Anomaly detection in spatiotemporal data via regularized non-negative tensor analysis

Anomaly detection in multidimensional data is a challenging task. Detecting anomalous mobility patterns in a city needs to take spatial, temporal, and traffic information into consideration. Although existing techniques are able to extract spatiotemporal features for anomaly analysis, few systematic analysis about how different factors contribute to or affect the anomalous patterns has been proposed. In this paper, we propose a novel technique to localize spatiotemporal anomalous events based on tensor decomposition. The proposed method employs a spatial-feature-temporal tensor model and analyzes latent mobility patterns through unsupervised learning. We first train the model based on historical data and then use the model to capture the anomalies, i.e., the mobility patterns that are significantly different from the normal patterns. The proposed technique is evaluated based on the yellow-cab dataset collected from New York City. The results show several interesting latent mobility patterns and traffic anomalies that can be deemed as anomalous events in the city, suggesting the effectiveness of the proposed anomaly detection method

Applying reranking strategies to route recommendation using sequence-aware evaluation

Venue recommendation approaches have become particularly useful nowadays due to the increasing number of users registered in location-based social networks (LBSNs), applications where it is possible to share the venues someone has visited and establish connections with other users in the system. Besides, the venue recommendation problem has certain characteristics that differ from traditional recommendation, and it can also benefit from other contextual aspects to not only recommend independent venues, but complete routes or venue sequences of related locations. Hence, in this paper, we investigate the problem of route recommendation under the perspective of generating a sequence of meaningful locations for the users, by analyzing both their personal interests and the intrinsic relationships between the venues. We divide this problem into three stages, proposing general solutions to each case: First, we state a general methodology to derive user routes from LBSNs datasets that can be applied in as many scenarios as possible; second, we define a reranking framework that generate sequences of items from recommendation lists using different techniques; and third, we propose an evaluation metric that captures both accuracy and sequentiality at the same time. We report our experiments on several LBSNs datasets and by means of different recommendation quality metrics and algorithms. As a result, we have found that classical recommender systems are comparable to specifically tailored algorithms for this task, although exploiting the temporal dimension, in general, helps on improving the performance of these techniques; additionally, the proposed reranking strategies show promising results in terms of finding a trade-off between relevance, sequentiality, and distance, essential dimensions in both venue and route recommendation tasksThis work has been funded by the Ministerio de Ciencia, Innovación y Universidades (reference: TIN2016-80630-P) and by the European Social Fund (ESF), within the 2017 call for predoctoral contract

Spotting misbehaviors in location-based social networks using tensors

The proliferation of mobile devices that are capable of estimating their position, has lead to the emergence of a new class of social networks, namely location-based social networks (LBSNs for short). The main interaction between users in an LBSN is location sharing. While the latter can be realized through continuous tracking of a user's whereabouts from the service provider, the majority of LBSNs allow users to voluntarily share their location, through check-ins. LBSNs provide incentives to users to perform check-ins. However, these incentives can also lead to people faking their location, thus, generating false information. In this work, we propose the use of tensor decomposition for spotting anomalies in the check-in behavior of users. To the best of our knowledge, this is the first attempt to model this problem using tensor analysis