Predicts user next location using CDR data.
problem Predicting user's next location using CDR data.
method Data processing framework with deep neural network model.
result Error of prediction decreased from 74% to 55%.
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48 results for “user location prediction”
Social networks are getting closer to our real physical world. People share the exact location and time of their check-ins and are influenced by their friends. Modeling the spatio-temporal behavior of users in social networks is of great importance for predicting the future behavior of users, controlling the users' mov…
Deep learning predicts user identity, activity, and location from Wi-Fi signals.
problem Privacy concerns and need for non-invasive user authentication, activity classification, and tracking.
method End-to-end deep learning framework using passive Wi-Fi signals.
result System autonomously predicts user identity, activity, and location without user intervention.
Calendar graph neural networks model user behavior with location and time data.
problem Modeling user behavior with location and time information for demographic prediction.
method Graph neural networks with a tripartite network of items, sessions, and locations, and a hierarchical calendar network.
result User embeddings preserve spatial and temporal patterns of various periodicity.
CMDRNN predicts user location using WiFi fingerprints with deep learning.
problem Predicting user activity with WiFi fingerprints is challenging due to high dimensionality.
method Combines CNN, RNN, and MDN to model high-dimensional time-series data.
result CMDRNN effectively predicts user location using WiFi fingerprints.
Which song will Smith listen to next? Which restaurant will Alice go to tomorrow? Which product will John click next? These applications have in common the prediction of user trajectories that are in a constant state of flux over a hidden network (e.g. website links, geographic location). What users are doing now may b…
The problem of predicting the location of users on large social networks like Twitter has emerged from real-life applications such as social unrest detection and online marketing. Twitter user geolocation is a difficult and active research topic with a vast literature. Most of the proposed methods follow either a conte…
Analyzing mobility behavior of users is extremely useful to create or improve existing services. Several research works have been done in order to study mobility behavior of users that mainly use users' significant locations. However, these existing analysis are extremely intrusive because they require the knowledge of…
Recently, how to expand data transmission to reduce cell data and repeated cell transmission has received more and more research attention. In mobile social networks, content popularity prediction has always been an important part of traffic offloading and expanding data dissemination. However, current mainstream conte…
This paper analyzes consumer choices over lunchtime restaurants using data from a sample of several thousand anonymous mobile phone users in the San Francisco Bay Area. The data is used to identify users' approximate typical morning location, as well as their choices of lunchtime restaurants. We build a model where res…
Named-entity recognition (NER) aims at identifying entities of interest in a text. Artificial neural networks (ANNs) have recently been shown to outperform existing NER systems. However, ANNs remain challenging to use for non-expert users. In this paper, we present NeuroNER, an easy-to-use named-entity recognition tool…
Mobile context determination is an important step for many context aware services such as location-based services, enterprise policy enforcement, building or room occupancy detection for power or HVAC operation, etc. Especially in enterprise scenarios where policies (e.g., attending a confidential meeting only when the…
Study develops a new model for predicting individual mobility based on activity patterns.
problem Existing mobility prediction methods overlook underlying activity patterns.
method Activity-based hidden Markov model (IOHMM) using transit smart card data.
result Proposed model achieves similar prediction performance to LSTM but also provides behavioral insights.
In this paper we propose a new method to predict the final destination of vehicle trips based on their initial partial trajectories. We first review how we obtained clustering of trajectories that describes user behaviour. Then, we explain how we model main traffic flow patterns by a mixture of 2d Gaussian distribution…
Proposes a privacy framework for location traces under conditional priors.
problem Challenges in protecting privacy for location-based services with multiple points.
method Rényi differential privacy framework for conditionally dependent data.
result Achieves privacy within a fixed radius for every user location in a trace.
Paper tackles online facility location with user requests and provides a quasi-linear time algorithm.
problem Online facility location problem with user requests and site selection costs.
method Design of a novel quasi-linear time algorithm based on the Hedge algorithm.
result Good theoretical guarantees on the performance of the novel algorithm.
