Research on POI Recommendation Model Based on Spatio-temporal Context Information

Jihua YE; Siyu YANG; Jiali ZUO; Mingwen WANG

doi:10.11999/JEIT200368

Volume 43 Issue 12

Dec. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(12): 3546-3553

Jihua YE, Siyu YANG, Jiali ZUO, Mingwen WANG. Research on POI Recommendation Model Based on Spatio-temporal Context Information[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3546-3553. doi: 10.11999/JEIT200368

Citation:

Jihua YE, Siyu YANG, Jiali ZUO, Mingwen WANG. Research on POI Recommendation Model Based on Spatio-temporal Context Information[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3546-3553. doi: 10.11999/JEIT200368

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Research on POI Recommendation Model Based on Spatio-temporal Context Information

doi: 10.11999/JEIT200368

Institute of Computer Information Engineering, Jiangxi Normal University, Nanchang 330022, China

Funds: The National Natural Science Foundation of China (61462042, 61866018, 61876074)

Received Date: 2020-05-07
Rev Recd Date: 2021-04-26

Available Online: 2021-06-02

Publish Date: 2021-12-21

Abstract

Abstract

With the high-speed development of Location-Based Social Networking (LBSN) technology, Point-Of-Interest(POI) recommendation for providing personalized services to mobile users has become the focus of attention. Because POI recommendation is faced with the challenges of sparse data, multiple influencing factors and complex user preferences, traditional POI recommendation usually only considers the influence of check-in frequency, check-in time and place on users, but ignores the correlation influence of users’ behaviors before and after the check-in sequence. In order to solve the above problems, this paper takes into account the time influence and spatial influence of the check-in data through the representation of the sequence, and establishes a Spatio-Temporal Context information of POI Recommendations (STCPR), provides a more accurate and personalized preference for POI Recommendations. The model based on the framework of sequence to sequence, the user information, POI information, categories and space-time context information are embedded in GRU helped after vectorization network, at the same time the time attention mechanism, the mechanism of spatial attention of the global and local comprehensive are used for consideration of user preferences and trends, and in Top - N POI is recommended to users. In order to verify the performance of the model, experiments on two real data sets show that the proposed method is superior to several existing methods in terms of recall rate (Recall) and Normalized Discounted Cumulative Gain (NDCG).
- Point-Of-Interest (POI) recommendation model,
- Spatiotemporal correlation,
- Context,
- Attention mechanism

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References(19)

References

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