Results 41 to 50 of about 283 (172)
Inferring Location Types With Geo-Social-Temporal Pattern Mining
With a rapid growth in the global population, the modern world is undergoing a rapid expansion of residential areas, especially in urban centres. This continuously demands for increased general services and basic amenities, which are required according ...
Tarique Anwar +5 more
doaj +1 more source
Blockchain-Enabled Privacy-Preserving Location Sharing Scheme for LBSNs [PDF]
The rise of Internet of Things (IoT) technology promotes the rapid development of location services industry. The idea of smart connectivity also provides a new direction for Location-Based Social Networks (LBSNs). However, due to limited calculate ability and internal storage space of IoT devices, historical location data of users is generally stored ...
Liang Zhu +4 more
openaire +1 more source
Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data
With rapid advancement in location-based services (LBS), their acquisition has become a powerful tool to link people with similar interests across long distances, as well as connecting family and friends.
Muhammad Rizwan, Wanggen Wan
doaj +1 more source
Due to its relatively high availability and low cost, location-based social network (LBSN) (e.g., Foursquare) data (a popular type of volunteered geographic information) seem to be an alternative or complement to survey data in the study of travel ...
Yeran Sun, Ming Li
doaj +1 more source
Location-based social networks (LBSN) allow users to socialize with friends by sharing their daily life experiences online. In particular, a large amount of check-ins data generated by LBSNs capture the visit locations of users and open a new line of ...
Shuqiang Xu, Qunying Huang, Zhiqiang Zou
doaj +1 more source
A study of neighbour selection strategies for POI recommendation in LBSNs [PDF]
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.
Carlos Rios +2 more
openaire +2 more sources
Location Recommendation System based on LBSNS
In LBSNS(Location-based Social Network Service), users can share locations and communicate with others by using check-in data. The check-in data consists of POI name, category, coordinate and address of locations, nickname of users, evaluating grade of locations, related article/photo/video, and etc.
Ku-Imm Jung +3 more
openaire +2 more sources
A Self-Attention Model for Next Location Prediction Based on Semantic Mining
With the rise in the Internet of Things (IOT), mobile devices and Location-Based Social Network (LBSN), abundant trajectory data have made research on location prediction more popular.
Eric Hsueh-Chan Lu, You-Ru Lin
doaj +1 more source
Due to the rapid growth of online and technology development, every people have an own mobile to search their doubts and easily find it out within a second. Hypertext induced topic search (HITS) is a link analyzed algorithm utilized to find reliable websites that satisfy broad‐topic queries.
John Vaseekaran S. +2 more
wiley +1 more source
Customized Tip Summarization Based on Customer Preferences in Online Social Networks
There has been a serious issue with overload of data because of the abundance of consumer‐generated suggestions on location‐based social networks (LBSNs). The amount and fragmented nature of all these brief comments make it hard for users to efficiently gather relevant details customized to their interests, even when they provide useful, current data ...
Muhmmad Al-Khiza’ay +5 more
wiley +1 more source

