1,118 research outputs found
FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
Advertising and feed ranking are essential to many Internet companies such as
Facebook and Sina Weibo. Among many real-world advertising and feed ranking
systems, click through rate (CTR) prediction plays a central role. There are
many proposed models in this field such as logistic regression, tree based
models, factorization machine based models and deep learning based CTR models.
However, many current works calculate the feature interactions in a simple way
such as Hadamard product and inner product and they care less about the
importance of features. In this paper, a new model named FiBiNET as an
abbreviation for Feature Importance and Bilinear feature Interaction NETwork is
proposed to dynamically learn the feature importance and fine-grained feature
interactions. On the one hand, the FiBiNET can dynamically learn the importance
of features via the Squeeze-Excitation network (SENET) mechanism; on the other
hand, it is able to effectively learn the feature interactions via bilinear
function. We conduct extensive experiments on two real-world datasets and show
that our shallow model outperforms other shallow models such as factorization
machine(FM) and field-aware factorization machine(FFM). In order to improve
performance further, we combine a classical deep neural network(DNN) component
with the shallow model to be a deep model. The deep FiBiNET consistently
outperforms the other state-of-the-art deep models such as DeepFM and extreme
deep factorization machine(XdeepFM).Comment: 8 pages,5 figure
Cross-convolutional-layer Pooling for Image Recognition
Recent studies have shown that a Deep Convolutional Neural Network (DCNN)
pretrained on a large image dataset can be used as a universal image
descriptor, and that doing so leads to impressive performance for a variety of
image classification tasks. Most of these studies adopt activations from a
single DCNN layer, usually the fully-connected layer, as the image
representation. In this paper, we proposed a novel way to extract image
representations from two consecutive convolutional layers: one layer is
utilized for local feature extraction and the other serves as guidance to pool
the extracted features. By taking different viewpoints of convolutional layers,
we further develop two schemes to realize this idea. The first one directly
uses convolutional layers from a DCNN. The second one applies the pretrained
CNN on densely sampled image regions and treats the fully-connected activations
of each image region as convolutional feature activations. We then train
another convolutional layer on top of that as the pooling-guidance
convolutional layer. By applying our method to three popular visual
classification tasks, we find our first scheme tends to perform better on the
applications which need strong discrimination on subtle object patterns within
small regions while the latter excels in the cases that require discrimination
on category-level patterns. Overall, the proposed method achieves superior
performance over existing ways of extracting image representations from a DCNN.Comment: Fixed typos. Journal extension of arXiv:1411.7466. Accepted to IEEE
Transactions on Pattern Analysis and Machine Intelligenc
- …