{"id":"https://openalex.org/W2084677224","doi":"https://doi.org/10.1145/2766462.2767711","title":"GeoSoCa","display_name":"GeoSoCa","publication_year":2015,"publication_date":"2015-08-04","ids":{"openalex":"https://openalex.org/W2084677224","doi":"https://doi.org/10.1145/2766462.2767711","mag":"2084677224"},"language":"en","primary_location":{"id":"doi:10.1145/2766462.2767711","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2766462.2767711","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018287566","display_name":"Jia-Dong Zhang","orcid":"https://orcid.org/0000-0001-6378-5894"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Jia-Dong Zhang","raw_affiliation_strings":["City University of Hong Kong, Hong Kong, Hong Kong"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong, Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022507249","display_name":"Chi-Yin Chow","orcid":"https://orcid.org/0000-0002-9566-0743"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Chi-Yin Chow","raw_affiliation_strings":["City University of Hong Kong, Hong Kong, Hong Kong"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong, Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5018287566"],"corresponding_institution_ids":["https://openalex.org/I168719708"],"apc_list":null,"apc_paid":null,"fwci":82.2029,"has_fulltext":false,"cited_by_count":284,"citation_normalized_percentile":{"value":0.9993857,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"443","last_page":"452"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9977999925613403,"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/T10609","display_name":"Digital Marketing and Social Media","score":0.982200026512146,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.7884169816970825},{"id":"https://openalex.org/keywords/popularity","display_name":"Popularity","score":0.7722660899162292},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7464044690132141},{"id":"https://openalex.org/keywords/point-of-interest","display_name":"Point of interest","score":0.6678831577301025},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5814732313156128},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.5022497177124023},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.444665789604187},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.3715393841266632},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3350751996040344},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.27745291590690613},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2519461512565613},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11142641305923462},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09521982073783875}],"concepts":[{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.7884169816970825},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.7722660899162292},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7464044690132141},{"id":"https://openalex.org/C150140777","wikidata":"https://www.wikidata.org/wiki/Q960648","display_name":"Point of interest","level":2,"score":0.6678831577301025},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5814732313156128},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.5022497177124023},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.444665789604187},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.3715393841266632},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3350751996040344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27745291590690613},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2519461512565613},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11142641305923462},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09521982073783875},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2766462.2767711","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2766462.2767711","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.6700000166893005}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W182907944","https://openalex.org/W1546409232","https://openalex.org/W1981886741","https://openalex.org/W1984189333","https://openalex.org/W1986050033","https://openalex.org/W1995109099","https://openalex.org/W2003684386","https://openalex.org/W2009779426","https://openalex.org/W2009799282","https://openalex.org/W2017921654","https://openalex.org/W2024504711","https://openalex.org