Time and Location Aware Points of Interest Recommendation in Location-Based Social Networks

2018 ◽  
Vol 33 (6) ◽  
pp. 1219-1230 ◽  
Author(s):  
Tie-Yun Qian ◽  
Bei Liu ◽  
Liang Hong ◽  
Zhen-Ni You
Keyword(s):  
Social Networks ◽  
Location Aware ◽  
Points Of Interest ◽  
Location Based Social Networks

scholarly journals Estratégias para Aprimorar a Diversidade Categórica e Geográfica de Sistemas de Recomendação de POIs

2020 ◽  
Author(s):  
Rodrigo Carvalho ◽  
Leonardo Rocha
Keyword(s):  
Social Networks ◽  
User Satisfaction ◽  
Recommendation Systems ◽  
Experimental Results ◽  
Data Sets ◽  
Processing Strategy ◽  
Post Processing ◽  
Practical Utility ◽  
Points Of Interest ◽  
Location Based Social Networks

Currently, so-called Recommendation Systems (SRs) have been used to assist users in discovering relevant Points of Interest (POIs) on Location-Based Social Networks (LBSN), such as FourSquare and Yelp. Given the main challenges of data-sparse and geographic influence in this scenario, most of the work on POI recommendations has focused only on improving the effectiveness (i.e. accuracy) of the systems. However, there is a growing consensus that just effectiveness is not sufficient to assess the practical utility of these systems. In real scenarios, categorical and geographic diversities were identified as the main complementary dimensions for assessing user satisfaction and the usefulness of recommendations. The works in the literature are concentrated on only one of these concepts. In this work, we propose a new post-processing strategy, which combines these concepts in order to improve the user’s interest in POIs. Our experimental results in the Yelp data sets show that our strategy can improve user satisfaction, considering different SRs and multiple diversification metrics. Our method is capable of improving diversity by up to 120 % without significant losses in terms of effectiveness.

scholarly journals An Automatic Annotation Method for Discovering Semantic Information of Geographical Locations from Location-Based Social Networks

2019 ◽  
Vol 8 (11) ◽  
pp. 487 ◽  
Author(s):  
Zou ◽  
He ◽  
Zhu
Keyword(s):  
New York ◽  
Social Networks ◽  
Semantic Information ◽  
Automatic Annotation ◽  
Baseline Method ◽  
Points Of Interest ◽  
The Social ◽  
Rich Information ◽  
Location Based Social Networks ◽  
Geographical Locations

Location-Based Social Networks (LBSNs) contain rich information that can be used to identify and annotate points of interest (POIs). Discovering these POIs and annotating them with this information is not only helpful for understanding the social behavior of users, but it also provides benefits for location recommendations. However, current methods still have some limitations, such as a long annotating time and a low annotating accuracy. In this study, we develop a hybrid method to annotate POIs with meaningful information from LBSNs. The method integrates three patterns: temporal, spatial, and text patterns. Firstly, we present an approach for preprocessing data based on temporal patterns. Secondly, we describe a way to discover POIs through spatial patterns. Thirdly, we build a keyword dictionary for discovering the categories of POIs to be annotated via mining the text patterns. Finally, we integrate these three patterns to label each POI. Taking New York and London as the target areas, we accomplish automatic POI annotation by using Precision, Recall, and F-values to evaluate the effectiveness. The results show that our F-value is 78%, which is superior to that of the baseline method (Falcone's method) at 73% and this suggests that our method is effective in extracting POIs and assigning them categories.

Tracking Group Movement in Location Based Social Networks

2020 ◽  
Author(s):  
Sameera Kannangara ◽  
Hairuo Xie ◽  
Egemen Tanin ◽  
Aaron Harwood ◽  
Shanika Karunasekera
Keyword(s):  
Social Networks ◽  
Group Movement ◽  
Location Based Social Networks
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