"reaching":[125],"8.6":[126],"7.9":[128],"points,":[129],"representing":[130],"increases":[131],"21.1%":[133],"33.9%,":[135],"respectively,":[136],"compared":[137],"traditional":[139],"filtering.":[141],"Ablation":[142],"studies":[143],"underscored":[144],"the":[145,153],"importance":[147],"feature":[150],"integration,":[151],"fused":[154],"maintaining":[156],"F1-scores":[157],"above":[158],"0.65,":[159],"substantially":[160],"outperforming":[161],"unimodal":[162],"configurations.":[163],"integrated":[166],"exhibits":[170],"substantial":[171],"potential":[172],"for":[173],"enhancing":[174],"accuracy,":[176],"mitigating":[177],"challenges,":[179],"improving":[181],"promotion":[187],"contexts.":[188]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-04-24T00:00:00"}
