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Predicting customer churn for platform businesses: using latent variables of variational autoencoder as consumers' purchasing behavior

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NEURAL COMPUTING & APPLICATIONS 2022年第21期34卷 18525-18541页

作者： Hasumoto, Kyosuke Goto, Masayuki Waseda Univ Inst Data Sci Tokyo Japan Waseda Univ Fac Sci & Engn Tokyo Japan

Customer churn is considered a critical issue for all businesses, as customer loss leads to a decrease in future profits. Although acquiring new customers can address such losses, this process tends to cost more than retaining existing customers. Therefore, identifying potential churners and then retaining them is important. While churn prediction has been studied widely, current research must analyze more complex business models in response to their increase, such as platform businesses. However, modeling churn prediction for these businesses is challenging because consumer behavior over platforms is more complicated. Past approaches to churn prediction can be improved upon using recent advancements in deep learning that capture the nonlinear relationships behind the data in a data-driven manner, especially for complex business models. This study proposes a method of extracting latent features from purchase histories as explanatory variables for churn prediction using a variational autoencoder with the actual customer distribution as a prior. The proposed method is validated using real purchase data from a platform business and shows a 1.5% improvement in F-measure against the baseline, and a 20% improvement for customers with recent transactions. Subsequently, the variables are examined using several methods for data analysis to interpret the meanings of the extracted features and underlying customers' purchasing behavior in the group of potential churners. With these analyses, the model provides practical implications for understanding what kind of purchasing behavior may lead to churn and planning effective retention strategies.

关键词： variational autoencoder Deep learning Churn prediction RFM Customer lifetime value Consumer purchase behavior

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Dimension reduction and 2D-visualization for early change of state detection in a machining process with a variational autoencoder approach

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2020年第11-12期111卷 3597-3611页

作者： Proteau, Antoine Zemouri, Ryad Tahan, Antoine Thomas, Marc Ecole Technol Super Dept Mech Engn Montreal PQ H3C 1K3 Canada HESAM Univ CEDRIC Lab Conservatoire Natl Arts & Metiers CNAM F-750141 Paris France

In this paper, we applied a variational autoencoder approach to an industrial machining problematic. We proposed a model based on a two-steps training process and a two-dimensional latent space. This two-dimensional latent space has better dimension reduction capability compared to a principal component analysis, which would require 24 components to express 90.0% of the variation. Moreover, the proposed model is shown capable of classifying a cutting operation based solely on data obtained from sensors mounted on a CNC machine, with an accuracy of 99.24%. The suggested model is also shown capable to be an efficient visual process monitoring tool capable of detecting early changes of state in a machining process. We show that this approach can visually identify defect caused by an increase of less than 1% of the energy in the signal, which is earlier than conventional monitoring methods. Additionally, our work is based on an industrial dataset acquired during regular production. This increases the opportunity for technological transfer when it comes to better understanding and better monitoring early changes in a machining process.

关键词： variational autoencoder Dimension reduction 2D-visualization Process monitoring Early detection Machining process

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Sentiment-Oriented Transformer-Based variational autoencoder Network for Live Video Commenting

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2024年第4期20卷 1-24页

作者： Fu, Fengyi Fang, Shancheng Chen, Weidong Mao, Zhendong Univ Sci & Technol China 100 Fuxing Rd Hefei 230000 Anhui Peoples R China

Automatic live video commenting is getting increasing attention due to its significance in narration generation, topic explanation, etc. However, the diverse sentiment consideration of the generated comments is missing from current methods. Sentimental factors are critical in interactive commenting, and there has been lack of research so far. Thus, in this article, we propose a Sentiment-oriented Transformer-based variational autoencoder (So-TVAE) network, which consists of a sentiment-oriented diversity encoder module and a batch attention module, to achieve diverse video commenting with multiple sentiments and multiple semantics. Specifically, our sentiment-oriented diversity encoder elegantly combines a VAE and random mask mechanism to achieve semantic diversity under sentiment guidance, which is then fused with cross-modal features to generate live video comments. A batch attention module is also proposed in this article to alleviate the problem of missing sentimental samples, caused by the data imbalance that is common in live videos as the popularity of videos varies. Extensive experiments on Livebot and VideoIC datasets demonstrate that the proposed So-TVAE outperforms the state-of-the-art methods in terms of the quality and diversity of generated comments. Related code is available at https://***/fufy1024/So-TVAE.

