{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1360580237197200000.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.1145/3295499"}},{"identifier":{"@type":"URI","@value":"https://dl.acm.org/doi/10.1145/3295499"}},{"identifier":{"@type":"URI","@value":"https://dl.acm.org/doi/pdf/10.1145/3295499"}}],"dc:title":[{"@value":"Spatiotemporal Representation Learning for Translation-Based POI Recommendation"}],"description":[{"type":"abstract","notation":[{"@value":"<jats:p>\n            The increasing proliferation of location-based social networks brings about a huge volume of user check-in data, which facilitates the recommendation of points of interest (POIs). Time and location are the two most important contextual factors in the user’s decision-making for choosing a POI to visit. In this article, we focus on the\n            <jats:italic>spatiotemporal context-aware</jats:italic>\n            POI recommendation, which considers the joint effect of time and location for POI recommendation. Inspired by the recent advances in knowledge graph embedding, we propose a\n            <jats:italic>spatiotemporal context-aware</jats:italic>\n            and translation-based recommender framework (STA) to model the third-order relationship among users, POIs, and spatiotemporal contexts for large-scale POI recommendation. Specifically, we embed both users and POIs into a “transition space” where spatiotemporal contexts (i.e., a <\n            <jats:italic>time, location</jats:italic>\n            > pair) are modeled as\n            <jats:italic>translation vectors</jats:italic>\n            operating on users and POIs. We further develop a series of strategies to exploit various correlation information to address the data sparsity and cold-start issues for new spatiotemporal contexts, new users, and new POIs. We conduct extensive experiments on two real-world datasets. The experimental results demonstrate that our STA framework achieves the superior performance in terms of high recommendation accuracy, robustness to data sparsity, and effectiveness in handling the cold-start problem.\n          </jats:p>"}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1380580237197200000","@type":"Researcher","foaf:name":[{"@value":"Tieyun Qian"}],"jpcoar:affiliationName":[{"@value":"Wuhan University, China"}]},{"@id":"https://cir.nii.ac.jp/crid/1380580237197200002","@type":"Researcher","foaf:name":[{"@value":"Bei Liu"}],"jpcoar:affiliationName":[{"@value":"Wuhan University, China"}]},{"@id":"https://cir.nii.ac.jp/crid/1380580237197200003","@type":"Researcher","foaf:name":[{"@value":"Quoc Viet Hung Nguyen"}],"jpcoar:affiliationName":[{"@value":"Griffith University, QLD, Australia"}]},{"@id":"https://cir.nii.ac.jp/crid/1380580237197200001","@type":"Researcher","foaf:name":[{"@value":"Hongzhi Yin"}],"jpcoar:affiliationName":[{"@value":"The University of Queensland, QLD, Australia"}]}],"publication":{"publicationIdentifier":[{"@type":"PISSN","@value":"10468188"},{"@type":"EISSN","@value":"15582868"}],"prism:publicationName":[{"@value":"ACM Transactions on Information Systems"}],"dc:publisher":[{"@value":"Association for Computing Machinery (ACM)"}],"prism:publicationDate":"2019-01-27","prism:volume":"37","prism:number":"2","prism:startingPage":"1","prism:endingPage":"24"},"reviewed":"false","dc:rights":["https://www.acm.org/publications/policies/copyright_policy#Background"],"url":[{"@id":"https://dl.acm.org/doi/10.1145/3295499"},{"@id":"https://dl.acm.org/doi/pdf/10.1145/3295499"}],"createdAt":"2019-01-28","modifiedAt":"2025-06-18","relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1360861707384686336","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"STaTRL: Spatial-temporal and text representation learning for POI recommendation"}]},{"@id":"https://cir.nii.ac.jp/crid/1360869855140297344","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"RPMTD: A Route Planning Model With Consideration of Tourists’ Distribution"}]}],"dataSourceIdentifier":[{"@type":"CROSSREF","@value":"10.1145/3295499"},{"@type":"CROSSREF","@value":"10.1007/s10489-022-03858-w_references_DOI_FrollAgN0LL8qZDBFNkQSsuSHy0"},{"@type":"CROSSREF","@value":"10.1109/access.2024.3400373_references_DOI_FrollAgN0LL8qZDBFNkQSsuSHy0"}]}