/W2034688657","https://openalex.org/W2035237009","https://openalex.org/W2045882407","https://openalex.org/W2051234849","https://openalex.org/W2054560962","https://openalex.org/W2058336014","https://openalex.org/W2070075246","https://openalex.org/W2072609015","https://openalex.org/W2073013176","https://openalex.org/W2074194940","https://openalex.org/W2082260230","https://openalex.org/W2086142729","https://openalex.org/W2087692915","https://openalex.org/W2109242993","https://openalex.org/W2112631146","https://openalex.org/W2122516730","https://openalex.org/W2129905273","https://openalex.org/W2139809240","https://openalex.org/W2149814409","https://openalex.org/W2189936406","https://openalex.org/W2323427203","https://openalex.org/W2405150027","https://openalex.org/W2405496393","https://openalex.org/W2408538552","https://openalex.org/W4233014035"],"related_works":["https://openalex.org/W2368605798","https://openalex.org/W2348524959","https://openalex.org/W2518037665","https://openalex.org/W2368049389","https://openalex.org/W2384861574","https://openalex.org/W2952704802","https://openalex.org/W4294565801","https://openalex.org/W2142306706","https://openalex.org/W308652608","https://openalex.org/W2151767655"],"abstract_inverted_index":{"Recommending":[0],"users":[1,36,87,105],"with":[2,138],"their":[3],"preferred":[4],"points-of-interest":[5],"(POIs),":[6],"e.g.,":[7],"museums":[8],"and":[9,28,83,88,93,108,176,218,253],"restaurants,":[10],"has":[11],"become":[12],"an":[13,44,119,139],"important":[14],"feature":[15],"for":[16,58,127,150,241],"location-based":[17],"social":[18,81,92,179,191],"networks":[19],"(LBSNs),":[20],"which":[21,53],"benefits":[22],"people":[23],"to":[24,30,110,118,124,142,188,207,227,266],"explore":[25],"new":[26,71],"places":[27],"businesses":[29],"discover":[31],"potential":[32],"customers.":[33],"However,":[34],"because":[35],"only":[37],"check":[38],"in":[39,43,65,130,214],"a":[40,55,70,116,134,144,170,174,185,201,204,212,224,237],"few":[41],"POIs":[42,107,149],"LBSN,":[45],"the":[46,100,112,156,164,178,190,198,209,215,220,229],"user-POI":[47],"check-in":[48,102,146,165,180,247],"interaction":[49],"is":[50],"highly":[51],"sparse,":[52],"renders":[54],"big":[56],"challenge":[57],"POI":[59,72,121,175,205,213,269],"recommendations.":[60],"To":[61],"tackle":[62],"this":[63,66],"challenge,":[64],"study":[67],"we":[68,132,235],"propose":[69,133],"recommendation":[73,263,270],"approach":[74],"called":[75],"GeoSoCa":[76,131,162,196,242,259],"through":[77],"exploiting":[78],"geographical":[79,157],"correlations,":[80],"correlations":[82,85,95,158,192,231],"categorical":[84,94,230],"among":[86],"POIs.":[89,160,233],"The":[90],"geographical,":[91],"can":[96],"be":[97],"learned":[98],"from":[99,251],"historical":[101],"data":[103,248],"of":[104,115,148,169,200,211],"on":[106,173,203],"utilized":[109],"predict":[111],"relevance":[113],"score":[114],"user":[117,152,202],"unvisited":[120],"so":[122],"as":[123,184,223],"make":[125],"recommendations":[126],"users.":[128,194],"First,":[129],"kernel":[135],"estimation":[136],"method":[137],"adaptive":[140],"bandwidth":[141],"determine":[143],"personalized":[145],"distribution":[147,187,226],"each":[151],"that":[153,258],"naturally":[154],"models":[155,177,219],"between":[159,193,232],"Then,":[161],"aggregates":[163],"frequency":[166,181],"or":[167,182],"rating":[168,183],"user's":[171],"friends":[172],"power-law":[186,225],"employ":[189],"Further,":[195],"applies":[197],"bias":[199],"category":[206,217],"weigh":[208],"popularity":[210,222],"corresponding":[216],"weighed":[221],"leverage":[228],"Finally,":[234],"conduct":[236],"comprehensive":[238],"performance":[239],"evaluation":[240],"using":[243],"two":[244],"large-scale":[245],"real-world":[246],"sets":[249],"collected":[250],"Foursquare":[252],"Yelp.":[254],"Experimental":[255],"results":[256],"show":[257],"achieves":[260],"significantly":[261],"superior":[262],"quality":[264],"compared":[265],"other":[267],"state-of-the-art":[268],"techniques.":[271]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":22},{"year":2021,"cited_by_count":38},{"year":2020,"cited_by_count":42},{"year":2019,"cited_by_count":46},{"year":2018,"cited_by_count":32},{"year":2017,"cited_by_count":35},{"year":2016,"cited_by_count":29},{"year":2015,"cited_by_count":4}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2016-06-24T00:00:00"}
