{"id":"https://openalex.org/W4389314695","doi":"https://doi.org/10.1145/3627377.3627386","title":"Neighbor-Discovery Recurrent Model for Point-of-Interest Recommendation","display_name":"Neighbor-Discovery Recurrent Model for Point-of-Interest Recommendation","publication_year":2023,"publication_date":"2023-09-22","ids":{"openalex":"https://openalex.org/W4389314695","doi":"https://doi.org/10.1145/3627377.3627386"},"language":"en","primary_location":{"id":"doi:10.1145/3627377.3627386","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627377.3627386","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3627377.3627386","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 6th International Conference on Big Data Technologies","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3627377.3627386","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073920897","display_name":"Song-Yu Han","orcid":"https://orcid.org/0009-0004-5136-1559"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Songyu Han","raw_affiliation_strings":["Mellon College of Science, Carnegie Mellon University, USA"],"raw_orcid":"https://orcid.org/0009-0004-5136-1559","affiliations":[{"raw_affiliation_string":"Mellon College of Science, Carnegie Mellon University, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021634548","display_name":"Zhaobin Ma","orcid":"https://orcid.org/0009-0007-2527-3178"},"institutions":[{"id":"https://openalex.org/I108688024","display_name":"Qingdao University","ror":"https://ror.org/021cj6z65","country_code":"CN","type":"education","lineage":["https://openalex.org/I108688024"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaobin Ma","raw_affiliation_strings":["College of Computer Science & Technology, Qingdao University, China"],"raw_orcid":"https://orcid.org/0009-0007-2527-3178","affiliations":[{"raw_affiliation_string":"College of Computer Science & Technology, Qingdao University, China","institution_ids":["https://openalex.org/I108688024"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028381646","display_name":"Wei Zhao","orcid":"https://orcid.org/0009-0003-6974-7735"},"institutions":[{"id":"https://openalex.org/I108688024","display_name":"Qingdao University","ror":"https://ror.org/021cj6z65","country_code":"CN","type":"education","lineage":["https://openalex.org/I108688024"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Zhao","raw_affiliation_strings":["College of Computer Science & Technology, Qingdao University, China"],"raw_orcid":"https://orcid.org/0009-0003-6974-7735","affiliations":[{"raw_affiliation_string":"College of Computer Science & Technology, Qingdao University, China","institution_ids":["https://openalex.org/I108688024"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033243530","display_name":"Zhiqiang Lv","orcid":"https://orcid.org/0000-0002-3071-160X"},"institutions":[{"id":"https://openalex.org/I108688024","display_name":"Qingdao University","ror":"https://ror.org/021cj6z65","country_code":"CN","type":"education","lineage":["https://openalex.org/I108688024"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiqiang Lv","raw_affiliation_strings":["College of Computer Science & Technology, Qingdao University, China"],"raw_orcid":"https://orcid.org/0000-0002-3071-160X","affiliations":[{"raw_affiliation_string":"College of Computer Science & Technology, Qingdao University, China","institution_ids":["https://openalex.org/I108688024"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.26086342,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"52","last_page":"59"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9781000018119812,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/computer-science","display_name":"Computer science","score":0.8346561789512634},{"id":"https://openalex.org/keywords/point-of-interest","display_name":"Point of interest","score":0.6734769344329834},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5647882223129272},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.5613188147544861},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5175763368606567},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4299388527870178},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.42311370372772217},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4171152114868164},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.39626917243003845},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36710143089294434},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35410887002944946},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.20505952835083008}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8346561789512634},{"id":"https://openalex.org/C150140777","wikidata":"https://www.wikidata.org/wiki/Q960648","display_name":"Point of