28 research outputs found
Modeling Occasion Evolution in Frequency Domain for Promotion-Aware Click-Through Rate Prediction
Promotions are becoming more important and prevalent in e-commerce to attract
customers and boost sales, leading to frequent changes of occasions, which
drives users to behave differently. In such situations, most existing
Click-Through Rate (CTR) models can't generalize well to online serving due to
distribution uncertainty of the upcoming occasion. In this paper, we propose a
novel CTR model named MOEF for recommendations under frequent changes of
occasions. Firstly, we design a time series that consists of occasion signals
generated from the online business scenario. Since occasion signals are more
discriminative in the frequency domain, we apply Fourier Transformation to
sliding time windows upon the time series, obtaining a sequence of frequency
spectrum which is then processed by Occasion Evolution Layer (OEL). In this
way, a high-order occasion representation can be learned to handle the online
distribution uncertainty. Moreover, we adopt multiple experts to learn feature
representations from multiple aspects, which are guided by the occasion
representation via an attention mechanism. Accordingly, a mixture of feature
representations is obtained adaptively for different occasions to predict the
final CTR. Experimental results on real-world datasets validate the superiority
of MOEF and online A/B tests also show MOEF outperforms representative CTR
models significantly
FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce Recommendation
Since clicks usually contain heavy noise, increasing research efforts have
been devoted to modeling implicit negative user behaviors (i.e., non-clicks).
However, they either rely on explicit negative user behaviors (e.g., dislikes)
or simply treat non-clicks as negative feedback, failing to learn negative user
interests comprehensively. In such situations, users may experience fatigue
because of seeing too many similar recommendations. In this paper, we propose
Fatigue-Aware Network (FAN), a novel CTR model that directly perceives user
fatigue from non-clicks. Specifically, we first apply Fourier Transformation to
the time series generated from non-clicks, obtaining its frequency spectrum
which contains comprehensive information about user fatigue. Then the frequency
spectrum is modulated by category information of the target item to model the
bias that both the upper bound of fatigue and users' patience is different for
different categories. Moreover, a gating network is adopted to model the
confidence of user fatigue and an auxiliary task is designed to guide the
learning of user fatigue, so we can obtain a well-learned fatigue
representation and combine it with user interests for the final CTR prediction.
Experimental results on real-world datasets validate the superiority of FAN and
online A/B tests also show FAN outperforms representative CTR models
significantly
Single-Molecule Electrochemical Transistor Utilizing a Nickel-Pyridyl Spinterface
Using a scanning tunnelling microscope
break-junction technique,
we produce 4,4′-bipyridine (44BP) single-molecule junctions
with Ni and Au contacts. Electrochemical control is used to prevent
Ni oxidation and to modulate the conductance of the devices via nonredox
gatingî—¸the first time this has been shown using non-Au contacts.
Remarkably the conductance and gain of the resulting Ni-44BP-Ni electrochemical
transistors is significantly higher than analogous Au-based devices.
Ab-initio calculations reveal that this behavior arises because charge
transport is mediated by spin-polarized Ni <i>d</i>-electrons,
which hybridize strongly with molecular orbitals to form a “spinterface”.
Our results highlight the important role of the contact material for
single-molecule devices and show that it can be varied to provide
control of charge and spin transport
CFD Modeling of Nucleation, Growth, Aggregation, and Breakage in Continuous Precipitation of Barium Sulfate in a Stirred Tank
In this work, the precipitation of barium sulfate (BaSO(4)) in a continuous stirred tank reactor (CSTR) is modeled. The flow field is obtained through solving the single-phase Reynolds averaged Navier-Stokes equations with a standard single-phase k-epsilon turbulence model. The population balance equation is solved through the standard method of moments (SMM) and the quadrature method of moments (QMOM) both with and without aggregation and breakage terms. In the cases of precipitation simulation without aggregation and breakage, the results predicted from 2-node QMOM, 3-node QMOM, and SMM are very close. Thus, 2-node QMOM could replace SMM and be well-incorporated into an in-house CFD code to simulate the precipitation in CSTR with acceptable accuracy. The predicted area-averaged crystal size d(32) decreases almost linearly with increasing feed concentration, and the deviation from experimental data becomes significant at high feed concentration. Numerical simulation using 2-node QMOM with the Brownian motion and shear-induced aggregation kernels as well as a power-law breakage kernel indicates that the predicted d(32) shows good qualitative agreement with experimental results, and the quantitative agreement is achieved when the appropriate breakage rate equation is adopted
Experimental determination and numerical simulation of mixing time in a gas–liquid stirred tank
Numerical Simulation of Barium Sulfate Precipitation Process in a Continuous Stirred Tank with Multiple-Time-Scale Turbulent Mixer Model
Mixing of reagents is very important in precipitation processes, as it can significantly affect the size distribution and morphology of products. In this work, the influence of turbulent mixing on the course of barium sulfate precipitation process in a continuous stirred tank was investigated with multiple-time-scale turbulent mixer model. The effect of various operating conditions such as feed concentration, stirrer speed, and mean residence time on the barium sulfate precipitation process was clearly demonstrated. The simulation results were compared to the literature data, and good agreement is observed