{"id":"https://openalex.org/W2825858716","doi":"https://doi.org/10.1145/3234944.3234945","title":"A Collaborative Ranking Model with Multiple Location-based Similarities for Venue Suggestion","display_name":"A Collaborative Ranking Model with Multiple Location-based Similarities for Venue Suggestion","publication_year":2018,"publication_date":"2018-09-10","ids":{"openalex":"https://openalex.org/W2825858716","doi":"https://doi.org/10.1145/3234944.3234945","mag":"2825858716"},"language":"en","primary_location":{"id":"doi:10.1145/3234944.3234945","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3234944.3234945","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1807.04210","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Mohammad Aliannejadi","orcid":null},"institutions":[{"id":"https://openalex.org/I57201433","display_name":"Universit\u00e0 della Svizzera italiana","ror":"https://ror.org/03c4atk17","country_code":"CH","type":"education","lineage":["https://openalex.org/I57201433"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Mohammad Aliannejadi","raw_affiliation_strings":["Universit\u00e0 della Svizzera italiana (USI), Lugano, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e0 della Svizzera italiana (USI), Lugano, Switzerland","institution_ids":["https://openalex.org/I57201433"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Dimitrios Rafailidis","orcid":null},"institutions":[{"id":"https://openalex.org/I130929987","display_name":"University of Mons","ror":"https://ror.org/02qnnz951","country_code":"BE","type":"education","lineage":["https://openalex.org/I130929987"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Dimitrios Rafailidis","raw_affiliation_strings":["University of Mons, Mons, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Mons, Mons, Belgium","institution_ids":["https://openalex.org/I130929987"]}]},{"author_position":"last","author":{"id":null,"display_name":"Fabio Crestani","orcid":null},"institutions":[{"id":"https://openalex.org/I57201433","display_name":"Universit\u00e0 della Svizzera italiana","ror":"https://ror.org/03c4atk17","country_code":"CH","type":"education","lineage":["https://openalex.org/I57201433"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Fabio Crestani","raw_affiliation_strings":["Universit\u00e0 della Svizzera italiana (USI), Lugano, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e0 della Svizzera italiana (USI), Lugano, Switzerland","institution_ids":["https://openalex.org/I57201433"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.7018,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.94268136,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"19","last_page":"26"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T13274","display_name":"Expert finding and Q&A systems","score":0.9869999885559082,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9556999802589417,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/ranking","display_name":"Ranking (information retrieval)","score":0.7763000130653381},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.7609000205993652},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.7329999804496765},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6421999931335449},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.6200000047683716},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.4521999955177307}],"concepts":[{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.7763000130653381},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.7609000205993652},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.7329999804496765},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7042999863624573},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6421999931335449},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.6200000047683716},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.54339998960495},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.4521999955177307},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.3939000070095062},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.36970001459121704},{"id":"https://openalex.org/C143271835","wikidata":"https://www.wikidata.org/wiki/Q254515","display_name":"Similitude","level":2,"score":0.3402999937534332},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3158999979496002},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2924000024795532},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26969999074935913}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3234944.3234945","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3234944.3234945","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1807.04210","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1807.04210","pdf_url":"https://arxiv.org/pdf/1807.04210","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1807.04210","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1807.04210","pdf_url":"https://arxiv.org/pdf/1807.04210","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1966553486","https://openalex.org/W1972436494","https://openalex.org/W1981886741","https://openalex.org/W1992435965","https://openalex.org/W1994389483","https://openalex.org/W2016666840","https://openalex.org/W2017921654","https://openalex.org/W2034688657","https://openalex.org/W2035949918","https://openalex.org/W2042281163","https://openalex.org/W2044672016","https://openalex.org/W2084677224","https://openalex.org/W2085040216","https://openalex.org/W2087692915","https://openalex.org/W2102937240","https://openalex.org/W2109242993","https://openalex.org/W2124187902","https://openalex.org/W2149427297","https://openalex.org/W2150921150","https://openalex.org/W2171960770","https://openalex.org/W2296244148","https://openalex.org/W2536436157","https://openalex.org/W2604598531","https://openalex.org/W2620173427","https://openalex.org/W2730085903","https://openalex.org/W2739957794","https://openalex.org/W2741898449","https://openalex.org/W2758089162","https://openalex.org/W2762735242"],"related_works":[],"abstract_inverted_index":{"Recommending":[0],"venues":[1,56,121,156],"plays":[2],"a":[3,100],"critical":[4],"rule":[5],"in":[6,57,64,93],"satisfying":[7],"users'":[8],"needs":[9,89],"on":[10,51,105,136,170],"location-based":[11],"social":[12],"networks.":[13],"Recent":[14],"studies":[15],"have":[16],"explored":[17,92],"the":[18,28,41,55,78,82,85,106,109,125,146,149,171],"idea":[19,29],"of":[20,30,54,108,117],"adopting":[21],"collaborative":[22,35,65],"ranking":[23],"(CR)":[24],"for":[25],"recommendation,":[26],"combining":[27],"learning":[31],"to":[32,80,90],"rank":[33],"and":[34,139],"filtering.":[36],"However,":[37],"CR":[38,101,180],"suffers":[39],"from":[40],"sparsity":[42,83],"problem,":[43,84],"mainly":[44],"because":[45],"it":[46,123],"associates":[47],"similar":[48,155],"users":[49,153,164],"based":[50,135],"exact":[52],"matching":[53],"their":[58],"check-in":[59],"history.":[60],"Even":[61],"though":[62],"research":[63],"filtering":[66],"has":[67],"shown":[68],"that":[69,103,177],"considering":[70],"auxiliary":[71],"information":[72],"such":[73],"as":[74,122,154],"geographical":[75],"influence,":[76],"helps":[77],"model":[79,147,181],"alleviate":[81],"same":[86],"direction":[87],"still":[88],"be":[91],"CR.":[94],"In":[95],"this":[96],"work,":[97],"we":[98],"present":[99],"framework":[102],"focuses":[104],"top":[107],"ranked":[110],"list":[111],"while":[112,162],"integrating":[113],"an":[114],"arbitrary":[115],"number":[116],"similarity":[118,133,143],"functions":[119],"between":[120,152],"learns":[124],"model's":[126],"parameters.":[127],"We":[128],"further":[129],"introduce":[130],"three":[131],"example":[132],"measures":[134,144],"venues'":[137],"contents":[138],"locations.":[140],"Incorporating":[141],"cross-venue":[142],"into":[145,160],"enhances":[148],"latent":[150],"associations":[151],"are":[157],"also":[158],"taken":[159],"account":[161],"associating":[163],"with":[165],"each":[166],"other.":[167],"Our":[168],"experiments":[169],"TREC":[172],"Contextual":[173],"Suggestion":[174],"dataset":[175],"show":[176],"our":[178],"proposed":[179],"beats":[182],"other":[183],"state-of-the-art":[184],"venue":[185],"suggestion":[186],"methods.":[187]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2018-07-19T00:00:00"}