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

CY - Basel

T2 - 17th International Symposium on Intelligent Data Analysis - IDA 2018

Y2 - 24 October 2018 through 26 October 2018

ER -