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.

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

Large-scale dynamic social data representation for structure feature learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5253-5262
Author(s):  
Xiaoxian Zhang ◽  
Jianpei Zhang ◽  
Jing Yang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Large Scale ◽  
Dynamic Change ◽  
Feature Learning ◽  
Data Representation ◽  
Feature Representation ◽  
Small Scale ◽  
Dynamic Feature ◽  
Dynamic Social Networks

The problems caused by network dimension disasters and computational complexity have become an important issue to be solved in the field of social network research. The existing methods for network feature learning are mostly based on static and small-scale assumptions, and there is no modified learning for the unique attributes of social networks. Therefore, existing learning methods cannot adapt to the dynamic and large-scale of current social networks. Even super large scale and other features. This paper mainly studies the feature representation learning of large-scale dynamic social network structure. In this paper, the positive and negative damping sampling of network nodes in different classes is carried out, and the dynamic feature learning method for newly added nodes is constructed, which makes the model feasible for the extraction of structural features of large-scale social networks in the process of dynamic change. The obtained node feature representation has better dynamic robustness. By selecting the real datasets of three large-scale dynamic social networks and the experiments of dynamic link prediction in social networks, it is found that DNPS has achieved a large performance improvement over the benchmark model in terms of prediction accuracy and time efficiency. When the α value is around 0.7, the model effect is optimal.

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.

Embedding Hierarchical Structures for Venue Category Representation

2022 ◽  
Vol 40 (3) ◽  
pp. 1-29
Author(s):  
Meng Chen ◽  
Lei Zhu ◽  
Ronghui Xu ◽  
Yang Liu ◽  
Xiaohui Yu ◽  
...  
Keyword(s):  
Hierarchical Structure ◽  
Hierarchical Structures ◽  
Category Representation ◽  
Semantic Relationships ◽  
The Hierarchical Structure ◽  
Sequential Information ◽  
Location Based Social Networks ◽  
Low Dimensional ◽  
The Given ◽  
Embedding Methods

Venue categories used in location-based social networks often exhibit a hierarchical structure, together with the category sequences derived from users’ check-ins. The two data modalities provide a wealth of information for us to capture the semantic relationships between those categories. To understand the venue semantics, existing methods usually embed venue categories into low-dimensional spaces by modeling the linear context (i.e., the positional neighbors of the given category) in check-in sequences. However, the hierarchical structure of venue categories, which inherently encodes the relationships between categories, is largely untapped. In this article, we propose a venue C ategory E mbedding M odel named Hier-CEM , which generates a latent representation for each venue category by embedding the Hier archical structure of categories and utilizing multiple types of context. Specifically, we investigate two kinds of hierarchical context based on any given venue category hierarchy and show how to model them together with the linear context collaboratively. We apply Hier-CEM to three tasks on two real check-in datasets collected from Foursquare. Experimental results show that Hier-CEM is better at capturing both semantic and sequential information inherent in venues than state-of-the-art embedding methods.

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