关键词： Automatic live video commenting multi-modal learning variational autoencoder batch attention mechanism

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Differentially Private Recurrent variational autoencoder For Text Privacy Preservation

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MOBILE NETWORKS & APPLICATIONS 2023年第5期28卷 1565-1580页

作者： Wang, Yuyang Meng, Xianjia Liu, Ximeng Northwest Univ Coll Informat Sci & Technol 229 North Taibai Rd Xian 710069 Shaanxi Peoples R China Northwest Univ Network & Data Ctr 229 North Taibai Rd Xian 710069 Shaanxi Peoples R China Fuzhou Univ Coll Comp & Data Sci 2 Xue Yuan Rd Fuzhou 350108 Fujian Peoples R China

Deep learning techniques have been widely used in natural language processing (NLP) tasks and have made remarkable progress. However, training the deep learning model relies on a large amount of data which may involve sensitive information like electronic medical records. The attacker can infer sensitive information from the model, which leads to privacy leakage. To solve this problem, we propose a Differentially Private Recurrent variational autoencoder (DP-RVAE) that can generate simulated data in place of the sensitive dataset to preserve privacy. To generate high utility synthetic text, a part of sensitive text data is employed as the conditional input of the model and uses a dropout and noise perturbing mechanism to preserve differential privacy. In addition, we expand the proposed DP-RVAE to a federated learning setting and design a novel training paradigm for NLP tasks. Specifically, DP-RVAE is deployed to the client-side to train and generate personalized text. These DP-RVAE models would be aggregated and updated through the Federated Optimisation (FedOPT) algorithm so that personal information can be well preserved. We evaluate our proposed DP-RVAE through a text classification task on the Tweets depression sentiment and IMDB reviews datasets. Our DP-RVAE achieves a higher average test accuracy by 5.90% and 3.94% compared to the typical centralized training and federated learning approach, respectively. We also perform the keywords inference attack experiment on the medical description dataset collected from the real world. Compared to the typical differentially private preserving approach, the DP-RVAE decreases by 15.2% in average attack accuracy. The experimental results demonstrate that DP-RVAE can be applied to the NLP models to leverage accuracy while preserving sensitive privacy.

关键词： Differential privacy variational autoencoder Generative model Sensitive data desensitization

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Condition-Transforming variational autoencoder for Generating Diverse Short Text Conversations

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2020年第6期19卷 1–13页

作者： Ruan, Yu-Ping Ling, Zhen-Hua Zhu, Xiaodan Univ Sci & Technol China Natl Engn Lab Speech & Language Informat Proc 443 Huangshan Rd Hefei Peoples R China Queens Univ Dept Elect & Comp Engn Walter Light HallUnion St Kingston ON K7L 2N1 Canada

In this article, conditional-transforming variational autoencoders (CTVAEs) are proposed for generating diverse short text conversations. In conditional variational autoencoders (CVAEs), the prior distribution of latent variable z follows a multivariate Gaussian distribution with mean and variance modulated by the input conditions. Previous work found that this distribution tended to become condition-independent in practical applications. Thus, this article designs CTVARs to enhance the influence of conditions in CVAEs. In a CTVAE model, the latent variable z is sampled by performing a non-linear transformation on the combination of the input conditions and the samples from a condition-independent prior distribution N(0.1). In our experiments using a Chinese Sina Weibo dataset, the CTVAE model derives z samples for decoding with better condition-dependency than that of the CVAE model. The earth mover's distance (EMD) between the distributions of the latent variable z at the training stage, and the testing stage is also reduced by using the CTVAE model. In subjective preference tests, our proposed CTVAE model performs significantly better than CVAE and sequence-to-sequence (Seq2Seq) models on generating diverse, informative, and topic-relevant responses.

