POI Recommendation of Spatiotemporal Sequence Embedding in Gated Dilation Residual Network

JIANG Tao; XU Shenghua; LI Xiaoyan; ZHANG Zhiran; WANG Yong; LUO An; HE Xuan

doi:10.13203/j.whugis20220658

Volume 49 Issue 9

Sep. 2024

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Geomatics and Information Science of Wuhan University > 2024 > 49(9): 1683-1692. > DOI: 10.13203/j.whugis20220658

JIANG Tao, XU Shenghua, LI Xiaoyan, ZHANG Zhiran, WANG Yong, LUO An, HE Xuan. POI Recommendation of Spatiotemporal Sequence Embedding in Gated Dilation Residual Network[J]. Geomatics and Information Science of Wuhan University, 2024, 49(9): 1683-1692. DOI: 10.13203/j.whugis20220658

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POI Recommendation of Spatiotemporal Sequence Embedding in Gated Dilation Residual Network

1.
Research Center of Geospatial Big Data Application , Chinese Academy of Surveying and Mapping, Beijing100830,China
2.
School of Resource and Environmental Sciences, Wuhan University, Wuhan430079,China
3.
School of Geomatics, Liaoning Technical University, Fuxin123000,China
4.
School of Earth Sciences and Engineering, Xi'an Shiyou University, Xi'an710065,China

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Received Date: June 03, 2023
Available Online: July 05, 2023

Graphical Abstract

Abstract

Abstract

Objectives
Personalized point of interest (POI) recommendation is a vital service in location-based social network. It can effectively use the sequence and spatiotemporal context information of check-in data to discover movement patterns and preferences of users.
Methods
This paper proposes a probabilistic generative model with embedded spatiotemporal conditions to fully exploit the long-term dependency between personalized spatiotemporal preferences and sequential check-in sequences of users, constructs a gated dilation residual network, and implements a POI recommendation method based on gated dilation residual network. The method in this paper learns check-in sequences of users through a gated dilation residual network. It mines and captures the spatiotemporal patterns, sequence preferences and temporal preferences constrained by the spatial distance and time interval of sequential check-in behavior of users.
Results
The proposed method shows significant improvements on the Foursquare and Instagram datasets. Compared to the best-performing algorithm NextItNet, our method demonstrates noticeable enhancements in terms of recall, precision, F1 score, and normalized discounted cumulative gain. On the Foursquare dataset, we achieve improvements ranging from 1.52% to 24.95%. On the Instagram dataset, the improvements range from 7.06% to 42.47%.
Conclusions
The proposed method is more suitable for mining the long-term dependency relationships in sequential check-in behavior of users. It effectively incorporates spatial distance and temporal interval factors, thereby improving the accuracy of POI recommendation.
- point of interest recommendation,
- spatial distance,
- time interval,
- gated dilation residual,
- spatiotemporal sequence

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References

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POI Recommendation of Spatiotemporal Sequence Embedding in Gated Dilation Residual Network

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