Article: PRMF: point of interest recommendation method integrating multiple factors Journal: International Journal of Web Engineering and Technology (IJWET) 2023 Vol.18 No.1 pp.45 - 61 Abstract: Point-of-interest (POI) recommendation plays an increasingly important role in location-based social networks (LBSNs) and is widely used in various e-commerce websites. However, due to the high sparsity of user check-in information, it is still challenging to recommend appropriate and accurate locations to users. As people decide where to visit based on numerous factors, recommendation systems need to consider check-in records and data on POI popularity and POI locations. In this paper, we propose a POI recommendation method that integrates multiple factors by analysing users' check-in records, POI category, location, and POI popularity, called PRMF. Firstly, we employ a neural network algorithm to calculate user preferences. Activity centres are then calculated based on the users' historical check-in history, and geographical preferences for each POI are calculated according to the activity centre. By combining the popularity of POIs in this study, we calculate POI popularity preferences, and the above three parts were obtained by linear fusion to calculate the users' final preference. Extensive experiments (based on real datasets, including long-term check-in data for locations in New York and Tokyo collected from Foursquare) show that our proposed method was superior to the baselines. Inderscience Publishers - linking academia, business and industry through research

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Title: PRMF: point of interest recommendation method integrating multiple factors

Authors: Ting Yu; Lihua Zhang; Yinhao Zhang

Addresses: Jiaxing Nanhu University, Jiaxing, Zhejiang 314001, China ' Jiaxing Nanhu University, Jiaxing, Zhejiang 314001, China ' Jiaxing Nanhu University, Jiaxing, Zhejiang 314001, China

Abstract: Point-of-interest (POI) recommendation plays an increasingly important role in location-based social networks (LBSNs) and is widely used in various e-commerce websites. However, due to the high sparsity of user check-in information, it is still challenging to recommend appropriate and accurate locations to users. As people decide where to visit based on numerous factors, recommendation systems need to consider check-in records and data on POI popularity and POI locations. In this paper, we propose a POI recommendation method that integrates multiple factors by analysing users' check-in records, POI category, location, and POI popularity, called PRMF. Firstly, we employ a neural network algorithm to calculate user preferences. Activity centres are then calculated based on the users' historical check-in history, and geographical preferences for each POI are calculated according to the activity centre. By combining the popularity of POIs in this study, we calculate POI popularity preferences, and the above three parts were obtained by linear fusion to calculate the users' final preference. Extensive experiments (based on real datasets, including long-term check-in data for locations in New York and Tokyo collected from Foursquare) show that our proposed method was superior to the baselines.

Keywords: point of interest; POI; recommendation methods; location social network; neural network; feature extraction; geographical location; popularity; MLP; user preferences; multi-factor.

DOI: 10.1504/IJWET.2023.131141

International Journal of Web Engineering and Technology, 2023 Vol.18 No.1, pp.45 - 61

Received: 24 Jun 2022
Received in revised form: 07 Jan 2023
Accepted: 14 Feb 2023
Published online: 31 May 2023 *

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Title: PRMF: point of interest recommendation method integrating multiple factors

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