We present and test a sequential learning algorithm for the short-term prediction of human mobility. This novel approach pairs the Exponential Weights forecaster with a very large ensemble of experts. The experts are individual sequence prediction algorithms constructed from the mobility traces of 10 million roaming mo…
Two deep learning models improve indoor location prediction from WiFi fingerprints.
problem Indoor location prediction from WiFi fingerprints.
method Convolutional mixture density recurrent neural network and VAE-based semi-supervised learning model.
result Proposed models outperform existing methods in real-world datasets.
Twitter is recently being used during crises to communicate with officials and provide rescue and relief operation in real time. The geographical location information of the event, as well as users, are vitally important in such scenarios. The identification of geographic location is one of the challenging tasks as the…
We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene. In contrast to previous work that relies on specialized image capture, user input, and/or simple scene models, we train an end-to-end deep neural network that di…
A new method for recommending groups and activities based on geo-social data.
problem Recommendation of groups and activities based on geo-social data with social constraints.
method Proposes an attentive geo-social group recommendation method with an attention mechanism and a spatial query algorithm.
result Significantly outperforms baseline methods in real-world datasets.
Click-through rate (CTR) is a key signal of relevance for search engine results, both organic and sponsored. CTR of a result has two core components: (a) the probability of examination of a result by a user, and (b) the perceived relevance of the result given that it has been examined by the user. There has been consid…
Paper presents a simple method for accurate user localization in urban areas.
problem Poor GPS performance in dense urban environments.
method Localization based on pathloss using base station signal strengths and RadioUNet approximations.
result Accurate user location extraction in urban environments.
We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carried multi-purpose sensors. Hypotheses with respect to each type of measurements are verified, including temperature, humidity, and light lev…
The first step of a secure communication is authenticating legible users and detecting the malicious ones. In the last recent years, some promising schemes proposed using wireless medium network's features, in particular, channel state information (CSI) as a means for authentication. These schemes mainly compare user's…
Model uses LLMs to process numerical data guided by natural language descriptions.
problem Challenges in integrating prior knowledge into probabilistic models.
method Developed LLM Processes to condition numerical predictive distributions on natural language.
result Improved predictive performance and structured qualitative descriptions.
A Federated Data Fusion-Based Prognostic Model for Applications with Multi-Stream Incomplete Signalsstat.ML
A federated model predicts failures using multi-stream incomplete data.
problem Insufficient data for reliable prognostic models in multi-stream applications.
method Federated data fusion, multivariate functional principal component analysis, (log)-location-scale regression model, federated algorithm.
result Performance is as good as classic non-federated models and better than individual models.
With the availability of vast amounts of user visitation history on location-based social networks (LBSN), the problem of Point-of-Interest (POI) prediction has been extensively studied. However, much of the research has been conducted solely on voluntary checkin datasets collected from social apps such as Foursquare o…
Deriving the Traveler Behavior Information from Social Media: A Case Study in Manhattan with Twittercs.SI
This study uses Twitter to analyze traveler behavior in Manhattan.
problem Analyzing traveler behavior using social media data.
method Systematic method to extract displacement information from geo-tagged tweets.
result Twitter reveals unique demographics and travel behavior patterns.
With the growth of user-generated content, we observe the constant rise of the number of companies, such as search engines, content aggregators, etc., that operate with tremendous amounts of web content not being the services hosting it. Thus, aiming to locate the most important content and promote it to the users, the…
Predicts destinations and routes from partial trajectory data.
problem Predicting destinations and routes from partial trajectory data for applications like parking suggestions and ride-sharing.
method Three-step procedure: k-d tree-based space discretization, recurrent neural network for destination prediction, and route calculation.
result Best models predict destinations with a mean error of 1.3 km and 1.43 km.
This review covers predictive uncertainty estimation in machine learning.
problem Improving the communication of uncertainty in machine learning predictions.
method A comprehensive review of probabilistic prediction methods from early statistical models to recent machine learning algorithms.
result The review highlights the importance of consistent scoring functions and proper scoring rules for assessing probabilistic predictions.
