32,689 research outputs found
Cluster-Aided Mobility Predictions
Predicting the future location of users in wireless net- works has numerous
applications, and can help service providers to improve the quality of service
perceived by their clients. The location predictors proposed so far estimate
the next location of a specific user by inspecting the past individual
trajectories of this user. As a consequence, when the training data collected
for a given user is limited, the resulting prediction is inaccurate. In this
paper, we develop cluster-aided predictors that exploit past trajectories
collected from all users to predict the next location of a given user. These
predictors rely on clustering techniques and extract from the training data
similarities among the mobility patterns of the various users to improve the
prediction accuracy. Specifically, we present CAMP (Cluster-Aided Mobility
Predictor), a cluster-aided predictor whose design is based on recent
non-parametric bayesian statistical tools. CAMP is robust and adaptive in the
sense that it exploits similarities in users' mobility only if such
similarities are really present in the training data. We analytically prove the
consistency of the predictions provided by CAMP, and investigate its
performance using two large-scale datasets. CAMP significantly outperforms
existing predictors, and in particular those that only exploit individual past
trajectories
A Shared Task on Bandit Learning for Machine Translation
We introduce and describe the results of a novel shared task on bandit
learning for machine translation. The task was organized jointly by Amazon and
Heidelberg University for the first time at the Second Conference on Machine
Translation (WMT 2017). The goal of the task is to encourage research on
learning machine translation from weak user feedback instead of human
references or post-edits. On each of a sequence of rounds, a machine
translation system is required to propose a translation for an input, and
receives a real-valued estimate of the quality of the proposed translation for
learning. This paper describes the shared task's learning and evaluation setup,
using services hosted on Amazon Web Services (AWS), the data and evaluation
metrics, and the results of various machine translation architectures and
learning protocols.Comment: Conference on Machine Translation (WMT) 201
Improving Search through A3C Reinforcement Learning based Conversational Agent
We develop a reinforcement learning based search assistant which can assist
users through a set of actions and sequence of interactions to enable them
realize their intent. Our approach caters to subjective search where the user
is seeking digital assets such as images which is fundamentally different from
the tasks which have objective and limited search modalities. Labeled
conversational data is generally not available in such search tasks and
training the agent through human interactions can be time consuming. We propose
a stochastic virtual user which impersonates a real user and can be used to
sample user behavior efficiently to train the agent which accelerates the
bootstrapping of the agent. We develop A3C algorithm based context preserving
architecture which enables the agent to provide contextual assistance to the
user. We compare the A3C agent with Q-learning and evaluate its performance on
average rewards and state values it obtains with the virtual user in validation
episodes. Our experiments show that the agent learns to achieve higher rewards
and better states.Comment: 17 pages, 7 figure
The Skipping Behavior of Users of Music Streaming Services and its Relation to Musical Structure
The behavior of users of music streaming services is investigated from the
point of view of the temporal dimension of individual songs; specifically, the
main object of the analysis is the point in time within a song at which users
stop listening and start streaming another song ("skip"). The main contribution
of this study is the ascertainment of a correlation between the distribution in
time of skipping events and the musical structure of songs. It is also shown
that such distribution is not only specific to the individual songs, but also
independent of the cohort of users and, under stationary conditions, date of
observation. Finally, user behavioral data is used to train a predictor of the
musical structure of a song solely from its acoustic content; it is shown that
the use of such data, available in large quantities to music streaming
services, yields significant improvements in accuracy over the customary
fashion of training this class of algorithms, in which only smaller amounts of
hand-labeled data are available
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