A robust personalized location recommendation based on ensemble learning

Zhu, Jun; Han, Lixin; Gou, Zhinan; Yang, Yi; Yuan, Xiaofeng; Li, Jingxian; Li, Shu

doi:10.1016/j.eswa.2020.114065

Cited by 3 publications

(1 citation statement)

References 66 publications

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“…On location based social networks, an important feature is to recommend POI to users (Zhu, et al 2021) (Han, et al 2023).…”

Section: Literature Reviewmentioning

confidence: 99%

Three-layered location recommendation algorithm using spectral clustering

Moayedikia,

Jahani,

Rahman

et al. 2024

Soc. Netw. Anal. Min.

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Users utilize Location-Based Social Networks (LBSNs) to check into diverse venues and share their experiences through ratings and comments. However, these platforms typically feature a considerably larger number of locations than users, resulting in a challenge known as insufficient historical data or user-location matrix sparsity. This sparsity arises because not all users can check into all available locations on a given LBSN, such as Yelp. To address this challenge, this paper proposes combining Spectral Clustering with a three-layered location recommendation model to develop a recommender system named LSC, applied to Yelp datasets. LSC leverages various information, including users’ check-in data, demographics, location demographics, and users’ friendship network data, to train the recommender system and generate recommendations. Evaluation of LSC’s performance utilizes the Yelp dataset and several comparison metrics, such as accuracy, RMSE, and F1-score. The results demonstrate that our proposed algorithm delivers reliable and significant performance improvements across various evaluation metrics compared to competing algorithms.

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“…On location based social networks, an important feature is to recommend POI to users (Zhu, et al 2021) (Han, et al 2023).…”

Section: Literature Reviewmentioning

confidence: 99%

Three-layered location recommendation algorithm using spectral clustering

Moayedikia,

Jahani,

Rahman

et al. 2024

Soc. Netw. Anal. Min.

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Personalized Recommendation System of Smart Library Based on Deep Learning

Kang¹

2022

Lecture Notes on Data Engineering and Communications Technologies

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A collaborative filtering recommendation algorithm based on fuzzy C-means clustering

Huang

2022

IFS

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With the rapid growth of data scale, the problems of collaborative filtering recommendation algorithm are more and more obvious, such as data sparsity, cold start, scalability, and the change of user interest over time. About the existing problems, we introduce the fuzzy clustering and propose a collaborative filtering algorithm based on fuzzy C-means clustering. The algorithm performs fuzzy clustering on the item attribute information to make items belonging to different categories in different membership degree, increases the data density, effectively reduces the data sparsity, and solves the issue that the inaccuracy of similarity leads to the low recommendation accuracy. Meanwhile, the algorithm introduces the time weight function. Different evaluation times give different time weight values, and recently evaluated items are more representative of the user current interest, so we give a higher weight value, and early evaluated items have less effect on the user current interest, thus the weight value are relatively lower. The experimental results show that our algorithm can effectively alleviate the data sparsity problem and time migration of users preferences, thus achieve better performance.

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A robust personalized location recommendation based on ensemble learning

Cited by 3 publications

References 66 publications

Three-layered location recommendation algorithm using spectral clustering

Three-layered location recommendation algorithm using spectral clustering

Personalized Recommendation System of Smart Library Based on Deep Learning

A collaborative filtering recommendation algorithm based on fuzzy C-means clustering

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