Federated Learning prioritizes client data contributions for better model quality.
problem Privacy concerns and reluctance to share private data in machine learning.
method Prioritizes client data contributions in Federated Learning by assigning scores based on defined criteria.
result The proposed approach yields a higher quality global model compared to standard Federated Learning.
Study predicts climate data at distant locations using machine learning.
problem Predict climate variables at distant locations where comprehensive data collection is not feasible.
method Uses reservoir computing and vector autoregression models for prediction.
result Machine learning improves prediction accuracy for highly correlated data.
There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility, communication and mobile phone usage patterns for quantifying individuals' mood and wellbei…
The recent adoption of recurrent neural networks (RNNs) for session modeling has yielded substantial performance gains compared to previous approaches. In terms of context-aware session modeling, however, the existing RNN-based models are limited in that they are not designed to explicitly model rich static user-side c…
πBO augments BO with user beliefs for better hyperparameter optimization.
problem BO ignores user beliefs, reducing its appeal to practitioners.
method Proposes BO, an acquisition function that incorporates user-provided prior beliefs.
result BO outperforms competing approaches and deep learning tasks.
MPE models traffic trajectory data to predict next locations.
problem Predicting next locations from traffic trajectory data.
method Mobility pattern embedding model MPE.
result MPE significantly outperforms state-of-the-art methods in next location prediction.
Navigating complex urban environments safely is a key to realize fully autonomous systems. Predicting future locations of vulnerable road users, such as pedestrians and cyclists, thus, has received a lot of attention in the recent years. While previous works have addressed modeling interactions with the static (obstacl…
A new method detects changes in multivariate data using random forests.
problem Detecting changes in multivariate data.
method A computationally feasible search method using random forests and class probability predictions.
result Consistently locates change points in simulations.
Building spatiotemporal activity models for people's activities in urban spaces is important for understanding the ever-increasing complexity of urban dynamics. With the emergence of Geo-Tagged Social Media (GTSM) records, previous studies demonstrate the potential of GTSM records for spatiotemporal activity modeling. …
How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rankcs.LG
FPL allows users to control their data in federated top-N recommendation.
problem Data privacy in recommendation services.
method Federated Learning with learning to rank optimization.
result Users can control the amount of sensitive data shared with a central server.
We propose channel charting (CC), a novel framework in which a multi-antenna network element learns a chart of the radio geometry in its surrounding area. The channel chart captures the local spatial geometry of the area so that points that are close in space will also be close in the channel chart and vice versa. CC w…
Users form information trails as they browse the web, checkin with a geolocation, rate items, or consume media. A common problem is to predict what a user might do next for the purposes of guidance, recommendation, or prefetching. First-order and higher-order Markov chains have been widely used methods to study such se…
Proposes AtCoR for predicting bike station usage, improving station network reconfiguration.
problem Challenges in predicting new bike stations due to lack of historical data.
method AtCoR algorithm that predicts both existing and new bike stations using station-centered heatmaps and historical correlations.
result AtCoR outperforms existing models in predicting bike station usage.
TIMeSynC combines financial service interactions for intent prediction.
problem Aligning and learning from multi-domain, multi-resolution sequences for accurate intent prediction.
method An encoder-decoder transformer model addressing sequence alignment, temporal dynamics, and dynamic/static sequence combination.
result Significant improvement in intent prediction over existing methods.
Paper proposes a graph neural network for accurate long-term ILI prediction.
problem Limited long-term prediction performance and spatio-temporal dependency in existing models.
method Cross-location attention based graph neural network (Cola-GNN) for time series embeddings and location aware attentions.
result Proposed method shows strong predictive performance and interpretable results for long-term epidemic predictions.
Collaborative filtering analyzes user preferences for items (e.g., books, movies, restaurants, academic papers) by exploiting the similarity patterns across users. In implicit feedback settings, all the items, including the ones that a user did not consume, are taken into consideration. But this assumption does not acc…