interest","level":2,"score":0.6734769344329834},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5647882223129272},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.5613188147544861},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5175763368606567},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4299388527870178},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.42311370372772217},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4171152114868164},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.39626917243003845},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36710143089294434},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35410887002944946},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.20505952835083008},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3627377.3627386","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627377.3627386","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3627377.3627386","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 6th International Conference on Big Data Technologies","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3627377.3627386","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627377.3627386","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3627377.3627386","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 6th International Conference on Big Data Technologies","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4389314695.pdf","grobid_xml":"https://content.openalex.org/works/W4389314695.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1546409232","https://openalex.org/W2017921654","https://openalex.org/W2054141820","https://openalex.org/W2071702404","https://openalex.org/W2087692915","https://openalex.org/W2171279286","https://openalex.org/W2329660289","https://openalex.org/W2519887557","https://openalex.org/W2539781657","https://openalex.org/W2612465529","https://openalex.org/W2767923185","https://openalex.org/W2788114581","https://openalex.org/W2901504064","https://openalex.org/W2907639449","https://openalex.org/W2911662370","https://openalex.org/W2998167534","https://openalex.org/W3010124513","https://openalex.org/W3040157551","https://openalex.org/W3086935078","https://openalex.org/W3098791569","https://openalex.org/W3182305291","https://openalex.org/W3198625276","https://openalex.org/W4226288807","https://openalex.org/W4252964922","https://openalex.org/W4376638685","https://openalex.org/W4384997512","https://openalex.org/W7134224534","https://openalex.org/W7139033639"],"related_works":["https://openalex.org/W2366107444","https://openalex.org/W4388145910","https://openalex.org/W1976205134","https://openalex.org/W4386781444","https://openalex.org/W2381570729","https://openalex.org/W4248336175","https://openalex.org/W3092950680","https://openalex.org/W2150182025","https://openalex.org/W4246980185","https://openalex.org/W2912355043"],"abstract_inverted_index":{"With":[0],"the":[1,41,67,82,86,97,101,109,122,129,135,138,154],"rapid":[2],"development":[3],"of":[4,28,44,85,99,103,112,137],"Location-Based":[5],"Social":[6],"Networks":[7],"(LBSNs),":[8],"Point-of-Interest":[9],"(POI)":[10],"recommendation":[11,17,70],"has":[12],"become":[13],"increasingly":[14],"important.":[15],"POI":[16,69,159,174],"can":[18,164],"help":[19],"users":[20,90],"to":[21,40,64,93,108,127],"find":[22,94],"interesting":[23],"information":[24,84],"from":[25],"massive":[26],"amounts":[27],"data.":[29],"Various":[30],"models":[31,156],"have":[32],"been":[33],"proposed":[34,139],"for":[35,96,172],"this":[36,46,52],"problem.":[37,71],"However,":[38],"due":[39],"check-in":[42,110],"data":[43,104],"sparseness,":[45],"problem":[47],"is":[48,91],"still":[49],"challenging.":[50],"In":[51,72,161],"paper,":[53],"we":[54,76,114,133],"propose":[55],"a":[56,78,116,167],"novel":[57],"model":[58,152],"named":[59],"Neighbor-Discovery":[60],"Recurrent":[61],"Model":[62],"(NDRM)":[63],"deal":[65],"with":[66],"next":[68,158,173],"our":[73,150],"model,":[74],"firstly":[75],"design":[77],"neighbor-discovery":[79],"mechanism,":[80],"where":[81],"category":[83],"location":[87],"visited":[88],"by":[89],"utilized":[92],"neighbors":[95],"purpose":[98],"reducing":[100],"impact":[102],"sparseness.":[105],"Moreover,":[106],"according":[107],"sequence":[111],"locations,":[113],"construct":[115],"graph":[117],"structure":[118],"and":[119,169],"then":[120],"use":[121],"Graph":[123],"Convolutional":[124],"Network":[125],"(GCN)":[126],"capture":[128],"spatial":[130],"feature.":[131],"Finally,":[132],"evaluate":[134],"performance":[136],"NDRM":[140,151],"on":[141],"three":[142],"real-world":[143],"datasets.":[144],"The":[145],"experimental":[146],"results":[147],"show":[148],"that":[149],"outperforms":[153],"state-of-the-art":[155],"in":[157],"recommendation.":[160,175],"addition,":[162],"it":[163],"also":[165],"provide":[166],"reasonable":[168],"intuitive":[170],"explanation":[171]},"counts_by_year":[],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}