MDP-based itinerary recommendation using geo-tagged social media
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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Advances in Intelligent Data Analysis XVII - 17th International Symposium, IDA 2018, Proceedings. Hrsg. / Wouter Duivesteijn; Arno Siebes; Antti Ukkonen. Basel: Springer Nature AG, 2018. S. 111-123 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Nr. 11191).
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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TY - CHAP
T1 - MDP-based itinerary recommendation using geo-tagged social media
AU - Gaonkar, Radhika
AU - Tavakol, Maryam
AU - Brefeld, Ulf
N1 - Conference code: 17
PY - 2018/10/25
Y1 - 2018/10/25
N2 - Planning vacations is a complex decision problem. Many variables like the place(s) to visit, how many days to stay, the duration at each location, and the overall travel budget need to be controlled and arranged by the user. Automatically recommending travel itineraries would thus be a remedy to quickly converge to an individual trip that is tailored to a user’s interests. While on a trip, users frequently share their experiences on social media platforms e.g., by uploading photos of specific locations and times of day. Their uploaded data serves as an asset when it comes to gathering information on their journey. In this paper, we leverage social media, more explicitly photo uploads and their tags, to reverse engineer historic user itineraries. Our solution grounds on Markov decision processes that capture the sequential nature of itineraries. The tags attached to the photos provide the factors to generate possible configurations and prove crucial for contextualising the proposed approach. Empirically, we observe that the predicted itineraries are more accurate than standard path planning algorithms.
AB - Planning vacations is a complex decision problem. Many variables like the place(s) to visit, how many days to stay, the duration at each location, and the overall travel budget need to be controlled and arranged by the user. Automatically recommending travel itineraries would thus be a remedy to quickly converge to an individual trip that is tailored to a user’s interests. While on a trip, users frequently share their experiences on social media platforms e.g., by uploading photos of specific locations and times of day. Their uploaded data serves as an asset when it comes to gathering information on their journey. In this paper, we leverage social media, more explicitly photo uploads and their tags, to reverse engineer historic user itineraries. Our solution grounds on Markov decision processes that capture the sequential nature of itineraries. The tags attached to the photos provide the factors to generate possible configurations and prove crucial for contextualising the proposed approach. Empirically, we observe that the predicted itineraries are more accurate than standard path planning algorithms.
KW - Digital media
KW - Personalisation
KW - Itinerary recommendation
KW - MDP
UR - http://www.scopus.com/inward/record.url?scp=85055718851&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-01768-2_10
DO - 10.1007/978-3-030-01768-2_10
M3 - Article in conference proceedings
AN - SCOPUS:85055718851
SN - 978-3-030-01767-5
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 111
EP - 123
BT - Advances in Intelligent Data Analysis XVII - 17th International Symposium, IDA 2018, Proceedings
A2 - Duivesteijn, Wouter
A2 - Siebes, Arno
A2 - Ukkonen, Antti
PB - Springer Nature AG
CY - Basel
T2 - 17th International Symposium on Intelligent Data Analysis - IDA 2018
Y2 - 24 October 2018 through 26 October 2018
ER -