An Adaptive Point-Of-Interest Recommendation Method Based on Check-in Activity and Temporal-Spatial Probabilistic Models

Yali SI; Fuzhi ZHANG; Wenyuan LIU

doi:10.11999/JEIT190287

Volume 42 Issue 3

Mar. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(3): 678-686

Yali SI, Fuzhi ZHANG, Wenyuan LIU. An Adaptive Point-Of-Interest Recommendation Method Based on Check-in Activity and Temporal-Spatial Probabilistic Models[J]. Journal of Electronics & Information Technology, 2020, 42(3): 678-686. doi: 10.11999/JEIT190287

Citation:

Yali SI, Fuzhi ZHANG, Wenyuan LIU. An Adaptive Point-Of-Interest Recommendation Method Based on Check-in Activity and Temporal-Spatial Probabilistic Models[J]. Journal of Electronics & Information Technology, 2020, 42(3): 678-686. doi: 10.11999/JEIT190287

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An Adaptive Point-Of-Interest Recommendation Method Based on Check-in Activity and Temporal-Spatial Probabilistic Models

doi: 10.11999/JEIT190287

Yali SI^{1, 2, 3},
Fuzhi ZHANG^{1, 3
,
,},
Wenyuan LIU^{1, 3}

1.
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
2.
School of Liren, Yanshan University, Qinhuangdao 066004, China
3.
Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province (Yanshan University), Qinhuangdao 066004, China

Funds: The National Natural Science Foundation of China (61379116, 61772452), The Natural Science Foundation of Hebei Province (F2015203046, F2015501105)

Received Date: 2019-04-25
Rev Recd Date: 2019-10-29

Available Online: 2019-11-11

Publish Date: 2020-03-19

Abstract

Abstract

Existing Point-Of-Interest (POI) recommendation algorithms lack adaptability for users with different check-in features. To solve this problem, an adaptive POI recommendation method UCA-TS based on User Check-in Activity (UCA) feature and Temporal-Spatial (TS) probabilistic models is proposed. The user check-in activity is extracted using a probabilistic statistical analysis method, and a calculation method of user's inactive and active membership is given. On this basis, one-dimensional power law function and two-dimensional Gaussian kernel density estimation combined with time factor are used to calculate the probability for inactive and active features respectively, and the popularity of POI is incorporated to recommend. This method can adapt to the users' check-in features and reflect the users' check-in temporal-spatial preferences more accurately. The experiments show that the proposed method can effectively improve the recommendation precision and recall.
- Location-based social networks,
- Point-Of-Interest (POI) recommendation,
- User activity,
- Membership,
- Gaussian kernel density estimation

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References(17)

References

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