869,564 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
Black Strings in Our World
The brane world scenario is a new approach to resolve the problem on how to
compactify the higher dimensional spacetime to our 4-dimensional world. One of
the remarkable features of this scenario is the higher dimensional effects in
classical gravitational interactions at short distances. Due to this feature,
there are black string solutions in our 4-dimensional world. In this paper,
assuming the simplest model of complex minimally coupled scalar field with the
local U(1) symmetry, we show a possibility of black-string formation by merging
processes of type I long cosmic strings in our 4-dimensional world. No fine
tuning for the parameters in the model might be necessary.Comment: 11pages, no figur
Decomposing Global Feature Effects Based on Feature Interactions
Global feature effect methods, such as partial dependence plots, provide an
intelligible visualization of the expected marginal feature effect. However,
such global feature effect methods can be misleading, as they do not represent
local feature effects of single observations well when feature interactions are
present. We formally introduce generalized additive decomposition of global
effects (GADGET), which is a new framework based on recursive partitioning to
find interpretable regions in the feature space such that the
interaction-related heterogeneity of local feature effects is minimized. We
provide a mathematical foundation of the framework and show that it is
applicable to the most popular methods to visualize marginal feature effects,
namely partial dependence, accumulated local effects, and Shapley additive
explanations (SHAP) dependence. Furthermore, we introduce a new
permutation-based interaction test to detect significant feature interactions
that is applicable to any feature effect method that fits into our proposed
framework. We empirically evaluate the theoretical characteristics of the
proposed methods based on various feature effect methods in different
experimental settings. Moreover, we apply our introduced methodology to two
real-world examples to showcase their usefulness
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
Combinatorial features are essential for the success of many commercial
models. Manually crafting these features usually comes with high cost due to
the variety, volume and velocity of raw data in web-scale systems.
Factorization based models, which measure interactions in terms of vector
product, can learn patterns of combinatorial features automatically and
generalize to unseen features as well. With the great success of deep neural
networks (DNNs) in various fields, recently researchers have proposed several
DNN-based factorization model to learn both low- and high-order feature
interactions. Despite the powerful ability of learning an arbitrary function
from data, plain DNNs generate feature interactions implicitly and at the
bit-wise level. In this paper, we propose a novel Compressed Interaction
Network (CIN), which aims to generate feature interactions in an explicit
fashion and at the vector-wise level. We show that the CIN share some
functionalities with convolutional neural networks (CNNs) and recurrent neural
networks (RNNs). We further combine a CIN and a classical DNN into one unified
model, and named this new model eXtreme Deep Factorization Machine (xDeepFM).
On one hand, the xDeepFM is able to learn certain bounded-degree feature
interactions explicitly; on the other hand, it can learn arbitrary low- and
high-order feature interactions implicitly. We conduct comprehensive
experiments on three real-world datasets. Our results demonstrate that xDeepFM
outperforms state-of-the-art models. We have released the source code of
xDeepFM at \url{https://github.com/Leavingseason/xDeepFM}.Comment: 10 page
Recognition of human interactions using limb-level feature points
Human activity recognition is an emerging area of research in computer vision with applications in video surveillance, human-computer interaction, robotics, and video annotation. Despite a number of recent advances, there are still many opportunities for new developments, especially in the area of person-person and person-object interaction. Many proposed algorithms focus on recognizing solely single person, person-person or person-object activities. An algorithm which can recognize all three types would be a significant step toward the real-world application of this technology. This thesis investigates the design and implementation of such an algorithm. It utilizes background subtraction to extract the subjects in the scene, and pixel clustering to segment their image into body parts. A location-based feature identification algorithm extracts feature points from these segments and feeds them to a classifier which identifies videos as activities. Together these techniques comprise an algorithm that can recognize single person, person-person and person-object interactions. This algorithm\u27s performance was evaluated based on interactions in a new video dataset, demonstrating the effectiveness of using limb-level feature points as a method of identifying human interactions
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