{"id":"https://openalex.org/W4412876856","doi":"https://doi.org/10.1145/3711896.3737129","title":"SILO: Semantic Integration for Location Prediction with Large Language Models","display_name":"SILO: Semantic Integration for Location Prediction with Large Language Models","publication_year":2025,"publication_date":"2025-08-03","ids":{"openalex":"https://openalex.org/W4412876856","doi":"https://doi.org/10.1145/3711896.3737129"},"language":"en","primary_location":{"id":"doi:10.1145/3711896.3737129","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737129","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737129","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","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/10.1145/3711896.3737129","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050149042","display_name":"Tianao Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianao Sun","raw_affiliation_strings":["School of Software, Shandong University, Jinan, China"],"raw_orcid":"https://orcid.org/0009-0007-7816-2260","affiliations":[{"raw_affiliation_string":"School of Software, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100357859","display_name":"Meng Chen","orcid":"https://orcid.org/0000-0002-6633-9205"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meng Chen","raw_affiliation_strings":["School of Software, Shandong University, Jinan, China"],"raw_orcid":"https://orcid.org/0000-0002-6633-9205","affiliations":[{"raw_affiliation_string":"School of Software, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100385138","display_name":"Bowen Zhang","orcid":"https://orcid.org/0000-0002-3581-9476"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bowen Zhang","raw_affiliation_strings":["College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-3581-9476","affiliations":[{"raw_affiliation_string":"College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China","institution_ids":["https://openalex.org/I4210152380"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018994926","display_name":"Genan Dai","orcid":"https://orcid.org/0000-0003-2583-0433"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Genan Dai","raw_affiliation_strings":["College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-2583-0433","affiliations":[{"raw_affiliation_string":"College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China","institution_ids":["https://openalex.org/I4210152380"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057631007","display_name":"Weiming Huang","orcid":"https://orcid.org/0000-0002-3208-4208"},"institutions":[{"id":"https://openalex.org/I130828816","display_name":"University of Leeds","ror":"https://ror.org/024mrxd33","country_code":"GB","type":"education","lineage":["https://openalex.org/I130828816"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Weiming Huang","raw_affiliation_strings":["School of Geography, University of Leeds, Leeds, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0002-3208-4208","affiliations":[{"raw_affiliation_string":"School of Geography, University of Leeds, Leeds, United Kingdom","institution_ids":["https://openalex.org/I130828816"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101481095","display_name":"Kai Zhao","orcid":"https://orcid.org/0000-0003-1040-0211"},"institutions":[{"id":"https://openalex.org/I1330693074","display_name":"Walmart (United States)","ror":"https://ror.org/04j0gge90","country_code":"US","type":"company","lineage":["https://openalex.org/I1330693074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kai Zhao","raw_affiliation_strings":["AI Lab, Walmart AI, Sunnyvale, USA"],"raw_orcid":"https://orcid.org/0000-0003-1040-0211","affiliations":[{"raw_affiliation_string":"AI Lab, Walmart AI, Sunnyvale, USA","institution_ids":["https://openalex.org/I1330693074"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.064,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.77951801,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2756","last_page":"2767"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9988999962806702,"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"}},"topics":[{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9988999962806702,"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"}},{"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9943000078201294,"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/computer-science","display_name":"Computer science","score":0.724086344242096},{"id":"https://openalex.org/keywords/silo","display_name":"Silo","score":0.7071231603622437},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.40858232975006104},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3620065748691559},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10628482699394226}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.724086344242096},{"id":"https://openalex.org/C2778024958","wikidata":"https://www.wikidata.org/wiki/Q213643","display_name":"Silo","level":2,"score":0.7071231603622437},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.40858232975006104},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3620065748691559},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10628482699394226},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3711896.3737129","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737129","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737129","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.whiterose.ac.uk:231319","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306400854","display_name":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2800616092","host_organization_name":"White