{"id":"https://openalex.org/W4417283641","doi":"https://doi.org/10.1145/3748636.3762708","title":"M3: Recommendation via Attention-Graph Cluster Q-Learning with Multi-Scale Spatial Heterogeneity for Multi-Purpose, Multi-Stakeholder Green Attractions in Transportation","display_name":"M3: Recommendation via Attention-Graph Cluster Q-Learning with Multi-Scale Spatial Heterogeneity for Multi-Purpose, Multi-Stakeholder Green Attractions in Transportation","publication_year":2025,"publication_date":"2025-11-03","ids":{"openalex":"https://openalex.org/W4417283641","doi":"https://doi.org/10.1145/3748636.3762708"},"language":null,"primary_location":{"id":"doi:10.1145/3748636.3762708","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748636.3762708","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3748636.3762708","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3748636.3762708","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103006974","display_name":"Shih-Yu Lai","orcid":"https://orcid.org/0009-0001-3769-3432"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Shih-Yu Lai","raw_affiliation_strings":["National Taiwan University, Taiwan, Taiwan"],"affiliations":[{"raw_affiliation_string":"National Taiwan University, Taiwan, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104208843","display_name":"Tzu-Hsin Hsieh","orcid":"https://orcid.org/0009-0007-2031-7928"},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Tzu-Hsin Hsieh","raw_affiliation_strings":["Technische Universiteit Delft, Delft, Netherlands"],"affiliations":[{"raw_affiliation_string":"Technische Universiteit Delft, Delft, Netherlands","institution_ids":["https://openalex.org/I98358874"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019094783","display_name":"Pei\u2010Jane Tsai","orcid":"https://orcid.org/0000-0001-9938-3299"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Pei-Chi Tsai","raw_affiliation_strings":["University College London, London, United Kingdom"],"affiliations":[{"raw_affiliation_string":"University College London, London, United Kingdom","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114006003","display_name":"C. Y. Kung","orcid":"https://orcid.org/0009-0004-2734-4740"},"institutions":[{"id":"https://openalex.org/I123152040","display_name":"Architectural Association School of Architecture","ror":"https://ror.org/03b8f3736","country_code":"GB","type":"education","lineage":["https://openalex.org/I123152040"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Chao-Chun Kung","raw_affiliation_strings":["OAOA Architecture Associates, London, United Kingdom"],"affiliations":[{"raw_affiliation_string":"OAOA Architecture Associates, London, United Kingdom","institution_ids":["https://openalex.org/I123152040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111490959","display_name":"S.M. Ling","orcid":"https://orcid.org/0009-0009-9521-5445"},"institutions":[{"id":"https://openalex.org/I4210143126","display_name":"Acer (Taiwan)","ror":"https://ror.org/03xajsx66","country_code":"TW","type":"company","lineage":["https://openalex.org/I4210143126"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Sing-Kai Ling","raw_affiliation_strings":["104 Corporation, Taiwan, Taiwan"],"affiliations":[{"raw_affiliation_string":"104 Corporation, Taiwan, Taiwan","institution_ids":["https://openalex.org/I4210143126"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007626411","display_name":"Hsun-Ping Hsieh","orcid":"https://orcid.org/0000-0001-6924-1337"},"institutions":[{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Hsun-Ping Hsieh","raw_affiliation_strings":["National Cheng Kung University, Taiwan, Taiwan"],"affiliations":[{"raw_affiliation_string":"National Cheng Kung University, Taiwan, Taiwan","institution_ids":["https://openalex.org/I91807558"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5103006974"],"corresponding_institution_ids":["https://openalex.org/I16733864"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.43610329,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"39","last_page":"51"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.43709999322891235,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.43709999322891235,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.10559999942779541,"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/T11942","display_name":"Transportation and Mobility Innovations","score":0.07370000332593918,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7042999863624573},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5302000045776367},{"id":"https://openalex.org/keywords/public-transport","display_name":"Public transport","score":0.5098999738693237},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.44269999861717224},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.41999998688697815},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.4034000039100647},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.35249999165534973}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7042999863624573},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6413999795913696},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5302000045776367},{"id":"https://openalex.org/C539828613","wikidata":"https://www.wikidata.org/wiki/Q178512","display_name":"Public