{"id":"https://openalex.org/W4413155210","doi":"https://doi.org/10.1109/access.2025.3598092","title":"Reinforcement Learning-Based Recommender Systems Enhanced With Graph Neural Networks","display_name":"Reinforcement Learning-Based Recommender Systems Enhanced With Graph Neural Networks","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4413155210","doi":"https://doi.org/10.1109/access.2025.3598092"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3598092","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3598092","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3598092","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5056927707","display_name":"Chen Fan","orcid":"https://orcid.org/0000-0003-2588-2516"},"institutions":[{"id":"https://openalex.org/I113306721","display_name":"Hiroshima University","ror":"https://ror.org/03t78wx29","country_code":"JP","type":"education","lineage":["https://openalex.org/I113306721"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Chenxi Fan","raw_affiliation_strings":["Department of Information Science, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan","Department of Information Science, Graduate School of Advanced Science and Engineering, Hiroshima University, Kagamiyama 1-4-1, Higashi-Hiroshima, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Information Science, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan","institution_ids":["https://openalex.org/I113306721"]},{"raw_affiliation_string":"Department of Information Science, Graduate School of Advanced Science and Engineering, Hiroshima University, Kagamiyama 1-4-1, Higashi-Hiroshima, Japan","institution_ids":["https://openalex.org/I113306721"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087923932","display_name":"Satoshi Fujita","orcid":"https://orcid.org/0000-0001-9412-7309"},"institutions":[{"id":"https://openalex.org/I113306721","display_name":"Hiroshima University","ror":"https://ror.org/03t78wx29","country_code":"JP","type":"education","lineage":["https://openalex.org/I113306721"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Satoshi Fujita","raw_affiliation_strings":["Department of Information Science, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan","Department of Information Science, Graduate School of Advanced Science and Engineering, Hiroshima University, Kagamiyama 1-4-1, Higashi-Hiroshima, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Information Science, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan","institution_ids":["https://openalex.org/I113306721"]},{"raw_affiliation_string":"Department of Information Science, Graduate School of Advanced Science and Engineering, Hiroshima University, Kagamiyama 1-4-1, Higashi-Hiroshima, Japan","institution_ids":["https://openalex.org/I113306721"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5056927707"],"corresponding_institution_ids":["https://openalex.org/I113306721"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":3.6916,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.94089447,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"13","issue":null,"first_page":"150228","last_page":"150243"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9325000047683716,"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":0.9325000047683716,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8213923573493958},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8126265406608582},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.7610622644424438},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4686298370361328},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.45603904128074646},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38990992307662964},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.20771920680999756}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8213923573493958},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8126265406608582},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.7610622644424438},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4686298370361328},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.45603904128074646},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38990992307662964},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.20771920680999756}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3598092","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3598092","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:bda2dfb894034f19b28fa9c7710faf57","is_oa":true,"landing_page_url":"https://doaj.org/article/bda2dfb894034f19b28fa9c7710faf57","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 