1,003 research outputs found
Trajectory-User Linking via Hierarchical Spatio-Temporal Attention Networks
Trajectory-User Linking (TUL) is crucial for human mobility modeling by
linking diferent trajectories to users with the exploration of complex mobility
patterns. Existing works mainly rely on the recurrent neural framework to
encode the temporal dependencies in trajectories, have fall short in capturing
spatial-temporal global context for TUL prediction. To ill this gap, this work
presents a new hierarchical spatio-temporal attention neural network, called
AttnTUL, to jointly encode the local trajectory transitional patterns and
global spatial dependencies for TUL. Speciically, our irst model component is
built over the graph neural architecture to preserve the local and global
context and enhance the representation paradigm of geographical regions and
user trajectories. Additionally, a hierarchically structured attention network
is designed to simultaneously encode the intra-trajectory and inter-trajectory
dependencies, with the integration of the temporal attention mechanism and
global elastic attentional encoder. Extensive experiments demonstrate the
superiority of our AttnTUL method as compared to state-of-the-art baselines on
various trajectory datasets. The source code of our model is available at
https://github.com/Onedean/AttnTUL.Comment: 22 pages, 8 figures, accepted by ACM Trans. Knowl. Discov. Data
Journal (TKDD
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
Colossal Trajectory Mining: A unifying approach to mine behavioral mobility patterns
Spatio-temporal mobility patterns are at the core of strategic applications such as urban planning and monitoring. Depending on the strength of spatio-temporal constraints, different mobility patterns can be defined. While existing approaches work well in the extraction of groups of objects sharing fine-grained paths, the huge volume of large-scale data asks for coarse-grained solutions. In this paper, we introduce Colossal Trajectory Mining (CTM) to efficiently extract heterogeneous mobility patterns out of a multidimensional space that, along with space and time dimensions, can consider additional trajectory features (e.g., means of transport or activity) to characterize behavioral mobility patterns. The algorithm is natively designed in a distributed fashion, and the experimental evaluation shows its scalability with respect to the involved features and the cardinality of the trajectory dataset
DP-LTOD: Differential Privacy Latent Trajectory Community Discovering Services over Location-Based Social Networks
IEEE Community detection for Location-based Social Networks (LBSNs) has been received great attention mainly in the field of large-scale Wireless Communication Networks. In this paper, we present a Differential Privacy Latent Trajectory cOmmunity Discovering (DP-LTOD) scheme, which obfuscates original trajectory sequences into differential privacy-guaranteed trajectory sequences for trajectory privacy-preserving, and discovers latent trajectory communities through clustering the uploaded trajectory sequences. Different with traditional trajectory privacy-preserving methods, we first partition original trajectory sequence into different segments. Then, the suitable locations and segments are selected to constitute obfuscated trajectory sequence. Specifically, we formulate the trajectory obfuscation problem to select an optimal trajectory sequence which has the smallest difference with original trajectory sequence. In order to prevent privacy leakage, we add Laplace noise and exponential noise to the outputs during the stages of location obfuscation matrix generation and trajectory sequence function generation, respectively. Through formal privacy analysis,we prove that DP-LTOD scheme can guarantee \epsilon-differential private. Moreover, we develop a trajectory clustering algorithm to classify the trajectories into different kinds of clusters according to semantic distance and geographical distance. Extensive experiments on two real-world datasets illustrate that our DP-LTOD scheme can not only discover latent trajectory communities, but also protect user privacy from leaking
Hidden location prediction using check-in patterns in location based social networks
Check-in facility in a Location Based Social Network (LBSN) enables people to share location information as well as real life activities. Analysing these historical series of check-ins to predict the future locations to be visited has been very popular in the research community. However, it has been found that people do not intend to share the privately visited locations and activities in a LBSN.
Research into extrapolating unchecked locations from historical data is limited.
Knowledge of hidden locations can have a wide range of benefits to society. It may help the investigating agencies in identifying possible places visited by a suspect, a marketing company in selecting potential customers for targeted marketing, for medical representatives in identifying areas for disease prevention and containment, etc. In this paper, we propose an Associative Location Prediction Model (ALPM), which infers privately visited unchecked locations from a published user
trajectory. The proposed ALPM explores the association between a user's checked-in data, the Hidden Markov Model and proximal locations around a published check-in for predicting the unchecked or hidden locations. We evaluate ALPM on real-world Gowalla LBSN dataset for the users residing in Beijing, China. Experimental results show that the proposed model outperforms the existing state of the
art work in literature
Reliable Collaborative Filtering on Spatio-Temporal Privacy Data
Lots of multilayer information, such as the spatio-temporal privacy check-in data, is accumulated in the location-based social network (LBSN). When using the collaborative filtering algorithm for LBSN location recommendation, one of the core issues is how to improve recommendation performance by combining the traditional algorithm with the multilayer information. The existing approaches of collaborative filtering use only the sparse user-item rating matrix. It entails high computational complexity and inaccurate results. A novel collaborative filtering-based location recommendation algorithm called LGP-CF, which takes spatio-temporal privacy information into account, is proposed in this paper. By mining the users check-in behavior pattern, the dataset is segmented semantically to reduce the data size that needs to be computed. Then the clustering algorithm is used to obtain and narrow the set of similar users. User-location bipartite graph is modeled using the filtered similar user set. Then LGP-CF can quickly locate the location and trajectory of users through message propagation and aggregation over the graph. Through calculating users similarity by spatio-temporal privacy data on the graph, we can finally calculate the rating of recommendable locations. Experiments results on the physical clusters indicate that compared with the existing algorithms, the proposed LGP-CF algorithm can make recommendations more accurately
Recommended from our members
The National Transport Data Framework
Report by Professor Peter Landshoff (Cambridge University) and
Professor John Polak (Imperial College London) on a project for
the Department for Transport.
emails: [email protected] [email protected] NTDF is designed to be a resource for data owners to deposit descriptions
into a central catalogue, so that people can search for data and find data
and understand their characteristics. The value of this is to individuals, to
commercial organizations, and to public bodies. For example, services that
provide better information to travellers will help to make their journey
less stressful and persuade them to make more use of public transport.
Transport operators need very diverse information to help them
plan developments to their services: demographic, geographical, economic etc.
And policy makers need a similar range of information to help them decide
how to divide their budget and afterwards to evaluate how valuable it has
been.This work was supported by the Department for Transport (DfT)
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