935 research outputs found
Adversarial Multimodal Representation Learning for Click-Through Rate Prediction
For better user experience and business effectiveness, Click-Through Rate
(CTR) prediction has been one of the most important tasks in E-commerce.
Although extensive CTR prediction models have been proposed, learning good
representation of items from multimodal features is still less investigated,
considering an item in E-commerce usually contains multiple heterogeneous
modalities. Previous works either concatenate the multiple modality features,
that is equivalent to giving a fixed importance weight to each modality; or
learn dynamic weights of different modalities for different items through
technique like attention mechanism. However, a problem is that there usually
exists common redundant information across multiple modalities. The dynamic
weights of different modalities computed by using the redundant information may
not correctly reflect the different importance of each modality. To address
this, we explore the complementarity and redundancy of modalities by
considering modality-specific and modality-invariant features differently. We
propose a novel Multimodal Adversarial Representation Network (MARN) for the
CTR prediction task. A multimodal attention network first calculates the
weights of multiple modalities for each item according to its modality-specific
features. Then a multimodal adversarial network learns modality-invariant
representations where a double-discriminators strategy is introduced. Finally,
we achieve the multimodal item representations by combining both
modality-specific and modality-invariant representations. We conduct extensive
experiments on both public and industrial datasets, and the proposed method
consistently achieves remarkable improvements to the state-of-the-art methods.
Moreover, the approach has been deployed in an operational E-commerce system
and online A/B testing further demonstrates the effectiveness.Comment: Accepted to WWW 2020, 10 page
A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor Networks
Due to the issue that existing wireless sensor network (WSN)-based anomaly
detection methods only consider and analyze temporal features, in this paper, a
self-supervised learning-based anomaly node detection method based on an
autoencoder is designed. This method integrates temporal WSN data flow feature
extraction, spatial position feature extraction and intermodal WSN correlation
feature extraction into the design of the autoencoder to make full use of the
spatial and temporal information of the WSN for anomaly detection. First, a
fully connected network is used to extract the temporal features of nodes by
considering a single mode from a local spatial perspective. Second, a graph
neural network (GNN) is used to introduce the WSN topology from a global
spatial perspective for anomaly detection and extract the spatial and temporal
features of the data flows of nodes and their neighbors by considering a single
mode. Then, the adaptive fusion method involving weighted summation is used to
extract the relevant features between different models. In addition, this paper
introduces a gated recurrent unit (GRU) to solve the long-term dependence
problem of the time dimension. Eventually, the reconstructed output of the
decoder and the hidden layer representation of the autoencoder are fed into a
fully connected network to calculate the anomaly probability of the current
system. Since the spatial feature extraction operation is advanced, the
designed method can be applied to the task of large-scale network anomaly
detection by adding a clustering operation. Experiments show that the designed
method outperforms the baselines, and the F1 score reaches 90.6%, which is 5.2%
higher than those of the existing anomaly detection methods based on
unsupervised reconstruction and prediction. Code and model are available at
https://github.com/GuetYe/anomaly_detection/GLS
A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining
Educational Data Mining (EDM) has emerged as a vital field of research, which
harnesses the power of computational techniques to analyze educational data.
With the increasing complexity and diversity of educational data, Deep Learning
techniques have shown significant advantages in addressing the challenges
associated with analyzing and modeling this data. This survey aims to
systematically review the state-of-the-art in EDM with Deep Learning. We begin
by providing a brief introduction to EDM and Deep Learning, highlighting their
relevance in the context of modern education. Next, we present a detailed
review of Deep Learning techniques applied in four typical educational
scenarios, including knowledge tracing, undesirable student detecting,
performance prediction, and personalized recommendation. Furthermore, a
comprehensive overview of public datasets and processing tools for EDM is
provided. Finally, we point out emerging trends and future directions in this
research area.Comment: 21 pages, 5 figure
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