39,445 research outputs found
Anticipating Information Needs Based on Check-in Activity
In this work we address the development of a smart personal assistant that is
capable of anticipating a user's information needs based on a novel type of
context: the person's activity inferred from her check-in records on a
location-based social network. Our main contribution is a method that
translates a check-in activity into an information need, which is in turn
addressed with an appropriate information card. This task is challenging
because of the large number of possible activities and related information
needs, which need to be addressed in a mobile dashboard that is limited in
size. Our approach considers each possible activity that might follow after the
last (and already finished) activity, and selects the top information cards
such that they maximize the likelihood of satisfying the user's information
needs for all possible future scenarios. The proposed models also incorporate
knowledge about the temporal dynamics of information needs. Using a combination
of historical check-in data and manual assessments collected via crowdsourcing,
we show experimentally the effectiveness of our approach.Comment: Proceedings of the 10th ACM International Conference on Web Search
and Data Mining (WSDM '17), 201
Modeling Taxi Drivers' Behaviour for the Next Destination Prediction
In this paper, we study how to model taxi drivers' behaviour and geographical
information for an interesting and challenging task: the next destination
prediction in a taxi journey. Predicting the next location is a well studied
problem in human mobility, which finds several applications in real-world
scenarios, from optimizing the efficiency of electronic dispatching systems to
predicting and reducing the traffic jam. This task is normally modeled as a
multiclass classification problem, where the goal is to select, among a set of
already known locations, the next taxi destination. We present a Recurrent
Neural Network (RNN) approach that models the taxi drivers' behaviour and
encodes the semantics of visited locations by using geographical information
from Location-Based Social Networks (LBSNs). In particular, RNNs are trained to
predict the exact coordinates of the next destination, overcoming the problem
of producing, in output, a limited set of locations, seen during the training
phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge
2015 dataset - based on the city of Porto -, obtaining better results with
respect to the competition winner, whilst using less information, and on
Manhattan and San Francisco datasets.Comment: preprint version of a paper submitted to IEEE Transactions on
Intelligent Transportation System
Hoodsquare: Modeling and Recommending Neighborhoods in Location-based Social Networks
Information garnered from activity on location-based social networks can be
harnessed to characterize urban spaces and organize them into neighborhoods. In
this work, we adopt a data-driven approach to the identification and modeling
of urban neighborhoods using location-based social networks. We represent
geographic points in the city using spatio-temporal information about
Foursquare user check-ins and semantic information about places, with the goal
of developing features to input into a novel neighborhood detection algorithm.
The algorithm first employs a similarity metric that assesses the homogeneity
of a geographic area, and then with a simple mechanism of geographic
navigation, it detects the boundaries of a city's neighborhoods. The models and
algorithms devised are subsequently integrated into a publicly available,
map-based tool named Hoodsquare that allows users to explore activities and
neighborhoods in cities around the world.
Finally, we evaluate Hoodsquare in the context of a recommendation
application where user profiles are matched to urban neighborhoods. By
comparing with a number of baselines, we demonstrate how Hoodsquare can be used
to accurately predict the home neighborhood of Twitter users. We also show that
we are able to suggest neighborhoods geographically constrained in size, a
desirable property in mobile recommendation scenarios for which geographical
precision is key.Comment: ASE/IEEE SocialCom 201
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