Survey of Point-of-Interest Recommendation Research Fused with Deep Learning

GUO Danhuai; ZHANG Mingke; JIA Nan; WANG Yangang

doi:10.13203/j.whugis20200334

Volume 45 Issue 12

Dec. 2020

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Geomatics and Information Science of Wuhan University > 2020 > 45(12): 1890-1902. > DOI: 10.13203/j.whugis20200334

GUO Danhuai, ZHANG Mingke, JIA Nan, WANG Yangang. Survey of Point-of-Interest Recommendation Research Fused with Deep Learning[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1890-1902. DOI: 10.13203/j.whugis20200334

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Survey of Point-of-Interest Recommendation Research Fused with Deep Learning

GUO Danhuai^{1, 2,},
ZHANG Mingke^{1, 2},
JIA Nan³,
WANG Yangang^{1, 2}

1.
Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
2.
Uinversity of Chinese Academy of Sciences, Beijing 100049, China
3.
Peoples' Public Security University of China, Beijing 100038, China

Funds:

The National Natural Science Foundation of China 41971366

The National Natural Science Foundation of China 91846301

The National Natural Science Foundation of China 71904095

the National Key Research and Development Program of China 2018YFC0809700

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Author Bio:
GUO Danhuai, PhD, associate professor, specializes in GeoAI and spatial scene similarity computing. E-mail: guodanhuai@cnic.cn
Received Date: October 14, 2020
Published Date: December 04, 2020

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Abstract

Abstract

Point-of-interest (POI) recommendation has emerged as a focal point in the research of location-based social network (LBSN) in recent years. It can help users find their favorite venue and bring considerable benefits to businesses. Nowadays, deep learning is gradually applied to the task of recommendation system because it can capture the nonlinear relationship between users and items more effectively. This paper thus focuses on recent research on POI recommendation combined with deep learning. Firstly, we introduce the difference between POI recommendation and other traditional recommendation tasks and illustrate various influencing factors that can improve the performance of the model. Then, the methods of applying deep learning to POI recommendation are divided into four categories, including POI embedding, deep collaborative filtering, feature extraction from side information, and sequence recommendation using recurrent neural network (RNN). We also investigate the development of user models performance and advantages combined with deep learning in these different aspects of applications. Finally, we summarize and look forward to the development of POI recommendation research combined with deep learning.
- venue recommendation,
- deep learning,
- collaborative filtering,
- personalized recommendation

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References

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