80 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
Single-cell RNA-seq data imputation using Feature Propagation
While single-cell RNA sequencing provides an understanding of the
transcriptome of individual cells, its high sparsity, often termed dropout,
hampers the capture of significant cell-cell relationships. Here, we propose
scFP (single-cell Feature Propagation), which directly propagates features,
i.e., gene expression, especially in raw feature space, via cell-cell graph.
Specifically, it first obtains a warmed-up cell-gene matrix via Hard Feature
Propagation which fully utilizes known gene transcripts. Then, we refine the
k-Nearest Neighbor(kNN) of the cell-cell graph with a warmed-up cell-gene
matrix, followed by Soft Feature Propagation which now allows known gene
transcripts to be further denoised through their neighbors. Through extensive
experiments on imputation with cell clustering tasks, we demonstrate our
proposed model, scFP, outperforms various recent imputation and clustering
methods. The source code of scFP can be found at
https://github.com/Junseok0207/scFP.Comment: ICML 2023 Workshop on Computational Biology (Contributed Talk
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
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
Set2Box: Similarity Preserving Representation Learning of Sets
Sets have been used for modeling various types of objects (e.g., a document
as the set of keywords in it and a customer as the set of the items that she
has purchased). Measuring similarity (e.g., Jaccard Index) between sets has
been a key building block of a wide range of applications, including,
plagiarism detection, recommendation, and graph compression. However, as sets
have grown in numbers and sizes, the computational cost and storage required
for set similarity computation have become substantial, and this has led to the
development of hashing and sketching based solutions. In this work, we propose
Set2Box, a learning-based approach for compressed representations of sets from
which various similarity measures can be estimated accurately in constant time.
The key idea is to represent sets as boxes to precisely capture overlaps of
sets. Additionally, based on the proposed box quantization scheme, we design
Set2Box+, which yields more concise but more accurate box representations of
sets. Through extensive experiments on 8 real-world datasets, we show that,
compared to baseline approaches, Set2Box+ is (a) Accurate: achieving up to
40.8X smaller estimation error while requiring 60% fewer bits to encode sets,
(b) Concise: yielding up to 96.8X more concise representations with similar
estimation error, and (c) Versatile: enabling the estimation of four
set-similarity measures from a single representation of each set.Comment: Accepted by ICDM 202
Age-of-Information Aware Contents Caching and Distribution for Connected Vehicles
To support rapid and accurate autonomous driving services, road environment
information, which is difficult to obtain through vehicle sensors themselves,
is collected and utilized through communication with surrounding infrastructure
in connected vehicle networks. For this reason, we consider a scenario that
utilizes infrastructure such as road side units (RSUs) and macro base station
(MBS) in situations where caching of road environment information is required.
Due to the rapidly changed road environment, a concept which represents a
freshness of the road content, age of information (AoI), is important. Based on
the AoI value, in the connected vehicle system, it is essential to keep
appropriate content in the RSUs in advance, update it before the content is
expired, and send the content to the vehicles which want to use it. However,
too frequent content transmission for the minimum AoI leads to indiscriminate
use of network resources. Furthermore, a transmission control, that content AoI
and service delay are not properly considered adversely, affects user service.
Therefore, it is important to find an appropriate compromise. For these
reasons, the objective of this paper is about to reduce the system cost used
for content delivery through the proposed system while minimizing the content
AoI presented in MBS, RSUs and UVs. The transmission process, which is able to
be divided into two states, i.e., content caching and service, is approached
using Markov decision process (MDP) and Lyapunov optimization framework,
respectively, which guarantee optimal solutions, as verified via data-intensive
performance evaluation
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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