131 research outputs found
Measuring the Eccentricity of Items
The long-tail phenomenon tells us that there are many items in the tail.
However, not all tail items are the same. Each item acquires different kinds of
users. Some items are loved by the general public, while some items are
consumed by eccentric fans. In this paper, we propose a novel metric, item
eccentricity, to incorporate this difference between consumers of the items.
Eccentric items are defined as items that are consumed by eccentric users. We
used this metric to analyze two real-world datasets of music and movies and
observed the characteristics of items in terms of eccentricity. The results
showed that our defined eccentricity of an item does not change much over time,
and classified eccentric and noneccentric items present significantly distinct
characteristics. The proposed metric effectively separates the eccentric and
noneccentric items mixed in the tail, which could not be done with the previous
measures, which only consider the popularity of items.Comment: Accepted at IEEE International Conference on Systems, Man, and
Cybernetics (SMC) 201
Interpretable Prototype-based Graph Information Bottleneck
The success of Graph Neural Networks (GNNs) has led to a need for
understanding their decision-making process and providing explanations for
their predictions, which has given rise to explainable AI (XAI) that offers
transparent explanations for black-box models. Recently, the use of prototypes
has successfully improved the explainability of models by learning prototypes
to imply training graphs that affect the prediction. However, these approaches
tend to provide prototypes with excessive information from the entire graph,
leading to the exclusion of key substructures or the inclusion of irrelevant
substructures, which can limit both the interpretability and the performance of
the model in downstream tasks. In this work, we propose a novel framework of
explainable GNNs, called interpretable Prototype-based Graph Information
Bottleneck (PGIB) that incorporates prototype learning within the information
bottleneck framework to provide prototypes with the key subgraph from the input
graph that is important for the model prediction. This is the first work that
incorporates prototype learning into the process of identifying the key
subgraphs that have a critical impact on the prediction performance. Extensive
experiments, including qualitative analysis, demonstrate that PGIB outperforms
state-of-the-art methods in terms of both prediction performance and
explainability.Comment: NeurIPS 202
Review-Electro-Kinetic Decontamination of Radioactive Concrete Waste from Nuclear Power Plants
Electro-kinetic decontamination has been studied for radioactive concrete of nuclear power plants because of its effective removal of contaminants from deep inside concrete. Although many experiments have been conducted, a systematic comparison has been scarcely conducted. By a thorough review, this study reveals how different conditions of electro-kinetic decontamination changes the decontamination ratio and rate of Cs and Co. The tested conditions include cell configurations (i.e., geometry of concrete waste, electrode materials, and volume of solutions) and operating conditions (i.e., types and concentrations of solutions, electric field, and test duration). The careful analysis suggests the important roles of pH in electrolytic solution, electric field, and pre-treatment. We also discuss the chemical conditions under which the decontamination of Cs and Co was optimized in the presence of an applied voltage. In addition, we critically review the conditions of simulated concrete samples in the previous experiments in comparison with actual nuclear plant data
Click-aware purchase prediction with push at the top
Eliciting user preferences from purchase records for performing purchase
prediction is challenging because negative feedback is not explicitly observed,
and because treating all non-purchased items equally as negative feedback is
unrealistic. Therefore, in this study, we present a framework that leverages
the past click records of users to compensate for the missing user-item
interactions of purchase records, i.e., non-purchased items. We begin by
formulating various model assumptions, each one assuming a different order of
user preferences among purchased, clicked-but-not-purchased, and non-clicked
items, to study the usefulness of leveraging click records. We implement the
model assumptions using the Bayesian personalized ranking model, which
maximizes the area under the curve for bipartite ranking. However, we argue
that using click records for bipartite ranking needs a meticulously designed
model because of the relative unreliableness of click records compared with
that of purchase records. Therefore, we ultimately propose a novel
learning-to-rank method, called P3Stop, for performing purchase prediction. The
proposed model is customized to be robust to relatively unreliable click
records by particularly focusing on the accuracy of top-ranked items.
