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Knowledge and Information Systems

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04.10.2017 | Regular Paper

A time-aware trajectory embedding model for next-location recommendation

verfasst von: Wayne Xin Zhao, Ningnan Zhou, Aixin Sun, Ji-Rong Wen, Jialong Han, Edward Y. Chang

Erschienen in: Knowledge and Information Systems | Ausgabe 3/2018

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Abstract

Next-location recommendation is an emerging task with the proliferation of location-based services. It is the task of recommending the next location to visit for a user, given her past check-in records. Although several principled solutions have been proposed for this task, existing studies have not well characterized the temporal factors in the recommendation. From three real-world datasets, our quantitative analysis reveals that temporal factors play an important role in next-location recommendation, including the periodical temporal preference and dynamic personal preference. In this paper, we propose a Time-Aware Trajectory Embedding Model (TA-TEM) to incorporate three kinds of temporal factors in next-location recommendation. Based on distributed representation learning, the proposed TA-TEM jointly models multiple kinds of temporal factors in a unified manner. TA-TEM also enhances the sequential context by using a longer context window. Experiments show that TA-TEM outperforms several competitive baselines.

Vorheriger Artikel From location to location pattern privacy in location-based services

Nächster Artikel On route planning by inferring visiting time, modeling user preferences, and mining representative trip patterns

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To check the recall of the ground-truth location using the above candidate generation method, we compute the hit ratios of the ground-truth location among the 5000 nearest locations on the three datasets as follows: 93, 96 and 98%. It can be seen the majority of the ground-truth locations were recalled using 5000 nearest locations.

It was originally proposed for next-basket recommendation in shopping, and we have slightly modified it to adapt to the current two tasks.

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Due to multi-threading techniques, the time cost does not show a strict linear increase with the increasing of VS.

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Titel: A time-aware trajectory embedding model for next-location recommendation
verfasst von: Wayne Xin Zhao
Ningnan Zhou
Aixin Sun
Ji-Rong Wen
Jialong Han
Edward Y. Chang
Publikationsdatum: 04.10.2017
Verlag: Springer London
Erschienen in: Knowledge and Information Systems / Ausgabe 3/2018
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI: https://doi.org/10.1007/s10115-017-1107-4

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