Rose University Consortium","host_organization_lineage":["https://openalex.org/I2800616092"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"NonPeerReviewed"}],"best_oa_location":{"id":"doi:10.1145/3711896.3737129","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737129","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737129","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6268967910","display_name":null,"funder_award_id":"61906107","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320311026","display_name":"Shandong University","ror":"https://ror.org/0207yh398"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412876856.pdf","grobid_xml":"https://content.openalex.org/works/W4412876856.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W1886704267","https://openalex.org/W1964461063","https://openalex.org/W2017921654","https://openalex.org/W2069065514","https://openalex.org/W2171279286","https://openalex.org/W2788114581","https://openalex.org/W2998167534","https://openalex.org/W3034912136","https://openalex.org/W3035098013","https://openalex.org/W3040358113","https://openalex.org/W3173650575","https://openalex.org/W3214905160","https://openalex.org/W4284668299","https://openalex.org/W4284696020","https://openalex.org/W4290944300","https://openalex.org/W4313140708","https://openalex.org/W4318775883","https://openalex.org/W4382239876","https://openalex.org/W4385562623","https://openalex.org/W4385583963","https://openalex.org/W4386589995","https://openalex.org/W4393147851","https://openalex.org/W4394932368","https://openalex.org/W4396818449","https://openalex.org/W4399880766","https://openalex.org/W4400525124","https://openalex.org/W4401024812","https://openalex.org/W4401025090","https://openalex.org/W4401110399","https://openalex.org/W4401856686","https://openalex.org/W4401856734","https://openalex.org/W4403390424"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2523330964","https://openalex.org/W2383857829","https://openalex.org/W2023971635","https://openalex.org/W2903433011","https://openalex.org/W2485498725","https://openalex.org/W2388672951","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Next":[0],"location":[1,34,146],"prediction":[2,35,77,116,134],"is":[3],"a":[4,69,83,125,155],"critical":[5],"task":[6,117],"in":[7,14,44,75,186],"human":[8,56],"mobility":[9,57,103,174,189],"modeling,":[10],"with":[11,36,162],"broad":[12],"applications":[13],"personalized":[15],"recommendation,":[16],"urban":[17],"planning,":[18],"and":[19,55,94,141],"location-based":[20],"services.":[21],"Recently,":[22],"researchers":[23],"have":[24],"used":[25],"prompt-based":[26],"large":[27],"language":[28],"models":[29],"(LLMs)":[30],"to":[31,136],"improve":[32],"next":[33],"pre-trained":[37],"knowledge.":[38],"However,":[39],"they":[40],"face":[41],"inherent":[42],"challenges":[43],"bridging":[45],"the":[46,115,133],"gap":[47],"between":[48],"textual":[49],"prompts":[50,112,140],"for":[51,59,72,118],"semantic":[52,85,165,192],"contextual":[53,96,106],"understanding":[54],"data":[58],"transition":[60,160],"pattern":[61],"modeling.":[62],"In":[63],"this":[64],"paper,":[65],"we":[66,129,152],"introduce":[67,154],"SILO,":[68],"framework":[70],"designed":[71],"Semantic":[73],"Integration":[74],"LOcation":[76],"via":[78],"LLMs.":[79,195],"We":[80,108],"first":[81],"construct":[82],"hybrid":[84,142],"space":[86],"that":[87,113,177],"seamlessly":[88],"integrates":[89],"ID-based":[90],"embeddings,":[91],"text-derived":[92],"semantics,":[93],"auxiliary":[95],"information,":[97],"enabling":[98],"comprehensive":[99],"modeling":[100,187],"of":[101,145],"sequential":[102,159],"patterns":[104,190],"alongside":[105],"nuances.":[107],"then":[109],"propose":[110],"user-centric":[111],"specify":[114],"LLMs":[119,131],"while":[120],"embedding":[121],"user":[122,163],"context":[123],"within":[124],"special":[126],"token.":[127],"Further,":[128],"utilize":[130],"as":[132],"backbone":[135],"process":[137],"both":[138],"user-specific":[139],"ID-context":[143],"embeddings":[144],"sequences.":[147],"To":[148],"enhance":[149],"predictive":[150],"performance,":[151],"finally":[153],"dual-logits":[156],"strategy,":[157],"combining":[158],"logits":[161],"profile-guided":[164],"preference":[166],"logits.":[167],"Extensive":[168],"experiments":[169],"on":[170],"two":[171],"large-scale":[172],"real-world":[173],"datasets":[175],"demonstrate":[176],"SILO":[178],"significantly":[179],"outperforms":[180],"state-of-the-art":[181],"baselines,":[182],"validating":[183],"its":[184],"effectiveness":[185],"complex":[188],"through":[191],"integration":[193],"using":[194]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
