17,235 research outputs found
Deep Interest Evolution Network for Click-Through Rate Prediction
Click-through rate~(CTR) prediction, whose goal is to estimate the
probability of the user clicks, has become one of the core tasks in advertising
systems. For CTR prediction model, it is necessary to capture the latent user
interest behind the user behavior data. Besides, considering the changing of
the external environment and the internal cognition, user interest evolves over
time dynamically. There are several CTR prediction methods for interest
modeling, while most of them regard the representation of behavior as the
interest directly, and lack specially modeling for latent interest behind the
concrete behavior. Moreover, few work consider the changing trend of interest.
In this paper, we propose a novel model, named Deep Interest Evolution
Network~(DIEN), for CTR prediction. Specifically, we design interest extractor
layer to capture temporal interests from history behavior sequence. At this
layer, we introduce an auxiliary loss to supervise interest extracting at each
step. As user interests are diverse, especially in the e-commerce system, we
propose interest evolving layer to capture interest evolving process that is
relative to the target item. At interest evolving layer, attention mechanism is
embedded into the sequential structure novelly, and the effects of relative
interests are strengthened during interest evolution. In the experiments on
both public and industrial datasets, DIEN significantly outperforms the
state-of-the-art solutions. Notably, DIEN has been deployed in the display
advertisement system of Taobao, and obtained 20.7\% improvement on CTR.Comment: 9 pages. Accepted by AAAI 201
The Third Order Scalar Induced Gravitational Waves
Since the gravitational waves were detected by LIGO and Virgo, it has been
promising that lots of information about the primordial Universe could be
learned by further observations on stochastic gravitational waves background.
The studies on gravitational waves induced by primordial curvature
perturbations are of great interest. The aim of this paper is to investigate
the third order induced gravitational waves. Based on the theory of
cosmological perturbations, the first order scalar induces the second order
scalar, vector and tensor perturbations. At the next iteration, the first order
scalar, the second order scalar, vector and tensor perturbations all induce the
third order tensor perturbations. We present the energy density spectrum of the
third order gravitational waves for a monochromatic primordial power spectrum.
The shape of the energy density spectrum of the third order gravitational waves
is different from that of the second order scalar induced gravitational waves.
And it is found that the third order gravitational waves sourced by the second
order scalar perturbations dominate the energy density spectrum.Comment: 33 pages, 4 figure
Bilinear effect in complex systems
The distribution of the lifetime of Chinese dynasties (as well as that of the
British Isles and Japan) in a linear Zipf plot is found to consist of two
straight lines intersecting at a transition point. This two-section
piecewise-linear distribution is different from the power law or the stretched
exponent distribution, and is called the Bilinear Effect for short. With
assumptions mimicking the organization of ancient Chinese regimes, a 3-layer
network model is constructed. Numerical results of this model show the bilinear
effect, providing a plausible explanation of the historical data. Bilinear
effect in two other social systems is presented, indicating that such a
piecewise-linear effect is widespread in social systems.Comment: 5 pages, 5 figure
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
Traffic signal control is an emerging application scenario for reinforcement
learning. Besides being as an important problem that affects people's daily
life in commuting, traffic signal control poses its unique challenges for
reinforcement learning in terms of adapting to dynamic traffic environment and
coordinating thousands of agents including vehicles and pedestrians. A key
factor in the success of modern reinforcement learning relies on a good
simulator to generate a large number of data samples for learning. The most
commonly used open-source traffic simulator SUMO is, however, not scalable to
large road network and large traffic flow, which hinders the study of
reinforcement learning on traffic scenarios. This motivates us to create a new
traffic simulator CityFlow with fundamentally optimized data structures and
efficient algorithms. CityFlow can support flexible definitions for road
network and traffic flow based on synthetic and real-world data. It also
provides user-friendly interface for reinforcement learning. Most importantly,
CityFlow is more than twenty times faster than SUMO and is capable of
supporting city-wide traffic simulation with an interactive render for
monitoring. Besides traffic signal control, CityFlow could serve as the base
for other transportation studies and can create new possibilities to test
machine learning methods in the intelligent transportation domain.Comment: WWW 2019 Demo Pape
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