Abstract
Recently, location-based social networks (LBSNs) have witnessed a significant development, and have attracted millions of mobile users to share their locations and location-related contents. Due to the huge data in LBSNs, it is imperative to design an efficient location recommendation scheme for abundant users. But most of existing works suffer from poor accuracy and efficiency. On one hand, the traditional user-based collaborative filtering (CF) methods only focus on user characteristics, which limit the recommendation accuracy. On the other hand, it is inefficient to traverse all LBSN data in each recommendation. To solve the above issues, this paper proposes a new location recommendation method LC–G–P, in which, Louvain method is adopted to cluster users into several communities; and meanwhile, multiple features including Geographical distance, location Popularity are incorporated into location recommendation. Experiments based on real LBSN dataset illustrate that the proposed recommendation scheme LC–G–P has better accuracy and efficiency, in comparison with the existed typical location recommendation schemes.
| Original language | English |
|---|---|
| Pages (from-to) | 289-299 |
| Number of pages | 11 |
| Journal | Computers and Electrical Engineering |
| Volume | 77 |
| DOIs | |
| Publication status | Published - 2019 Jul 1 |
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Keywords
- Collaborative filtering
- Community detection
- Data fusion
- Location recommendation
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science(all)
- Electrical and Electronic Engineering
Cite this
An efficient location recommendation scheme based on clustering and data fusion. / Cai, Wenjie; Wang, Yufeng; Lv, Ruheng; Jin, Qun.
In: Computers and Electrical Engineering, Vol. 77, 01.07.2019, p. 289-299.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - An efficient location recommendation scheme based on clustering and data fusion
AU - Cai, Wenjie
AU - Wang, Yufeng
AU - Lv, Ruheng
AU - Jin, Qun
PY - 2019/7/1
Y1 - 2019/7/1
N2 - Recently, location-based social networks (LBSNs) have witnessed a significant development, and have attracted millions of mobile users to share their locations and location-related contents. Due to the huge data in LBSNs, it is imperative to design an efficient location recommendation scheme for abundant users. But most of existing works suffer from poor accuracy and efficiency. On one hand, the traditional user-based collaborative filtering (CF) methods only focus on user characteristics, which limit the recommendation accuracy. On the other hand, it is inefficient to traverse all LBSN data in each recommendation. To solve the above issues, this paper proposes a new location recommendation method LC–G–P, in which, Louvain method is adopted to cluster users into several communities; and meanwhile, multiple features including Geographical distance, location Popularity are incorporated into location recommendation. Experiments based on real LBSN dataset illustrate that the proposed recommendation scheme LC–G–P has better accuracy and efficiency, in comparison with the existed typical location recommendation schemes.
AB - Recently, location-based social networks (LBSNs) have witnessed a significant development, and have attracted millions of mobile users to share their locations and location-related contents. Due to the huge data in LBSNs, it is imperative to design an efficient location recommendation scheme for abundant users. But most of existing works suffer from poor accuracy and efficiency. On one hand, the traditional user-based collaborative filtering (CF) methods only focus on user characteristics, which limit the recommendation accuracy. On the other hand, it is inefficient to traverse all LBSN data in each recommendation. To solve the above issues, this paper proposes a new location recommendation method LC–G–P, in which, Louvain method is adopted to cluster users into several communities; and meanwhile, multiple features including Geographical distance, location Popularity are incorporated into location recommendation. Experiments based on real LBSN dataset illustrate that the proposed recommendation scheme LC–G–P has better accuracy and efficiency, in comparison with the existed typical location recommendation schemes.
KW - Collaborative filtering
KW - Community detection
KW - Data fusion
KW - Location recommendation
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U2 - 10.1016/j.compeleceng.2019.06.006
DO - 10.1016/j.compeleceng.2019.06.006
M3 - Article
AN - SCOPUS:85067391744
VL - 77
SP - 289
EP - 299
JO - Computers and Electrical Engineering
JF - Computers and Electrical Engineering
SN - 0045-7906
ER -