251 research outputs found
Dynamic Learning of Sequential Choice Bandit Problem under Marketing Fatigue
Motivated by the observation that overexposure to unwanted marketing
activities leads to customer dissatisfaction, we consider a setting where a
platform offers a sequence of messages to its users and is penalized when users
abandon the platform due to marketing fatigue. We propose a novel sequential
choice model to capture multiple interactions taking place between the platform
and its user: Upon receiving a message, a user decides on one of the three
actions: accept the message, skip and receive the next message, or abandon the
platform. Based on user feedback, the platform dynamically learns users'
abandonment distribution and their valuations of messages to determine the
length of the sequence and the order of the messages, while maximizing the
cumulative payoff over a horizon of length T. We refer to this online learning
task as the sequential choice bandit problem. For the offline combinatorial
optimization problem, we show that an efficient polynomial-time algorithm
exists. For the online problem, we propose an algorithm that balances
exploration and exploitation, and characterize its regret bound. Lastly, we
demonstrate how to extend the model with user contexts to incorporate
personalization
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes
Graph convolutional networks (GCNs) have achieved great success in graph
representation learning by extracting high-level features from nodes and their
topology. Since GCNs generally follow a message-passing mechanism, each node
aggregates information from its first-order neighbour to update its
representation. As a result, the representations of nodes with edges between
them should be positively correlated and thus can be considered positive
samples. However, there are more non-neighbour nodes in the whole graph, which
provide diverse and useful information for the representation update. Two
non-adjacent nodes usually have different representations, which can be seen as
negative samples. Besides the node representations, the structural information
of the graph is also crucial for learning. In this paper, we used
quality-diversity decomposition in determinant point processes (DPP) to obtain
diverse negative samples. When defining a distribution on diverse subsets of
all non-neighbouring nodes, we incorporate both graph structure information and
node representations. Since the DPP sampling process requires matrix eigenvalue
decomposition, we propose a new shortest-path-base method to improve
computational efficiency. Finally, we incorporate the obtained negative samples
into the graph convolution operation. The ideas are evaluated empirically in
experiments on node classification tasks. These experiments show that the newly
proposed methods not only improve the overall performance of standard
representation learning but also significantly alleviate over-smoothing
problems.Comment: Accepted by IEEE TNNLS on 30-Aug-2023. arXiv admin note: text overlap
with arXiv:2210.0072
Fatigue-aware Bandits for Dependent Click Models
As recommender systems send a massive amount of content to keep users
engaged, users may experience fatigue which is contributed by 1) an
overexposure to irrelevant content, 2) boredom from seeing too many similar
recommendations. To address this problem, we consider an online learning
setting where a platform learns a policy to recommend content that takes user
fatigue into account. We propose an extension of the Dependent Click Model
(DCM) to describe users' behavior. We stipulate that for each piece of content,
its attractiveness to a user depends on its intrinsic relevance and a discount
factor which measures how many similar contents have been shown. Users view the
recommended content sequentially and click on the ones that they find
attractive. Users may leave the platform at any time, and the probability of
exiting is higher when they do not like the content. Based on user's feedback,
the platform learns the relevance of the underlying content as well as the
discounting effect due to content fatigue. We refer to this learning task as
"fatigue-aware DCM Bandit" problem. We consider two learning scenarios
depending on whether the discounting effect is known. For each scenario, we
propose a learning algorithm which simultaneously explores and exploits, and
characterize its regret bound
On Finite Difference Jacobian Computation in Deformable Image Registration
Producing spatial transformations that are diffeomorphic has been a central
problem in deformable image registration. As a diffeomorphic transformation
should have positive Jacobian determinant everywhere, the number of
voxels with has been used to test for diffeomorphism and also to
measure the irregularity of the transformation. For digital transformations,
is commonly approximated using central difference, but this strategy can
yield positive 's for transformations that are clearly not diffeomorphic
-- even at the voxel resolution level. To show this, we first investigate the
geometric meaning of different finite difference approximations of . We
show that to determine diffeomorphism for digital images, use of any individual
finite difference approximations of is insufficient. We show that for a
2D transformation, four unique finite difference approximations of 's must
be positive to ensure the entire domain is invertible and free of folding at
the pixel level. We also show that in 3D, ten unique finite differences
approximations of 's are required to be positive. Our proposed digital
diffeomorphism criteria solves several errors inherent in the central
difference approximation of and accurately detects non-diffeomorphic
digital transformations
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