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 gating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

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