scholarly journals User Modeling for Point-of-Interest Recommendations in Location-Based Social Networks: The State of the Art

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Shudong Liu
Keyword(s):  
Social Networks ◽  
User Modeling ◽  
Real Life ◽  
Location Based Services ◽  
Geographical Information ◽  
Daily Movement ◽  
Spatiotemporal Information ◽  
Point Of Interest ◽  
Future Challenges ◽  
Location Based Social Networks

The rapid growth of location-based services (LBSs) has greatly enriched people’s urban lives and attracted millions of users in recent years. Location-based social networks (LBSNs) allow users to check-in at a physical location and share daily tips on points of interest (POIs) with their friends anytime and anywhere. Such a check-in behavior can make daily real-life experiences spread quickly through the Internet. Moreover, such check-in data in LBSNs can be fully exploited to understand the basic laws of humans’ daily movement and mobility. This paper focuses on reviewing the taxonomy of user modeling for POI recommendations through the data analysis of LBSNs. First, we briefly introduce the structure and data characteristics of LBSNs, and then we present a formalization of user modeling for POI recommendations in LBSNs. Depending on which type of LBSNs data was fully utilized in user modeling approaches for POI recommendations, we divide user modeling algorithms into four categories: pure check-in data-based user modeling, geographical information-based user modeling, spatiotemporal information-based user modeling, and geosocial information-based user modeling. Finally, summarizing the existing works, we point out the future challenges and new directions in five possible aspects.

scholarly journals A Geographical Factor of Interest Recommended Strategies in Location Based Social Networks

2018 ◽  
Vol 7 (3.27) ◽  
pp. 32
Author(s):  
Bulusu Rama ◽  
K Sai Prasad ◽  
Ayesha Sultana ◽  
K Shekar
Keyword(s):  
User Modeling ◽  
Life Experiences ◽  
Real Life ◽  
Geographical Information ◽  
Huge Number ◽  
Fast Development ◽  
New Guidelines ◽  
Location Based Social Networks ◽  
Legal Guidelines

The fast development of area based administrations (LBSNs) has extensively advanced individuals' city lives and pulled in a huge number of recent years. Area based informal organizations (LBSNs) allow clients to registration at a real region and offer step by step rules on purposes of-intrigue (POIs) with their pals each time and anyplace. Such check-in behavior can make daily real-life experiences spread rapidly via the Internet. Moreover, such check-in records in LBSNs can be totally exploited to understand the basic legal guidelines of humans’ every day motion and mobility. This paper centers on evaluating the scientific classification of client displaying for POI proposals through the information investigation of LBSNs. First, we quickly introduce the shape and records traits of LBSNs, then we current a formalization of user modeling for POI suggestions in LBSNs. Contingent upon which sort of LBSNs records used to be completely used in buyer displaying forms for POI proposals, we separate client demonstrating calculations into four classifications: pure check-in data-based consumer modeling, geographical information-based consumer modeling, spatial-temporal information-based consumer modeling, and geo-social information-based consumer modeling. At finally, condensing the current works, we bring up the future difficulties and new guidelines in five possible aspects  

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.

scholarly journals A study of neighbour selection strategies for POI recommendation in LBSNs

2018 ◽  
Vol 44 (6) ◽  
pp. 802-817 ◽  
Author(s):  
Carlos Rios ◽  
Silvia Schiaffino ◽  
Daniela Godoy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Location Based Services ◽  
Geographical Information ◽  
Good Prediction ◽  
General Terms ◽  
The Impact ◽  
Filtering Approach

Location-based recommender systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of like-minded people, so-called neighbours, for prediction. Thus, an adequate selection of such neighbours becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbours in the context of a collaborative filtering–based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighbourhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from location-based social networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbours based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area and to recommender system developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs.

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.

Sign in / Sign up

Export Citation Format

Share Document