Location-Aware Group Preference Queries in Social-Networks

2018 ◽  
pp. 53-67 ◽  
Author(s):  
Ammar Sohail ◽  
Arif Hidayat ◽  
Muhammad Aamir Cheema ◽  
David Taniar
Keyword(s):  
Social Networks ◽  
Group Preference ◽  
Location Aware ◽  
Preference Queries ◽  
Aware Group

Efficient Processing of Spatial Group Preference Queries

2019 ◽  
pp. 642-659
Author(s):  
Zhou Zhang ◽  
Peiquan Jin ◽  
Yuan Tian ◽  
Shouhong Wan ◽  
Lihua Yue
Keyword(s):  
Group Preference ◽  
Preference Queries ◽  
Efficient Processing

scholarly journals A User Proprietary Obfuscate System for Positions Sharing in Location-Aware Social Networks

2015 ◽  
Vol 03 (05) ◽  
pp. 7-20 ◽  
Author(s):  
Wei Cherng Cheng ◽  
Masayoshi Aritsugi
Keyword(s):  
Social Networks ◽  
Location Aware

Like a Poke on Facebook Emergent Semantics in Location-Aware Social Network Services

2011 ◽  
pp. 909-924
Author(s):  
Anders Kofod-Petersen ◽  
Rebekah Wegener
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Networking ◽  
Systemic Functional Linguistics ◽  
Network Services ◽  
Social Networking Services ◽  
Location Aware ◽  
Social Network Services ◽  
Emergent Semantics ◽  
Iconic Signs

Location-aware social network services are set to be the next generation of social networking services. These services typically allow users to send and receive messages and icons. Iconic signs, which look like what they represent, may be said to have a commonly understood meaning attached to them. However, this is fluid, leaving them open to variation in meaning. More precise meanings are free to emerge within specific contexts and within particular social networks. Within this chapter the authors explore the semantics that emerge for three icons used within a location-aware social network service. Using Systemic Functional Linguistics (SFL), focus is given to the dominant speech function attached to each icon and the resultant meanings that emerge within social networks of the systems users. This study allows the authors to better understand how users interact with each other in smart spaces and utilise location information in social network services. By understanding how icons are used to engage others and how the meanings attached to these icons develop, the authors are better placed to create systems that fit naturally and beneficially into the users’ context.

Like a Poke on Facebook Emergent Semantics in Location-Aware Social Network Services

2010 ◽  
pp. 497-512 ◽  
Author(s):  
Anders Kofod-Petersen ◽  
Rebekah Wegener
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Networking ◽  
Systemic Functional Linguistics ◽  
Network Services ◽  
Social Networking Services ◽  
Location Aware ◽  
Social Network Services ◽  
Emergent Semantics ◽  
Iconic Signs

Location-aware social network services are set to be the next generation of social networking services. These services typically allow users to send and receive messages and icons. Iconic signs, which look like what they represent, may be said to have a commonly understood meaning attached to them. However, this is fluid, leaving them open to variation in meaning. More precise meanings are free to emerge within specific contexts and within particular social networks. Within this chapter the authors explore the semantics that emerge for three icons used within a location-aware social network service. Using Systemic Functional Linguistics (SFL), focus is given to the dominant speech function attached to each icon and the resultant meanings that emerge within social networks of the systems users. This study allows the authors to better understand how users interact with each other in smart spaces and utilise location information in social network services. By understanding how icons are used to engage others and how the meanings attached to these icons develop, the authors are better placed to create systems that fit naturally and beneficially into the users’ context.

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 Context-Aware Group-Oriented Location Recommendation in Location-Based Social Networks

2019 ◽  
Vol 8 (9) ◽  
pp. 406 ◽  
Author(s):  
Khazaei ◽  
Alimohammadi
Keyword(s):  
Social Networks ◽  
Random Walk ◽  
Spatial Proximity ◽  
Context Aware ◽  
Human Beings ◽  
Location Recommendation ◽  
Aware Group ◽  
Location Based Social Networks ◽  
Group Profile ◽  
User Location

Location-based social networking services have attracted great interest with the growth of smart mobile devices. Recommending locations for users based on their preferences is an important task for location-based social networks (LBSNs). Since human beings are social by nature, group activities are important in individuals’ lives. Due to the different interests and priorities of individuals, it is difficult to find places that are ideal for all members of a group. In this study, a context-aware group-oriented location recommendation system is proposed based on a random walk algorithm. The proposed approach considers three different contexts, namely users’ contexts (i.e., social relationships, personal preferences), location context (i.e., category, popularity, capacity, and spatial proximity), and environmental context (i.e., weather, day of the week). Three graph models of LBSNs are constructed to perform a random walk with restart (RWR) algorithm in which a user-location graph is considered as the basis. In addition, two group recommendation strategies are used. One is an aggregated prediction strategy, and the other is derived from extending the RWR to the group. After performing the RWR algorithm, the group profile and location popularity are used to improve the effectiveness of the recommendation. The performance of the proposed system is examined using the Gowalla dataset related to the city of London from March 2009 to July 2011. The results indicate that the RWR algorithm outperforms popularity-based, collaborative filtering and content-based filtering. In addition, using the group profile and location popularity significantly improves the accuracy of recommendation. On the user-location graph, the number of users with recommendations matching the test data increases by 1.18 times, while the precision of creating relevant recommendations is increased by 3.4 times.

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