Social Group Buying as a Marketing Strategy

Social group buying (SGB) is a novel form of group buying that encourages customers to purchase deeply discounted products together with friends. Over the past few years, SGB has become a popular marketing strategy for online sellers to acquire new customers. Using a dataset from an e-commerce platform, we investigate whether and how SGB affects the sales of sellers. We find that enrolling a few products into SGB has a positive spillover effect on the sales of the sellers’ other products, and the effect varies substantially across different types of sellers. Specifically, the positive spillover effect is larger for smaller sellers and more diversified sellers. Moreover, we find that the spillover effect exhibits similar heterogeneity at the brand level, except that it can be negative for large brands and non-diversified brands. This finding suggests that sellers may gain from SGB at the expense of large or non-diversified brands

Social Group Buying as a Marketing Strategy

Social group buying (SGB) is a novel form of group buying that encourages customers to purchase deeply discounted products together with friends. Over the past few years, SGB has become a popular marketing strategy for online sellers to acquire new customers. Using a dataset from an e-commerce platform, we investigate whether and how SGB affects the sales of sellers. We find that enrolling a few products into SGB has a positive spillover effect on the sales of the sellers’ other products, and the effect varies substantially across different types of sellers. Specifically, the positive spillover effect is larger for smaller sellers and more diversified sellers. Moreover, we find that the spillover effect exhibits similar heterogeneity at the brand level, except that it can be negative for large brands and non-diversified brands. This finding suggests that sellers may gain from SGB at the expense of large or non-diversified brands

AI Assistant in Online Pharmacy

Artificial intelligence (AI) has been increasingly popular in diagnosing diseases and recommending drugs in digital healthcare platforms. Leveraging the introduction of an AI-powered medical assistant to one drug category in an online pharmacy platform, we investigate how the adoption of AI affects users’ purchase behaviors using a difference-in-differences design. We find that the adoption of the AI assistant significantly increases users’ purchases in the platform, even for drugs not recommended by the AI assistant. Furthermore, we find that the positive effect of the AI assistant adoption is stronger for early technology adopters, inexperienced users, and users with higher privacy concerns, likely because these users tend to perceive higher value from AI. Finally, our mediation analysis shows that the AI feature increases users’ purchases by increasing their engagement levels in the platform. Our results have important implications for designing and evaluating AI features in online platforms

Environmental geological features of the red clay surrounding rock deformation under the influence of rock-fracture water

The development degree of fissure water in underground rock is a great trouble to the construction of railway tunnel, which will cause a series of environmental geological problems. Take the surrounding rock-section of the typical red clay in Lvliang-Mt. railway tunnel below the underground water level as an example, several aspects about the red clay surrounding rock will be researched, including pore water pressure, volume moisture content, stress of surrounding rock, vault subsidence and horizontal convergence through the field monitoring. Taking into account the importance of railway tunnel engineering, the large shear test of red clay was carried out at the construction site specially and the reliable situ shear strength parameters of surrounding rock will be obtained. These investigations and field tests helped to do a series of work: Three dimensional finite element numerical model of railway tunnel will be established, the deformation law of the red clay surrounding rock will be investigated, respectively, for the water-stress coupling effect and without considering it, the variation of the pore water pressure during excavation, the influence degree about the displacement field and stress field of water-stress coupling on red clay-rock will be discussed and the mechanism of the surrounding rock deformation will be submitted. Finally, the paper puts forward the feasible drainage scheme of the surrounding rock and the tunnel cathode. The geological environment safety of tunnel construction is effectively protected