Experimental results on two real-world e-commerce datasets demonstrate that
P3STop considerably outperforms the state-of-the-art implicit-feedback-based
recommendation methods, especially for top-ranked items.Comment: For the final published journal version, see
https://doi.org/10.1016/j.ins.2020.02.06
S-Mixup: Structural Mixup for Graph Neural Networks
Existing studies for applying the mixup technique on graphs mainly focus on
graph classification tasks, while the research in node classification is still
under-explored. In this paper, we propose a novel mixup augmentation for node
classification called Structural Mixup (S-Mixup). The core idea is to take into
account the structural information while mixing nodes. Specifically, S-Mixup
obtains pseudo-labels for unlabeled nodes in a graph along with their
prediction confidence via a Graph Neural Network (GNN) classifier. These serve
as the criteria for the composition of the mixup pool for both inter and
intra-class mixups. Furthermore, we utilize the edge gradient obtained from the
GNN training and propose a gradient-based edge selection strategy for selecting
edges to be attached to the nodes generated by the mixup. Through extensive
experiments on real-world benchmark datasets, we demonstrate the effectiveness
of S-Mixup evaluated on the node classification task. We observe that S-Mixup
enhances the robustness and generalization performance of GNNs, especially in
heterophilous situations. The source code of S-Mixup can be found at
\url{https://github.com/SukwonYun/S-Mixup}Comment: CIKM 2023 (Short Paper
Task Relation-aware Continual User Representation Learning
User modeling, which learns to represent users into a low-dimensional
representation space based on their past behaviors, got a surge of interest
from the industry for providing personalized services to users. Previous
efforts in user modeling mainly focus on learning a task-specific user
representation that is designed for a single task. However, since learning
task-specific user representations for every task is infeasible, recent studies
introduce the concept of universal user representation, which is a more
generalized representation of a user that is relevant to a variety of tasks.
Despite their effectiveness, existing approaches for learning universal user
representations are impractical in real-world applications due to the data
requirement, catastrophic forgetting and the limited learning capability for
continually added tasks. In this paper, we propose a novel continual user
representation learning method, called TERACON, whose learning capability is
not limited as the number of learned tasks increases while capturing the
relationship between the tasks. The main idea is to introduce an embedding for
each task, i.e., task embedding, which is utilized to generate task-specific
soft masks that not only allow the entire model parameters to be updated until
the end of training sequence, but also facilitate the relationship between the
tasks to be captured. Moreover, we introduce a novel knowledge retention module
with pseudo-labeling strategy that successfully alleviates the long-standing
problem of continual learning, i.e., catastrophic forgetting. Extensive
experiments on public and proprietary real-world datasets demonstrate the
superiority and practicality of TERACON. Our code is available at
https://github.com/Sein-Kim/TERACON.Comment: KDD 202
Workload-Aware Scheduling using Markov Decision Process for Infrastructure-Assisted Learning-Based Multi-UAV Surveillance Networks
In modern networking research, infrastructure-assisted unmanned autonomous
vehicles (UAVs) are actively considered for real-time learning-based
surveillance and aerial data-delivery under unexpected 3D free mobility and
coordination. In this system model, it is essential to consider the power
limitation in UAVs and autonomous object recognition (for abnormal behavior
detection) deep learning performance in infrastructure/towers. To overcome the
power limitation of UAVs, this paper proposes a novel aerial scheduling
algorithm between multi-UAVs and multi-towers where the towers conduct wireless
power transfer toward UAVs. In addition, to take care of the high-performance
learning model training in towers, we also propose a data delivery scheme which
makes UAVs deliver the training data to the towers fairly to prevent problems
due to data imbalance (e.g., huge computation overhead caused by larger data
delivery or overfitting from less data delivery). Therefore, this paper
proposes a novel workload-aware scheduling algorithm between multi-towers and
multi-UAVs for joint power-charging from towers to their associated UAVs and
training data delivery from UAVs to their associated towers. To compute the
workload-aware optimal scheduling decisions in each unit time, our solution
approach for the given scheduling problem is designed based on Markov decision
process (MDP) to deal with (i) time-varying low-complexity computation and (ii)
pseudo-polynomial optimality. As shown in performance evaluation results, our
proposed algorithm ensures (i) sufficient times for resource exchanges between
towers and UAVs, (ii) the most even and uniform data collection during the
processes compared to the other algorithms, and (iii) the performance of all
towers convergence to optimal levels.Comment: 15 pages, 10 figure
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