Deep Collaborative Filtering Approaches for Context-Aware Venue Recommendation

In recent years, vast amounts of user-generated data have being created on Location-Based Social Networks (LBSNs) such as Yelp and Foursquare. Making effective personalised venue suggestions to users based on their preferences and surrounding context is a challenging task. Context-Aware Venue Recommendation (CAVR) is an emerging topic that has gained a lot of attention from researchers, where context can be the user's current location for example. Matrix Factorisation (MF) is one of the most popular collaborative filtering-based techniques, which can be used to predict a user's rating on venues by exploiting explicit feedback (e.g. users' ratings on venues). However, such explicit feedback may not be available, particularly for inactive users, while implicit feedback is easier to obtain from LBSNs as it does not require the users to explicitly express their satisfaction with the venues. In addition, the MF-based approaches usually suffer from the sparsity problem where users/venues have very few rating, hindering the prediction accuracy. Although previous works on user-venue rating prediction have proposed to alleviate the sparsity problem by leveraging user-generated data such as social information from LBSNs, research that investigates the usefulness of Deep Neural Network algorithms (DNN) in alleviating the sparsity problem for CAVR remains untouched or partially studied

Context-aware multi-head self-attentional neural network model for next location prediction

Accurate activity location prediction is a crucial component of many mobility applications and is particularly required to develop personalized, sustainable transportation systems. Despite the widespread adoption of deep learning models, next location prediction models lack a comprehensive discussion and integration of mobility-related spatio-temporal contexts. Here, we utilize a multi-head self-attentional (MHSA) neural network that learns location transition patterns from historical location visits, their visit time and activity duration, as well as their surrounding land use functions, to infer an individual's next location. Specifically, we adopt point-of-interest data and latent Dirichlet allocation for representing locations' land use contexts at multiple spatial scales, generate embedding vectors of the spatio-temporal features, and learn to predict the next location with an MHSA network. Through experiments on two large-scale GNSS tracking datasets, we demonstrate that the proposed model outperforms other state-of-the-art prediction models, and reveal the contribution of various spatio-temporal contexts to the model's performance. Moreover, we find that the model trained on population data achieves higher prediction performance with fewer parameters than individual-level models due to learning from collective movement patterns. We also reveal mobility conducted in the recent past and one week before has the largest influence on the current prediction, showing that learning from a subset of the historical mobility is sufficient to obtain an accurate location prediction result. We believe that the proposed model is vital for context-aware mobility prediction. The gained insights will help to understand location prediction models and promote their implementation for mobility applications.Comment: updated Discussion section; accepted by Transportation Research Part

Indoor location prediction using multiple wireless received signal strengths

This paper presents a framework for indoor location prediction system using multiple wireless signals available freely in public or office spaces. We first propose an abstract architectural design for the system, outlining its key components and their functionalities. Different from existing works, such as robot indoor localization which requires as precise localization as possible, our work focuses on a higher grain: location prediction. Such a problem has a great implication in context-aware systems such as indoor navigation or smart self-managed mobile devices (e.g., battery management). Central to these systems is an effective method to perform location prediction under different constraints such as dealing with multiple wireless sources, effects of human body heats or mobility of the users. To this end, the second part of this pa- per presents a comparative and comprehensive study on different choices for modeling signals strengths and prediction methods under different condition settings. The results show that with simple, but effective modeling method, almost perfect prediction accuracy can be achieved in the static environment, and up to 85% in the presence of human movements. Finally, adopting the proposed framework we outline a fully developed system, named Marauder, that support user interface interaction and real-time voice-enabled location prediction.<br /

Context Aware Deep Learning for Brain Tumor Segmentation, Subtype Classification, and Survival Prediction Using Radiology Images