Auto-tuning Streamed Applications on Intel Xeon Phi

Many-core accelerators, as represented by the XeonPhi coprocessors and GPGPUs, allow software to exploit spatial and temporal sharing of computing resources to improve the overall system performance. To unlock this performance potential requires software to effectively partition the hardware resource to maximize the overlap between host-device communication and accelerator computation, and to match the granularity of task parallelism to the resource partition. However, determining the right resource partition and task parallelism on a per program, per dataset basis is challenging. This is because the number of possible solutions is huge, and the benefit of choosing the right solution may be large, but mistakes can seriously hurt the performance. In this paper, we present an automatic approach to determine the hardware resource partition and the task granularity for any given streamed application, targeting the Intel XeonPhi architecture. Instead of hand-crafting the heuristic for which the process will have to repeat for each hardware generation, we employ machine learning techniques to automatically learn it. We achieve this by first learning a predictive model offline using training programs; we then use the learned model to predict the resource partition and task granularity for any unseen programs at runtime. We apply our approach to 23 representative parallel applications and evaluate it on a CPU-XeonPhi mixed heterogenous many-core platform. Our approach achieves, on average, a 1.6x (upto 5.6x) speedup, which translates to 94.5% of the performance delivered by a theoretically perfect predictor

A Semi-supervised Graph Attentive Network for Financial Fraud Detection

With the rapid growth of financial services, fraud detection has been a very important problem to guarantee a healthy environment for both users and providers. Conventional solutions for fraud detection mainly use some rule-based methods or distract some features manually to perform prediction. However, in financial services, users have rich interactions and they themselves always show multifaceted information. These data form a large multiview network, which is not fully exploited by conventional methods. Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection. To address the problem, we expand the labeled data through their social relations to get the unlabeled data and propose a semi-supervised attentive graph neural network, namedSemiGNN to utilize the multi-view labeled and unlabeled data for fraud detection. Moreover, we propose a hierarchical attention mechanism to better correlate different neighbors and different views. Simultaneously, the attention mechanism can make the model interpretable and tell what are the important factors for the fraud and why the users are predicted as fraud. Experimentally, we conduct the prediction task on the users of Alipay, one of the largest third-party online and offline cashless payment platform serving more than 4 hundreds of million users in China. By utilizing the social relations and the user attributes, our method can achieve a better accuracy compared with the state-of-the-art methods on two tasks. Moreover, the interpretable results also give interesting intuitions regarding the tasks.Comment: icd

Demineralized dentin matrix promotes gingival healing in alveolar ridge preservation of premolars extracted for orthodontic reason: a split-mouth study

ObjectiveThe purpose of this study was to prospectively evaluate the efficacy of a demineralized dentin matrix (DDM) in decreasing the initial inflammatory response of the gingiva and facilitating the repair and regeneration of soft tissue in alveolar ridge preservation.MethodsThis clinical study employed a split-mouth design. Fourteen patients with a total of forty-four sites underwent extraction and alveolar ridge preservation (ARP) procedures. A Bilaterally symmetrical extraction operation were conducted on the premolars of each patient. The experimental group received DDM as a graft material for ARP, while the control group underwent natural healing. Within the first month postoperatively, the pain condition, color, and swelling status of the extraction sites were initially assessed at different time points Subsequently, measurements were taken for buccal gingival margin height, buccal-lingual width, extraction socket contour, and the extraction socket area and healing rate were digitally measured. Additionally, Alcian Blue staining was used for histological evaluation of the content during alveolar socket healing.ResultsBoth groups experienced uneventful healing, with no adverse reactions observed at any of the extraction sites. The differences in VAS pain scores between the two groups postoperatively were not statistically significant. In the early stage of gingival tissue healing (3 days postoperatively), there were statistically significant differences in gingival condition and buccal gingival margin height between the two groups. In the later stage of gingival tissue healing (7, 14, and 30 days postoperatively), there were statistically significant differences in buccal-lingual width, extraction socket healing area, and healing rate between the two groups. Furthermore, the histological results from Alcian Blue staining suggested that the experimental group may play a significant role in promoting gingival tissue healing, possibly by regulating inflammatory responses when compared to the control group.ConclusionThe application of DDM in alveolar ridge preservation has been found to diminish initial gingival inflammation after tooth extraction. Additionally, it has shown the ability to accelerate early gingival soft tissue healing and preserve its anatomical contour.Clinical trial registrationchictr.org.cn, identifier ChiCTR2100050650