article

Time-aware metric embedding with asymmetric projection for successive POI recommendation

Authors:

Tingting Liang,

Hui XiongAuthors Info & Claims

World Wide Web, Volume 22, Issue 5

Pages 2209 - 2224

https://doi.org/10.1007/s11280-018-0596-8

Published: 01 September 2019 Publication History

Abstract

Successive Point-of-Interest (POI) recommendation aims to recommend next POIs for a given user based on this user's current location. Indeed, with the rapid growth of Location-based Social Networks (LBSNs), successive POI recommendation has become an important and challenging task, since it can help to meet users' dynamic interests based on their recent check-in behaviors. While some efforts have been made for this task, most of them do not capture the following properties: 1) The transition between consecutive POIs in user check-in sequences presents asymmetric property, however existing approaches usually assume the forward and backward transition probabilities between a POI pair are symmetric. 2) Users usually prefer different successive POIs at different time, but most existing studies do not consider this dynamic factor. To this end, in this paper, we propose a time-aware metric embedding approach with asymmetric projection (referred to as MEAP-T) for successive POI recommendation, which takes the above two properties into consideration. In addition, we exploit three latent Euclidean spaces to project the POI-POI, POI-user, and POI-time relationships. Finally, the experimental results on two real-world datasets show MEAP-T outperforms the state-of-the-art methods in terms of both precision and recall.

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1. Information systems
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cover image World Wide Web

World Wide Web Volume 22, Issue 5

September 2019

341 pages

ISSN:1386-145X

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Copyright © Copyright © 2019 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 September 2019

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Zhao MHarvey MCameron DHopfgartner F(2024)The Effect of Simulated Contextual Factors on Recipe Rating and Nutritional Intake BehaviourProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638328(97-107)Online publication date: 10-Mar-2024
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Ou JJin HWang XJiang HWang XZhou C(2023)STA-TCN: Spatial-temporal Attention over Temporal Convolutional Network for Next Point-of-interest RecommendationACM Transactions on Knowledge Discovery from Data10.1145/359649717:9(1-19)Online publication date: 15-Jun-2023
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Zhang CZhao KChen M(2023)Beyond the Limits of Predictability in Human Mobility Prediction: Context-Transition PredictabilityIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.314830035:5(4514-4526)Online publication date: 1-May-2023
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