Search Results for author:
Found 9 papers, 5 papers with code
Automated classification of pre-defined movement patterns: A comparison between GNSS and UWB technology
However, to date, few studies have investigated the performance of different localisation systems regarding the classification of human movement patterns in small areas.
A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation
The major contributions of this paper are: (i) providing an extensive survey of context-aware location recommendation (ii) quantifying and analyzing the impact of different contextual information (e. g., social, temporal, spatial, and categorical) in the POI recommendation on available baselines and two new linear and non-linear models, that can incorporate all the major contextual information into a single recommendation model, and (iii) evaluating the considered models using two well-known real-world datasets.
Exploring the Impact of Noise on Hybrid Inversion of PROSAIL RTM on Sentinel-2 Data
We focused on synthetic Sentinel-2 (S2) data generated using the PROSAIL RTM and four commonly applied ML algorithms: Gaussian Processes (GPR), Random Forests (RFR), and Artificial Neural Networks (ANN) and Multi-task Neural Networks (MTN).
Unsupervised Discretization by Two-dimensional MDL-based Histogram
As the flexibility of our model class comes at the cost of a vast search space, we introduce a heuristic algorithm, named PALM, which Partitions each dimension ALternately and then Merges neighboring regions, all using the MDL principle.
Metaheuristic macro scale traffic flow optimisation from urban movement data
This allows us to pinpoint roads that occur in many routes and are thus sensitive to congestion.
Physics and Society Multiagent Systems I.2.1; I.5.4
Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation
Previous studies show that incorporating contextual information such as geographical and temporal influences is necessary to improve POI recommendation by addressing the data sparsity problem.
LGLMF: Local Geographical based Logistic Matrix Factorization Model for POI Recommendation
To address these problems, a POI recommendation method is proposed in this paper based on a Local Geographical Model, which considers both users' and locations' points of view.
Category-Aware Location Embedding for Point-of-Interest Recommendation
With the recent advances of neural models, much work has sought to leverage neural networks to learn neural embeddings in a pre-training phase that achieve an improved representation of POIs and consequently a better recommendation.
Spaceprint: a Mobility-based Fingerprinting Scheme for Public Spaces
In this paper, we address the problem of how automated situation-awareness can be achieved by learning real-world situations from ubiquitously generated mobility data.
