Dynamic preference elicitation of customer behaviours in e-commerce from online reviews based on expectation confirmation theory

Preference change, also known as preference drift, is one of the factors that online retailers need to consider to accurately collect consumer preferences and make personalised recommendations. Online reviews have been widely used to analyse the preference drift of consumers. However, previous studies on online reviews ignored the psychological perceptions of consumers in terms of satisfaction. This paper aims to develop a method for dynamic preference elicitation from online reviews based on exploring the theory of consumer satisfaction formation. Based on the framework of expectation confirmation theory, we develop formulas for expressing the relations among expectation, perceived performance, confirmation, and satisfaction. We then use the proposed dynamic preference elicitation model to predict the change of consumer overall preference after each review and rank products for consumers’ next purchase. We test the proposed approach with a case study based on a data set from Amazon.com. It is founded that the satisfaction changes in each purchase, and this change will affect the prediction of the next product ranking. The case study is based on one product group, and further research is needed to see if the operation of the proposed method can be extended to other kinds of product

Plasmonic nano-resonator enhanced one-photon luminescence from single gold nanorods

Strong Stokes and anti-Stokes one-photon luminescence from single gold nanorods is measured in experiments. It is found that the intensity and polarization of the Stokes and anti-Stokes emissions are in strong correlation. Our experimental observation discovered a coherent process in light emission from single gold nanorods. We present a theoretical mode, based on the concept of cavity resonance, for consistently understanding both Stokes and anti-Stokes photoluminescence. Our theory is in good agreement of all our measurements.Comment: 14 pages, 7 figures, 2 table

A Remanufacturing News-vendor with Pricing and Take-back Pricing

This paper analyzes the problem of a remanufacturing news-vendor with selling and take-back price decision. In our model, the remanufacturer decides selling price, take-back price, and order quantity for new materials. She then uses the stochastic take-back quantity and the new material to meet the stochastic demand comparably to a news vendor setting. We allow demand and take-back supply to be correlated. In this thesis, we study a production problem with dual input sources: raw materials and recycled or remanufactured take-back items. To answer when mixed-sourcing is best, we analyze the model under deterministic setting first, provide criteria for different sourcing strategies, and give corresponding joint optimal solutions. Assuming that a mixed strategy is optimal, we then analyze the stochastic case, and find the optimal joint decision for raw-material order quantity, selling product price and take-back price. We find that, when the selling price remains fixed, the optimal take-back price and thus the expected take-back quantity does not change with increased demand and take-back supply variance. Also, the takeback price can exceed the net savings achieved by remanufacturing if consumers take this price into account when purchasing new products. And, the adding of randomness of demand and take-back supply will lower the optimal selling price and thus lower the take-back price. In future research, we will provide numerical analysis to report the impact and performance if a required recycling level is imposed in the problem; study the remanufacture problems with multiplicative demand function; multiple customer classes, such as the trade-in consideration; or multiple order opportunities, such as postponing the raw material procurement

Soft Computing Techniques and Their Applications in Intel-ligent Industrial Control Systems: A Survey

Soft computing involves a series of methods that are compatible with imprecise information and complex human cognition. In the face of industrial control problems, soft computing techniques show strong intelligence, robustness and cost-effectiveness. This study dedicates to providing a survey on soft computing techniques and their applications in industrial control systems. The methodologies of soft computing are mainly classified in terms of fuzzy logic, neural computing, and genetic algorithms. The challenges surrounding modern industrial control systems are summarized based on the difficulties in information acquisition, the difficulties in modeling control rules, the difficulties in control system optimization, and the requirements for robustness. Then, this study reviews soft-computing-related achievements that have been developed to tackle these challenges. Afterwards, we present a retrospect of practical industrial control applications in the fields including transportation, intelligent machines, process industry as well as energy engineering. Finally, future research directions are discussed from different perspectives. This study demonstrates that soft computing methods can endow industry control processes with many merits, thus having great application potential. It is hoped that this survey can serve as a reference and provide convenience for scholars and practitioners in the fields of industrial control and computer science

An Efficient Source Model Selection Framework in Model Databases

With the explosive increase of big data, training a Machine Learning (ML) model becomes a computation-intensive workload, which would take days or even weeks. Thus, reusing an already trained model has received attention, which is called transfer learning. Transfer learning avoids training a new model from scratch by transferring knowledge from a source task to a target task. Existing transfer learning methods mostly focus on how to improve the performance of the target task through a specific source model, and assume that the source model is given. Although many source models are available, it is difficult for data scientists to select the best source model for the target task manually. Hence, how to efficiently select a suitable source model in a model database for model reuse is an interesting but unsolved problem. In this paper, we propose SMS, an effective, efficient, and flexible source model selection framework. SMS is effective even when the source and target datasets have significantly different data labels, and is flexible to support source models with any type of structure, and is efficient to avoid any training process. For each source model, SMS first vectorizes the samples in the target dataset into soft labels by directly applying this model to the target dataset, then uses Gaussian distributions to fit for clusters of soft labels, and finally measures the distinguishing ability of the source model using Gaussian mixture-based metric. Moreover, we present an improved SMS (I-SMS), which decreases the output number of the source model. I-SMS can significantly reduce the selection time while retaining the selection performance of SMS. Extensive experiments on a range of practical model reuse workloads demonstrate the effectiveness and efficiency of SMS

Inelastic Scattering of Dark Matter with Heavy Cosmic Rays

We investigate the impact of inelastic collisions between dark matter (DM) and heavy cosmic ray (CR) nuclei on CR propagation. We approximate the fragmentation cross-sections for DM-CR collisions using collider-measured proton-nuclei scattering cross-sections, allowing us to assess how these collisions affect the spectra of CR Boron and Carbon. We derive new CR spectra from DM-CR collisions by incorporating these DM-CR cross-sections into the source terms and solving the diffusion equation for the complete network of reactions involved in generating secondary species. Utilizing the latest data from AMS-02 and DAMPE on the Boron-to-Carbon ratio, we estimate a 95\% upper limit for the effective inelastic cross-section of DM-proton as a function of DM mass. Our findings reveal that at $m_\chi \simeq 2 \,\rm{MeV}$, the effective inelastic cross-section between DM and protons must be less than $\mathcal{O}(10^{-32})~{\rm cm}^2$.Comment: 25 pages, 8 figure

An overview of fuzzy techniques in supply chain management: bibliometrics, methodologies, applications and future directions

Every practice in supply chain management (SCM) requires decision making. However, due to the complexity of evaluated objects and the cognitive limitations of individuals, the decision information given by experts is often fuzzy, which may make it difficult to make decisions. In this regard, many scholars applied fuzzy techniques to solve decision making problems in SCM. Although there were review papers about either fuzzy methods or SCM, most of them did not use bibliometrics methods or did not consider fuzzy sets theory-based techniques comprehensively in SCM. In this paper, for the purpose of analyzing the advances of fuzzy techniques in SCM, we review 301 relevant papers from 1998 to 2020. By the analyses in terms of bibliometrics, methodologies and applications, publication trends, popular methods such as fuzzy MCDM methods, and hot applications such as supplier selection, are found. Finally, we propose future directions regarding fuzzy techniques in SCM. It is hoped that this paper would be helpful for scholars and practitioners in the field of fuzzy decision making and SCM