Situational Recommender: Are You On the Spot, Refining Plans, or Just Bored?

Viswanathan, Sruthi; Boulard, Cécile; Bruyat, Adrien; Grasso, Antonietta

doi:10.1145/3491102.3501909

Cited by 4 publications

(1 citation statement)

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“…Butron-Revilla et al address the detection of mobility patterns (and PoIs) based on mobile phone data [8]. Viswanathan et al focus on situational awareness to define the nature of PoIs (stopovers, specific interest in an area, or occasional stop) [9]. The focus of the authors is on situational awareness and not on semi-automated outlier detection.…”

Section: Related Workmentioning

confidence: 99%

An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories

Pereira

Sofia

2022

Future Internet

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This paper provides an analysis of two machine learning algorithms, density-based spatial clustering of applications with noise (DBSCAN) and the local outlier factor (LOF), applied in the detection of outliers in the context of a continuous framework for the detection of points of interest (PoI). This framework has as input mobile trajectories of users that are continuously fed to the framework in close to real time. Such frameworks are today still in their infancy and highly required in large-scale sensing deployments, e.g., Smart City planning deployments, where individual anonymous trajectories of mobile users can be useful to better develop urban planning. The paper’s contributions are twofold. Firstly, the paper provides the functional design for the overall PoI detection framework. Secondly, the paper analyses the performance of DBSCAN and LOF for outlier detection considering two different datasets, a dense and large dataset with over 170 mobile phone-based trajectories and a smaller and sparser dataset, involving 3 users and 36 trajectories. Results achieved show that LOF exhibits the best performance across the different datasets, thus showing better suitability for outlier detection in the context of frameworks that perform PoI detection in close to real time.

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Section: Related Workmentioning

confidence: 99%