关键词： Neural network variational autoencoder conversation text generation

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Degradation assessment for the ball screw with variational autoencoder and kernel density estimation

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ADVANCES IN MECHANICAL ENGINEERING 2018年第9期10卷

作者： Wen, Juan Gao, Hongli Southwest Jiaotong Univ Sch Mech Engn 111North 1st2nd Ring Rd Chengdu 610031 Sichuan Peoples R China

The ball screw is an important component of machine tools, and its degradation assessment is therefore critical for the health management of the entire machine tool. Generally, the degradation assessment includes health indicator construction and degradation modeling. However, the health indicator is often constructed manually with prior knowledge, and its sensitivity can be affected by various factors. In addition, most existing degradation models rely on a large amount of failure data, which is not practical for the ball screw due to its high reliability. To solve these problems, this article presents a novel ball screw performance evaluation method. First, the raw data collected in the normal status are used to train the variational autoencoder, and then, the online raw signals are input into the learned variational autoencoder to construct health indicators. After that, the kernel density estimation is utilized to estimate the probability distribution of health indicator points in a dynamic sliding window, and then, the deterioration can be evaluated by summarizing the probability distribution that exceeds a predefined threshold. Experimental results show that the presented methodology can establish the health indicator automatically and adaptively. Also, it can evaluate the ball screw performance effectively and quantitatively when only data in healthy state are available.

关键词： Ball screw degradation assessment health indicator variational autoencoder kernel density estimation

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Learning and controlling the source-filter representation of speech with a variational autoencoder

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SPEECH COMMUNICATION 2023年 148卷 53-65页

作者： Sadok, Samir Leglaive, Simon Girin, Laurent Alameda-Pineda, Xavier Seguier, Renaud CNRS CentraleSupelec IETR UMR 6164 Paris France Univ Grenoble Alpes GIPSA Lab CNRS Grenoble INP Grenoble France Univ Grenoble Alpes Inria CNRS LJK Grenoble France

Understanding and controlling latent representations in deep generative models is a challenging yet important problem for analyzing, transforming and generating various types of data. In speech processing, inspiring from the anatomical mechanisms of phonation, the source-filter model considers that speech signals are produced from a few independent and physically meaningful continuous latent factors, among which the fundamental frequency f(0) and the formants are of primary importance. In this work, we start from a variational autoencoder (VAE) trained in an unsupervised manner on a large dataset of unlabeled natural speech signals, and we show that the source-filter model of speech production naturally arises as orthogonal subspaces of the VAE latent space. Using only a few seconds of labeled speech signals generated with an artificial speech synthesizer, we propose a method to identify the latent subspaces encoding f(0) and the first three formant frequencies, we show that these subspaces are orthogonal, and based on this orthogonality, we develop a method to accurately and independently control the source-filter speech factors within the latent subspaces. Without requiring additional information such as text or human-labeled data, this results in a deep generative model of speech spectrograms that is conditioned on f(0) and the formant frequencies, and which is applied to the transformation speech signals. Finally, we also propose a robust f(0) estimation method that exploits the projection of a speech signal onto the learned latent subspace associated with f(0).

关键词： Representation learning Deep generative models variational autoencoder Source-filter model

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Intelligent optimal feature selection-based hybrid variational autoencoder and block recurrent transformer network for accurate emotion recognition model using EEG signals

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第2期18卷 1027-1039页

作者： Reddy, C. H. Narsimha Mahesh, Shanthi Manjunathachari, K. GITAM Univ Elect Elect & Commun Engn EECE Hyderabad 502329 Telangana India Atria Inst Technol Informat Sci & Engn Bengaluru 560024 India

In the context of emotion recognition, Artificial Intelligence technology has demonstrated several functions in people's lives. Computing research is now focused on Electroencephalogram (EEG) signals to identify emotional states. The connection and interaction between multichannel EEG signals give important information about emotional states. However, most existing emotion identification techniques perform poorly in practical applications by preventing their advancement. The main objective of this paper is to design an efficient model for emotion recognition based on deep learning technology by EEG signals. The proposed model for emotion recognition collects the EEG signals from the standard benchmark datasets. Then, the signal decomposition is performed using the Tunable Q-factor Wavelet Transform with the collected EEG signals. The decomposed signals are taken for the optimal feature selection phase, where the significant features of the emotion are selected through the hybrid optimization algorithm named Aquila Fireworks Optimization Algorithm (AFOA). Finally, the EEG emotion classification is performed using Hybrid variational autoencoder and Block Recurrent Transformer Network. The tuning of the parameter is made through the same AFOA to improve the efficiency of classification. The experimental analysis is conducted to analyze the efficiency of the recommended emotion recognition model with the comparison over the traditional techniques.

