Smart Retail, Replaces All? Some? : Different Influence of Amazon Go to Local Restaurant Industry.

Amazon Go, the pioneering smart retailer, has been opening physical stores in metropolitan areas of the USA, and seductively distracted customers from adjacent competitors by provisioning quick-and-easy service. This study focuses on how the appearance of the smart retailer affects adjacent competing businesses. We constructed a panel dataset with various features and reviews of restaurants from Yelp.com, and created two dummies, , one if the restaurant is in a certain radius of a smart retailer and zero outside, and , one after the introduction and zero before. By using Difference-in-Difference estimation, we find that (1) negative impacts on the adjacent restaurants after Amazon Go compared to non-adjacent and before the appearance, and (2) less negative impact on adjacent fine-dining restaurants than fast-food restaurants. After Amazon Go, customers’ sentiments about the adjacent restaurants have changed more negatively. This paper may provide businesses with useful implications for their strategies

Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection

With the explosive growth of e-commerce, online transaction fraud has become one of the biggest challenges for e-commerce platforms. The historical behaviors of users provide rich information for digging into the users' fraud risk. While considerable efforts have been made in this direction, a long-standing challenge is how to effectively exploit internal user information and provide explainable prediction results. In fact, the value variations of same field from different events and the interactions of different fields inside one event have proven to be strong indicators for fraudulent behaviors. In this paper, we propose the Dual Importance-aware Factorization Machines (DIFM), which exploits the internal field information among users' behavior sequence from dual perspectives, i.e., field value variations and field interactions simultaneously for fraud detection. The proposed model is deployed in the risk management system of one of the world's largest e-commerce platforms, which utilize it to provide real-time transaction fraud detection. Experimental results on real industrial data from different regions in the platform clearly demonstrate that our model achieves significant improvements compared with various state-of-the-art baseline models. Moreover, the DIFM could also give an insight into the explanation of the prediction results from dual perspectives.Comment: 11 pages, 4 figure

Understanding the Impact of Emotional Comments and Image on Resistance Intention and Participation: A Study of Taiwanese Consumers\u27 Buying

Consumer resistance behavior is becoming increasingly prevalent in the age of social media, and this study aims to investigate the influence of emotional comments on such behavior and its underlying mechanisms. To achieve this objective, an eye-tracking experiment was conducted, with online comments from actual users on a popular social media platform used as stimuli. The findings indicate that both positive and negative emotional comments are associated with resistance intention and resistance participation, which, in turn, affect consumers\u27 purchasing behavior. Product image was found to be linked to resistance intention, whereas brand image had little impact. Participants\u27 liking or disliking of a comment description may serve as a basis for their behavior. The study underscores the importance of prompt action by managers in addressing inappropriate behaviors in the face of resistance movements. They can accomplish this by highlighting the specific differences between the product before and after improvement and targeting young potential resistance groups to receive the brand\u27s message before they join the resistance movement

xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

Combinatorial features are essential for the success of many commercial models. Manually crafting these features usually comes with high cost due to the variety, volume and velocity of raw data in web-scale systems. Factorization based models, which measure interactions in terms of vector product, can learn patterns of combinatorial features automatically and generalize to unseen features as well. With the great success of deep neural networks (DNNs) in various fields, recently researchers have proposed several DNN-based factorization model to learn both low- and high-order feature interactions. Despite the powerful ability of learning an arbitrary function from data, plain DNNs generate feature interactions implicitly and at the bit-wise level. In this paper, we propose a novel Compressed Interaction Network (CIN), which aims to generate feature interactions in an explicit fashion and at the vector-wise level. We show that the CIN share some functionalities with convolutional neural networks (CNNs) and recurrent neural networks (RNNs). We further combine a CIN and a classical DNN into one unified model, and named this new model eXtreme Deep Factorization Machine (xDeepFM). On one hand, the xDeepFM is able to learn certain bounded-degree feature interactions explicitly; on the other hand, it can learn arbitrary low- and high-order feature interactions implicitly. We conduct comprehensive experiments on three real-world datasets. Our results demonstrate that xDeepFM outperforms state-of-the-art models. We have released the source code of xDeepFM at \url{https://github.com/Leavingseason/xDeepFM}.Comment: 10 page

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI