Extreme Learning Machine Based Point-of-Interest Recommendation in Location-Based Social Networks

2016 ◽  
pp. 249-261 ◽  
Author(s):  
Mo Chen ◽  
Feng Li ◽  
Ge Yu ◽  
Dan Yang
Keyword(s):  
Social Networks ◽  
Extreme Learning Machine ◽  
Point Of Interest ◽  
Learning Machine ◽  
Location Based Social Networks

Trust-Aware Personalized Route Query Using Extreme Learning Machine in Location-Based Social Networks

2018 ◽  
Vol 10 (6) ◽  
pp. 965-979 ◽  
Author(s):  
Yuliang Ma ◽  
Ye Yuan ◽  
Guoren Wang ◽  
Xin Bi ◽  
Hongchao Qin
Keyword(s):  
Social Networks ◽  
Extreme Learning Machine ◽  
Learning Machine ◽  
Location Based Social Networks ◽  
Route Query

scholarly journals Point-of-Interest Recommender Systems based on Location-Based Social Networks: A Survey from an Experimental Perspective

2022 ◽  
Author(s):  
Pablo Sánchez ◽  
Alejandro Bellogín
Keyword(s):  
Social Networks ◽  
Recommender Systems ◽  
Information Sources ◽  
Learning Approaches ◽  
Performance Improvements ◽  
Point Of Interest ◽  
Real Performance ◽  
Research Questions ◽  
Developing Area ◽  
Location Based Social Networks

Point-of-Interest recommendation is an increasing research and developing area within the widely adopted technologies known as Recommender Systems. Among them, those that exploit information coming from Location-Based Social Networks (LBSNs) are very popular nowadays and could work with different information sources, which pose several challenges and research questions to the community as a whole. We present a systematic review focused on the research done in the last 10 years about this topic. We discuss and categorize the algorithms and evaluation methodologies used in these works and point out the opportunities and challenges that remain open in the field. More specifically, we report the leading recommendation techniques and information sources that have been exploited more often (such as the geographical signal and deep learning approaches) while we also alert about the lack of reproducibility in the field that may hinder real performance improvements.

A Unified Point-of-Interest Recommendation Framework in Location-Based Social Networks

2016 ◽  
Vol 8 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Chen Cheng ◽  
Haiqin Yang ◽  
Irwin King ◽  
Michael R. Lyu
Keyword(s):  
Social Networks ◽  
Point Of Interest ◽  
Location Based Social Networks

A New Point-of-Interest Classification Model with an Extreme Learning Machine

2018 ◽  
Vol 10 (6) ◽  
pp. 951-964 ◽  
Author(s):  
Zhen Zhang ◽  
Xiangguo Zhao ◽  
Guoren Wang ◽  
Xin Bi
Keyword(s):  
Extreme Learning Machine ◽  
Classification Model ◽  
Point Of Interest ◽  
Learning Machine

scholarly journals Mobile User Location Inference Attacks Fusing with Multiple Background Knowledge in Location-Based Social Networks

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 262 ◽  
Author(s):  
Xiao Pan ◽  
Weizhang Chen ◽  
Lei Wu
Keyword(s):  
Social Networks ◽  
Location Privacy ◽  
Background Knowledge ◽  
Mobile User ◽  
Inference Model ◽  
Point Of Interest ◽  
Information User ◽  
Location Inference ◽  
Location Based Social Networks ◽  
Location Privacy Protection

Location-based social networks have been widely used. However, due to the lack of effective and safe data management, a large number of privacy disclosures commonly occur. Thus, academia and industry have needed to focus more on location privacy protection. This paper proposes a novel location attack method using multiple background options to infer the hidden locations of mobile users. In order to estimate the possibility of a hidden position being visited by a user, two hidden location attack models are proposed, i.e., a Bayesian hidden location inference model and the multi-factor fusion based hidden location inference model. Multiple background factors, including the check-in sequences, temporal information, user social networks, personalized service preferences, point of interest (POI) popularities, etc., are considered in the two models. Moreover, a hidden location inference algorithm is provided as well. Finally, a series of experiments are conducted on two real check-in data examples to evaluate the accuracy of the model and verify the validity of the proposed algorithm. The experimental results show that multiple background knowledge fusion provides benefits for improving location inference precision.

scholarly journals Finding Potential Propagators and Customers in Location-Based Social Networks: An Embedding-Based Approach

2020 ◽  
Vol 10 (22) ◽  
pp. 8003
Author(s):  
Yi-Chun Chen ◽  
Cheng-Te Li
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Main Idea ◽  
Feature Representation ◽  
Specific Point ◽  
Point Of Interest ◽  
The Future ◽  
Location Based Social Networks ◽  
Low Dimensional ◽  
Embedding Methods

In the scenarios of location-based social networks (LBSN), the goal of location promotion is to find information propagators to promote a specific point-of-interest (POI). While existing studies mainly focus on accurately recommending POIs for users, less effort is made for identifying propagators in LBSN. In this work, we propose and tackle two novel tasks, Targeted Propagator Discovery (TPD) and Targeted Customer Discovery (TCD), in the context of Location Promotion. Given a target POI l to be promoted, TPD aims at finding a set of influential users, who can generate more users to visit l in the future, and TCD is to find a set of potential users, who will visit l in the future. To deal with TPD and TCD, we propose a novel graph embedding method, LBSN2vec. The main idea is to jointly learn a low dimensional feature representation for each user and each location in an LBSN. Equipped with learned embedding vectors, we propose two similarity-based measures, Influential and Visiting scores, to find potential targeted propagators and customers. Experiments conducted on a large-scale Instagram LBSN dataset exhibit that LBSN2vec and its variant can significantly outperform well-known network embedding methods in both tasks.

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