transport","level":2,"score":0.5098999738693237},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4503999948501587},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.44269999861717224},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.41999998688697815},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.4034000039100647},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3977999985218048},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.35249999165534973},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.3257000148296356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3192000091075897},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.30970001220703125},{"id":"https://openalex.org/C180478619","wikidata":"https://www.wikidata.org/wiki/Q7574066","display_name":"Spatial heterogeneity","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C157181609","wikidata":"https://www.wikidata.org/wiki/Q1364310","display_name":"Sustainable transport","level":3,"score":0.25999999046325684},{"id":"https://openalex.org/C22047676","wikidata":"https://www.wikidata.org/wiki/Q898680","display_name":"Clustering coefficient","level":3,"score":0.2587999999523163},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.25870001316070557},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.2554999887943268},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.2533000111579895},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3748636.3762708","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748636.3762708","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3748636.3762708","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3748636.3762708","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748636.3762708","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3748636.3762708","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320331164","display_name":"National Science and Technology Council","ror":"https://ror.org/00wnb9798"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4417283641.pdf"},"referenced_works_count":14,"referenced_works":["https://openalex.org/W1987971958","https://openalex.org/W2051224630","https://openalex.org/W2070232376","https://openalex.org/W2465748142","https://openalex.org/W2945827377","https://openalex.org/W2964395550","https://openalex.org/W3083455343","https://openalex.org/W3084230959","https://openalex.org/W3136370372","https://openalex.org/W4235169531","https://openalex.org/W4300672471","https://openalex.org/W4392243958","https://openalex.org/W4392508629","https://openalex.org/W4395038432"],"related_works":[],"abstract_inverted_index":{"With":[0],"growing":[1],"environmental":[2],"concerns":[3],"and":[4,43,55,78,109,123,131,139,154,158,169,193],"the":[5,23,80,105,162,166,182],"push":[6],"for":[7,57,111],"sustainable":[8],"urban":[9,95],"development,":[10],"promoting":[11],"green":[12,27,101,196],"travel":[13,38,96],"has":[14],"become":[15],"a":[16,173],"critical":[17],"initiative.":[18],"Urban":[19],"transit":[20],"systems":[21],"face":[22],"challenge":[24],"of":[25,82,107,165,184],"integrating":[26],"initiatives":[28],"with":[29,85,97,195],"efficient":[30],"transport":[31,190],"routes,":[32],"while":[33],"sophisticated":[34],"graph":[35,145,185],"modeling":[36,122],"enhances":[37],"efficiency.":[39],"However,":[40],"blending":[41],"historical":[42],"contemporary":[44],"elements":[45],"introduces":[46],"complex":[47],"variations":[48],"in":[49,144,187],"traffic":[50],"networks,":[51],"complicating":[52],"feature":[53],"extraction":[54],"clustering":[56,132,186],"information":[58,99],"retrieval":[59],"due":[60],"to":[61],"multi-scale":[62,170],"spatial":[63,204],"heterogeneity.":[64,205],"Traditional":[65],"methods":[66],"often":[67],"overlook":[68],"key":[69],"nuances":[70],"by":[71,199],"oversimplifying":[72],"data":[73],"relationships.":[74],"We":[75],"proposed":[76],"M3":[77],"validated":[79],"integration":[81],"GIS-based":[83],"Attention-Cluster-GCN":[84],"Dueling":[86],"Double":[87],"Deep":[88],"Q":[89],"Network":[90],"across":[91],"various":[92],"cities,":[93],"enhancing":[94,127],"detailed":[98],"on":[100],"attraction":[102],"recommendations,":[103,200],"considering":[104],"usage":[106],"Multi-Purpose":[108],"Multi-Stakeholder":[110],"Multi-Scale":[112],"Spatial":[113],"Heterogeneity":[114],"scenarios.":[115],"Utilizing":[116],"Attention-Based":[117],"Reinforcement":[118],"Graph":[119],"Clustering":[120],"refines":[121],"emphasizes":[124],"vital":[125],"connections,":[126],"personalized":[128],"recommendation":[129],"precision":[130],"performance.":[133],"Our":[134,156,179],"method":[135],"surpasses":[136],"both":[137],"conventional":[138],"advanced":[140],"GNN":[141],"methods,":[142],"even":[143,201],"convolution-based":[146],"deep":[147],"reinforcement":[148],"learning,":[149],"achieving":[150],"superior":[151],"cluster":[152],"separation":[153],"accuracy.":[155],"sampling":[157],"ablation":[159],"studies":[160],"confirm":[161],"pivotal":[163],"role":[164],"attention":[167],"mechanism":[168],"features,":[171],"showing":[172],"significant":[174,203],"performance":[175],"decline":[176],"without":[177],"attention.":[178],"findings":[180],"underscore":[181],"potential":[183],"making":[188],"public":[189],"more":[191],"engaging":[192],"aligned":[194],"attractions":[197],"policies":[198],"amidst":[202]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-12-12T00:00:00"}
