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Personalized Location Recommendation System Personalized Location Recommendation System: A Review

Personalized Location Recommendation System Personalized Location Recommendation System: A Review

Ashwini Arun Ughade
Copyright: © 2019 |Volume: 10 |Issue: 1 |Pages: 10
ISSN: 1942-3594|EISSN: 1942-3608|EISBN13: 9781522565680|DOI: 10.4018/IJAEC.2019010104
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MLA

Ughade, Ashwini Arun. "Personalized Location Recommendation System Personalized Location Recommendation System: A Review." IJAEC vol.10, no.1 2019: pp.49-58. http://doi.org/10.4018/IJAEC.2019010104

APA

Ughade, A. A. (2019). Personalized Location Recommendation System Personalized Location Recommendation System: A Review. International Journal of Applied Evolutionary Computation (IJAEC), 10(1), 49-58. http://doi.org/10.4018/IJAEC.2019010104

Chicago

Ughade, Ashwini Arun. "Personalized Location Recommendation System Personalized Location Recommendation System: A Review," International Journal of Applied Evolutionary Computation (IJAEC) 10, no.1: 49-58. http://doi.org/10.4018/IJAEC.2019010104

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Abstract

Location acquisition and wireless communication technologies are growing in location-based social networks. With the rapid development of location-based social networks (LBSNs), location recommendation has become an important for helping users to discover interesting locations. Most current studies on spatial item recommendations do not consider the sequential influence of locations. The authors proposed a personalized location recommendation system as a probabilistic generative model that aims to mimic the process of human decision-making when visiting locations. In this system, three tasks are involved, such as: extracting user's personal interests; extracting sequential influence; and combining them into unified networks. This system utilizes data collected from LBSNs to model a user's behavior and locations with real datasets, and it determines a user's preferred locations using collaborative filtering and a Locality Sensitive Hashing (ALSH) technique. It overcomes the challenges of the user's check-in data in LBSNs having a low sampling rate in both space and time and a huge prediction space.

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