Exploiting Two-Dimensional Geographical and Synthetic Social Influences for Location Recommendation
Bibliographic Data
| ID | 22032052 |
|---|---|
| Authors | Jiping Liu (0000-0002-8338-8586, Wuhan University), Zhiran Zhang (0000-0003-1447-4517, Wuhan University, corresponding author), Chunyang Liu (0000-0002-3694-0075, China University of Mining and Technology), Agen Qiu (0009-0007-2195-9252, Chinese Academy of Surveying and Mapping), Fuhao Zhang (0009-0007-5702-7553, Chinese Academy of Surveying and Mapping) |
| Year | 2020 |
| Volume | 9 |
| Issue | 4 |
| Pages | 285 |
| Publication date | 2020-04-24 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi9040285 |
| OpenAlex | W3019472053 |
| Language | EN |
| Citations received | 2 |
| References cited | 47 |
With the rapid development of location-based social networks (LBSNs), because human behaviors exhibit specific distribution patterns, personalized geo-social recommendation has played a significant role for LBSNs. In addition to user preference and social influence, geographical influence has also been widely researched in location recommendation. Kernel density estimation (KDE) is a key method in modeling geographical influence. However, most current studies based on KDE do not consider the problems of influence range and outliers on users’ check-in behaviors. In this paper, we propose a method to exploit geographical and synthetic social influences (GeSSo) on location recommendation. GeSSo uses a kernel estimation approach with a quartic kernel function to model geographical influences, and two kinds of weighted distance are adopted to calculate bandwidth. Furthermore, we consider the social closeness and connections between friends, and a synthetic friend-based recommendation method is introduced to model social influences. Finally, we adopt a sum framework which combines user’s preferences on a location with geographical and social influences. Extensive experiments are conducted on three real-life datasets. The results show that our method achieves superior performance compared to other advanced geo-social recommendation techniques
Closeness · Data mining · Exploit · Kernel density estimation · Outlier · Statistics · Computer Science · Expert finding and Q&A systems · Human Mobility and Location-Based Analysis · Mathematics · Recommender Systems and Techniques · Artificial Intelligence
| Citations per year | 0.33 |
|---|---|
| Citation span | 2020 - 2020 (1) |
| Highly cited | No |