关键词： Hybrid variational autoencoder and block recurrent transformer network Aquila fireworks optimization algorithm Emotion recognition EEG signals Block recurrent transformer network variational autoencoder

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Rapid Inference of Nitrogen Oxide Emissions Based on a Top-Down Method with a Physically Informed variational autoencoder

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ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022年第14期56卷 9903-9914页

作者： Xing, Jia Li, Siwei Zheng, Shuxin Liu, Chang Wang, Xiaochun Huang, Lin Song, Ge He, Yihan Wang, Shuxiao Sahu, Shovan Kumar Zhang, Jia Bian, Jiang Zhu, Yun Liu, Tie-Yan Hao, Jiming Tsinghua Univ Sch Environm State Key Joint Lab Environm Simulat & Pollut Con Beijing 100084 Peoples R China State Environm Protect Key Lab Sources & Control Beijing 100084 Peoples R China Microsoft Res Asia Beijing 100080 Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Carnegie Mellon Univ Mech Engn Dept Pittsburgh PA 15213 USA South China Univ Technol Coll Environm & Energy Guangzhou Higher Educ Mega Ctr Guangzhou 510006 Peoples R China

Accurate timely estimation of emissions of nitrogen oxides (NOx) is a prerequisite for designing an effective strategy for reducing O-3 and PM2.5 pollution. The satellite-based top-down method can provide near-real-time constraints on emissions;however, its efficiency is largely limited by efforts in dealing with the complex emission-concentration response. Here, we propose a novel machine-learning-based method using a physically informed variational autoencoder (VAE) emission predictor to infer NOx emissions from satellite-retrieved surface NO2 concentrations. The computational burden can be significantly reduced with the help of a neural network trained with a chemical transport model, allowing the VAE emission predictor to provide a timely estimation of posterior emissions based on the satellite-retrieved surface NO2 concentration. The VAE emission predictor successfully corrected the underestimation of NOx emissions in rural areas and the overestimation in urban areas, resulting in smaller normalized mean biases (reduced from -0.8 to -0.4) and larger R-2 values (increased from 0.4 to 0.7). The interpretability of the VAE emission predictor was investigated using sensitivity analysis by modulating each feature, indicating that NO2 concentration and planetary boundary layer (PBL) height are important for estimating NO(x )emissions, which is consistent with our common knowledge. The advantages of the VAE emission predictor in efficiency, flexibility, and accuracy demonstrate its great potential in estimating the latest emissions and evaluating the control effectiveness from observations.

关键词： NOx emissions satellite variational autoencoder machine learning inverse modeling

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A variational autoencoder Cascade Generative Adversarial Network for Scalable 3D Object Generation and Reconstruction

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SENSORS 2024年第3期24卷 751页

作者： Yu, Min-Su Jung, Tae-Won Yun, Dai-Yeol Hwang, Chi-Gon Park, Sea-Young Kwon, Soon-Chul Jung, Kye-Dong Kwangwoon Univ Dept Smart Convergence Seoul 01897 South Korea Kwangwoon Univ Dept Immers Content Convergence Seoul 01897 South Korea Kwangwoon Univ Inst Informat Technol Seoul 01897 South Korea Kwangwoon Univ Ingenium Coll Liberal Arts Seoul 01897 South Korea

Generative Adversarial Networks (GANs) for 3D volume generation and reconstruction, such as shape generation, visualization, automated design, real-time simulation, and research applications, are receiving increased amounts of attention in various fields. However, challenges such as limited training data, high computational costs, and mode collapse issues persist. We propose combining a variational autoencoder (VAE) and a GAN to uncover enhanced 3D structures and introduce a stable and scalable progressive growth approach for generating and reconstructing intricate voxel-based 3D shapes. The cascade-structured network involves a generator and discriminator, starting with small voxel sizes and incrementally adding layers, while subsequently supervising the discriminator with ground-truth labels in each newly added layer to model a broader voxel space. Our method enhances the convergence speed and improves the quality of the generated 3D models through stable growth, thereby facilitating an accurate representation of intricate voxel-level details. Through comparative experiments with existing methods, we demonstrate the effectiveness of our approach in evaluating voxel quality, variations, and diversity. The generated models exhibit improved accuracy in 3D evaluation metrics and visual quality, making them valuable across various fields, including virtual reality, the metaverse, and gaming.

关键词： variational autoencoder generative adversarial network progressive neural network generation reconstruction voxel

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