150228-150243 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3598092","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3598092","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2024320089","https://openalex.org/W2042281163","https://openalex.org/W2054141820","https://openalex.org/W2155106456","https://openalex.org/W2194775991","https://openalex.org/W2283196293","https://openalex.org/W2605350416","https://openalex.org/W2746553466","https://openalex.org/W2799544270","https://openalex.org/W2807021761","https://openalex.org/W2902572901","https://openalex.org/W2952317511","https://openalex.org/W2964015378","https://openalex.org/W3034853385","https://openalex.org/W4252707176","https://openalex.org/W4399074796","https://openalex.org/W4400526470","https://openalex.org/W4401235221","https://openalex.org/W4403452035","https://openalex.org/W4406626884","https://openalex.org/W4406866326"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Graph":[0],"Neural":[1],"Networks":[2],"(GNNs)":[3],"have":[4],"emerged":[5],"as":[6,41,219],"powerful":[7],"tools":[8],"in":[9,64,273],"recommender":[10,82],"systems,":[11],"enabling":[12,189],"the":[13,92,99,150,155,159,190,266],"modeling":[14,94],"of":[15,96,103,270],"complex":[16],"user-item":[17],"interactions":[18],"by":[19,235,241,247],"leveraging":[20],"graph-structured":[21],"representations.":[22],"However,":[23],"conventional":[24],"GNN-based":[25],"recommendation":[26,275],"models":[27],"often":[28],"struggle":[29],"to":[30,32,55,112,149,153,192],"adapt":[31],"dynamically":[33],"evolving":[34],"user":[35,67,114,119,138,160,179],"preferences":[36,115,139],"and":[37,58,69,110,136,166,196,207,216,237,243,245,249,253,260,268],"newly":[38],"introduced":[39],"items,":[40],"they":[42],"predominantly":[43],"rely":[44],"on":[45,202,251],"static":[46],"supervised":[47],"learning":[48,109],"frameworks.":[49],"These":[50,263],"limitations":[51],"hinder":[52],"their":[53],"ability":[54],"provide":[56],"accurate":[57],"personalized":[59],"recommendations":[60],"over":[61],"time,":[62],"particularly":[63],"scenarios":[65],"where":[66],"behavior":[68],"item":[70],"availability":[71],"change":[72],"frequently.":[73],"To":[74],"address":[75],"this":[76],"challenge,":[77],"we":[78,122,171],"propose":[79],"a":[80,128,144,173],"novel":[81],"system":[83],"that":[84,177,226],"integrates":[85],"GNNs":[86,97],"with":[87,98,158,186],"Reinforcement":[88],"Learning":[89],"(RL),":[90],"combining":[91],"structural":[93],"capabilities":[95],"sequential":[100],"decision-making":[101],"strengths":[102],"RL.":[104],"Our":[105],"approach":[106,272],"enables":[107],"continuous":[108],"adaptation":[111],"shifting":[113],"while":[116,256],"optimizing":[117],"long-term":[118,135],"engagement.":[120],"Specifically,":[121],"introduce":[123],"three":[124],"key":[125],"components.":[126],"First,":[127],"self-attention-based":[129],"state":[130],"encoder":[131],"adaptively":[132],"captures":[133],"both":[134],"short-term":[137],"from":[140],"historical":[141],"interactions.":[142],"Second,":[143],"residual":[145],"connection":[146],"is":[147],"added":[148],"actor":[151],"network":[152],"align":[154],"action":[156],"space":[157],"embedding":[161],"space,":[162],"thereby":[163],"stabilizing":[164],"training":[165],"improving":[167],"representation":[168],"consistency.":[169],"Third,":[170],"design":[172],"hybrid":[174],"reward":[175],"function":[176],"combines":[178],"feedback":[180],"types":[181],"(e.g.,":[182],"clicks,":[183],"cart,":[184],"purchase)":[185],"embedding-based":[187],"similarity,":[188],"model":[191],"balance":[193],"accuracy,":[194],"diversity,":[195],"adaptability.":[197],"We":[198],"evaluate":[199],"our":[200,227,271],"method":[201,228],"two":[203],"real-world":[204],"datasets:":[205],"Taobao":[206,252],"Amazon-CD,":[208,254],"using":[209],"Recall,":[210],"Hit":[211,239],"Ratio,":[212],"NDCG,":[213],"Item":[214],"Coverage,":[215],"Entropy-based":[217],"Diversity":[218],"core":[220],"evaluation":[221],"metrics.":[222],"Experimental":[223],"results":[224,264],"show":[225],"consistently":[229],"outperforms":[230],"baselines:":[231],"it":[232],"improves":[233],"Recall":[234],"21.4%":[236],"50.4%,":[238],"Ratio":[240],"8.3%":[242],"33.9%,":[244],"NDCG":[246],"11.0%":[248],"22.2%":[250],"respectively,":[255],"maintaining":[257],"competitive":[258],"Coverage":[259],"Entropy":[261],"scores.":[262],"demonstrate":[265],"effectiveness":[267],"robustness":[269],"dynamic":[274],"scenarios.":[276]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-19T19:40:27.379048","created_date":"2025-10-10T00:00:00"}