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Comparing Sequential and Temporal Patterns from Human Mobility Data for Next-Place Prediction

Comparing Sequential and Temporal Patterns from Human Mobility Data for Next-Place Prediction

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WANG, Yunlong, Corinna BREITINGER, Björn SOMMER, Falk SCHREIBER, Harald REITERER, 2018. Comparing Sequential and Temporal Patterns from Human Mobility Data for Next-Place Prediction. 26th Conference on User Modeling, Adaptation and Personalization. Singapore, Jul 8, 2018 - Jul 11, 2018. In: Proceedings of UMAP '18 : Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization. New York:ACM Press, pp. 157-164. ISBN 978-1-4503-5784-5. Available under: doi: 10.1145/3213586.3226212

@inproceedings{Wang2018Compa-42878, title={Comparing Sequential and Temporal Patterns from Human Mobility Data for Next-Place Prediction}, year={2018}, doi={10.1145/3213586.3226212}, isbn={978-1-4503-5784-5}, address={New York}, publisher={ACM Press}, booktitle={Proceedings of UMAP '18 : Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization}, pages={157--164}, author={Wang, Yunlong and Breitinger, Corinna and Sommer, Björn and Schreiber, Falk and Reiterer, Harald} }

<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/42878"> <dcterms:issued>2018</dcterms:issued> <dc:creator>Schreiber, Falk</dc:creator> <dc:contributor>Reiterer, Harald</dc:contributor> <dc:contributor>Sommer, Björn</dc:contributor> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-07-18T13:58:21Z</dcterms:available> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dc:language>eng</dc:language> <dcterms:title>Comparing Sequential and Temporal Patterns from Human Mobility Data for Next-Place Prediction</dcterms:title> <dcterms:rights rdf:resource="https://kops.uni-konstanz.de/page/termsofuse"/> <dc:creator>Reiterer, Harald</dc:creator> <dc:creator>Breitinger, Corinna</dc:creator> <dc:contributor>Schreiber, Falk</dc:contributor> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-07-18T13:58:21Z</dc:date> <dc:contributor>Wang, Yunlong</dc:contributor> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dcterms:abstract xml:lang="eng">In the domain of human behavior prediction, next-place prediction is an active research field. While prior work has applied sequential and temporal patterns for next-place prediction, no work has yet studied the prediction performance of combining sequential with temporal patterns compared to using them separately. In this paper, we address next-place prediction using the sequential and temporal patterns embedded in human mobility data that has been collected using the GPS sensor of smartphones. We test five next-place prediction methods, including single pattern-based methods and hybrid methods that combine temporal and sequential patterns. Instead of only examining average accuracy as in related work, we additionally evaluate the selected methods using incremental-prediction accuracy on two publicly available datasets (the MDC dataset and the StudentLife dataset). Our results suggest that (1) integrating multiple patterns is not necessarily more effective than using single patterns in average prediction accuracy, (2) most of the tested methods can outperform others for a certain time period (either for the prediction of all places or each place individually), and (3) average prediction accuracies of the top-three candidates using sequential patterns are relatively high (up to 0.77 and 0.91 in the median for both datasets). For real-time applications, we recommend applying multiple methods in parallel and choosing the prediction of the best method according to incremental-prediction accuracy. Lastly, we present an expert tool for visualizing the prediction results.</dcterms:abstract> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/42878/1/Wang_2-11ruk1hrgl5pj0.pdf"/> <dc:contributor>Breitinger, Corinna</dc:contributor> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/42878"/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:creator>Sommer, Björn</dc:creator> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/42878/1/Wang_2-11ruk1hrgl5pj0.pdf"/> <dc:rights>terms-of-use</dc:rights> <dc:creator>Wang, Yunlong</dc:creator> </rdf:Description> </rdf:RDF>

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