A brain tumor is an uncontrolled growth of cancerous cells in the brain. Accurate segmentation and classification of tumors are critical for subsequent prognosis and treatment planning. This work proposes context aware deep learning for brain tumor segmentation, subtype classification, and overall survival prediction using structural multimodal magnetic resonance images (mMRI). We first propose a 3D context aware deep learning, that considers uncertainty of tumor location in the radiology mMRI image sub-regions, to obtain tumor segmentation. We then apply a regular 3D convolutional neural network (CNN) on the tumor segments to achieve tumor subtype classification. Finally, we perform survival prediction using a hybrid method of deep learning and machine learning. To evaluate the performance, we apply the proposed methods to the Multimodal Brain Tumor Segmentation Challenge 2019 (BraTS 2019) dataset for tumor segmentation and overall survival prediction, and to the dataset of the Computational Precision Medicine Radiology-Pathology (CPM-RadPath) Challenge on Brain Tumor Classification 2019 for tumor classification. We also perform an extensive performance evaluation based on popular evaluation metrics, such as Dice score coefficient, Hausdorff distance at percentile 95 (HD95), classification accuracy, and mean square error. The results suggest that the proposed method offers robust tumor segmentation and survival prediction, respectively. Furthermore, the tumor classification results in this work is ranked at second place in the testing phase of the 2019 CPM-RadPath global challenge

Efficient prediction model management in mobile systems

With the advent of affordable mobile devices such as smartphones and tablets, the vision of Pervasive Computing has made a big step closer to becoming reality. In order to become truly ubiquitous and seamlessly integrate into everyday life, the design of context-aware applications is essential. Using contextual information obtained for example from the device's sensors such as motion sensors and gps receiver, context-aware applications can adapt their behavior depending on the environment the user is in. In some scenarios, context aware applications can also benefit from knowledge about future contexts. This necessitates the use of a context prediction model. We examine a social network scenario where in addition, the context in question is originally being acquired on another user's device. In this scenario, the prediction model could for example be used to predict the next location or activity of a friend. Prior to that, the prediction model needs to be distributed to and stored on the mobile device running the application. Both high transfer cost and limited space make it imperative to produce small prediction models which still predict the context considerably well. In this thesis, we examined methods to compress Markov-based prediction models of higher order in a lossless and lossy fashion and evaluated these methods on real world and generated data. Our evaluation showed clearly that the compression mechanisms introduced can be successfully applied to significantly reduce the size of the prediction models with only a minor impact on prediction performance

Deep Collaborative Filtering Approaches for Context-Aware Venue Recommendation

In recent years, vast amounts of user-generated data have being created on Location-Based Social Networks (LBSNs) such as Yelp and Foursquare. Making effective personalised venue suggestions to users based on their preferences and surrounding context is a challenging task. Context-Aware Venue Recommendation (CAVR) is an emerging topic that has gained a lot of attention from researchers, where context can be the user's current location for example. Matrix Factorisation (MF) is one of the most popular collaborative filtering-based techniques, which can be used to predict a user's rating on venues by exploiting explicit feedback (e.g. users' ratings on venues). However, such explicit feedback may not be available, particularly for inactive users, while implicit feedback is easier to obtain from LBSNs as it does not require the users to explicitly express their satisfaction with the venues. In addition, the MF-based approaches usually suffer from the sparsity problem where users/venues have very few rating, hindering the prediction accuracy. Although previous works on user-venue rating prediction have proposed to alleviate the sparsity problem by leveraging user-generated data such as social information from LBSNs, research that investigates the usefulness of Deep Neural Network algorithms (DNN) in alleviating the sparsity problem for CAVR remains untouched or partially studied

Topological spatial relations between a spatially extended point and a line for predicting movement in space

Location is an important dimension of contextual information for mobile systems, playing a key role in the development of context-aware and location-based applications. The identification of a specific location is well addressed by several existing technologies such as, for example, GPS (Global Positioning System). Moreover, the prediction of the next position of a mobile user is a valuable enabler for the development of pro-active location-based applications. Based on this knowledge, those applications become able to provide useful services for the users before they explicitly ask for them. As a step towards the prediction of the next position of a mobile user, this paper presents the identification of the topological spatial relations that can exist between a spatially extended point (representing the uncertainty on the position of a mobile user) and a line (representing objects in which movement in space is possible). Using a 4x3 intersection matrix we identified 38 topological spatial relations that can exist between the objects in analysis (spatially extended points and lines). The geometric realization of the 38 topological spatial relations was done through the analysis of each one of the identified valid matrices. The validation of the existence of the identified topological relations was verified from their geometric realization

Venue2Vec: An efficient embedding model for fine-grained user location prediction in geo-social networks

Geo-Social Networks (GSN) significantly improve location-aware capability of services by offering geo-located content based on the huge volumes of data generated in the GSN. The problem of user location prediction based on user-generated data in GSN has been extensively studied. However, existing studies are either concerning predicting users' next check-in location or predicting their future check-in location at a given time with coarse granularity. A unified model that can predict both scenarios with fine granularity is quite rare. Also, due to the heterogeneity of multiple factors associated with both locations and users, how to efficiently incorporate these information still remains challenging. Inspired by the recent success of word embedding in natural language processing, in this paper, we propose a novel embedding model called Venue2Vec which automatically incorporates temporal-spatial context, semantic information, and sequential relations for fine-grained user location prediction. Locations of the same type, and those that are geographically close or often visited successively by users will be situated closer within the embedding space. Based on our proposed Venue2Vec model, we design techniques that allow for predicting a user's next check-in location, and also their future check-in location at a given time. We conduct experiments on three real-world GSN datasets to verify the performance of the proposed model. Experimental results on both tasks show that Venue2Vec model outperforms several state-of-the-art models on various evaluation metrics. Furthermore, we show how the Venue2Vec model can be more time-efficient due to being parallelizable

Εξελιγμένες Τεχνικές Πρόβλεψης Θέσης στον Κινητό Υπολογισμό

Η επίγνωση-πλαισίου εμφανίζεται ως μία από τις πιο σημαντικές πτυχές στο αναδυόμενο περιβάλλον του διάχυτου υπολογισμού. Απαιτούνται κινητές εφαρμογές επίγνωσης πλαισίου για την αίσθηση και την αντίδραση σε συνθήκες μεταβαλλόμενου περιβάλλοντος. Τέτοιες εφαρμογές, συχνά, χρειάζεται να αναγνωρίζουν, να ταξινομούν και να προβλέπουν το πλαίσιο με σκοπό να δρουν αποδοτικά, εκ των προτέρων, προς όφελος του χρήστη. Πρώτον, προτείνουμε έναν αποδοτικό ταξινομητή χωρικού πλαισίου και έναν βραχείας-μνήμης προγνώστη για την μελλοντική θέση ενός κινητού χρήστη σε κυψελωτά δίκτυα. Δεύτερον, προτείνουμε έναν καινοτόμο προσαρμοστικό αλγόριθμο, ο οποίος χειρίζεται το πλαίσιο αναπαράστασης θέσης και την πρόβλεψη τροχιών των κινούμενων χρηστών. Τρίτον, προτείνουμε έναν βραχείας- μνήμης προσαρμοστικό προγνώστη θέσης που χειρίζεται την πρόβλεψη υπό την απουσία ιστορικής κινητής πληροφορίας. Τέταρτον, υποθέτουμε μία βάση προτύπων και προσπαθούμε να συγκρίνουμε το πρότυπο κίνησης ενός χρήστη με την αποθηκευμένη πληροφορία με σκοπό να προβλέψουμε μελλοντικές θέσεις. Τα συμπεράσματά μας, συγκρινόμενα με άλλα σχήματα, είναι πολύ ελπιδοφόρα για το πρόβλημα της πρόβλεψης θέσης.Context-awareness is viewed as one of the most important aspects in the emerging pervasive computing paradigm. Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify and predict context in order to act efficiently, beforehand, for the benefit of the user. Firstly, we propose an efficient spatial context classifier and a short-term predictor for the future location of a mobile user in cellular networks. Secondly, we propose a novel adaptive mobility prediction algorithm, which deals with location context representation and trajectory prediction of moving users. Thirdly, we propose a short-memory adaptive location predictor that realizes mobility prediction in the absence of extensive historical mobility information. Fourthly, we assume the existence of a pattern base and try to compare the movement pattern of a certain user with stored information in order to predict future locations. Our findings, compared with other schemes, are very promising for the location prediction problem and the adoption of proactive context-aware applications and services