The Effect of E-commerce on The International Trade of Small and Medium Enterprises in China

In modern world, economic globalization is the trend of the economic development. In order to adapt to the trend of economic globalization and survive, enterprises have to do the international trade to improve their competitive ability, especially for the small and medium enterprises(SMEs). SMEs do not have good way to get economic information and cannot sell their products in big markets like the big enterprises. The development of the e-commerce provides great opportunity for the SMEs to do the international trade. As we all know that China is called the factory of the world now. China\u27s products are sold in the whole word and the small and medium enterprises\u27 goods account for a large proportion. 21st century is the information age. The small and medium enterprises should use the e-commerce to do the international trade and expand their markets. The small and medium enterprises in China have their special characteristics and China has special policy. This research talks about the characteristics of the international trade of small and medium enterprises in China. Then it discusses how e-commerce is developed in China. By considering these information it discusses how e-commerce effects the international trade of small and medium enterprises in China

The calendar effect of Dutch Auction on Gongtianxia’s agricultural products

In recent years, agricultural e-commerce sales model is in full swing. As the nation’s largest B2C e-commerce company of agricultural products, ShanXi GongTianXia E-commerce Co., Ltd. (hereinafter referred to as GongTianXia ) launched the 7-day auction and 15-minute auction since the end of 2014 on their official Wechat platform. Through these two new sales patterns, GongTianXia wants to attract more customers’ attention to agricultural products, and thus lead to greater trade volume and profits.Many studies have shown that calendar effect has a wide range of use in financial markets. Likewise, as temporal-series data, did GongTianXia’s two kinds of price reduction auctions have similarities with the laws of financial markets? There’s no research to prove it so far. Taking GongTianXia’s over 200,000 transactions data occurred during 432 instances of “7-day auction” and 943 instances of “15-minute auction” within 2015, this paper discusses the impacts of different periods of “significant time points” under both “7-day auction” and “15-minute auction” on different types of commodities, then explain why those results may happen. With the fin dings, we can improve calender effect theory and make a theoretical complement for Dutch auction as Mobile commerce, and give more optimization advices on mobile commerce companies

PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information

With the increase of content pages and interactive buttons in online services such as online-shopping and video-watching websites, industrial-scale recommender systems face challenges in multi-domain and multi-task recommendations. The core of multi-task and multi-domain recommendation is to accurately capture user interests in multiple scenarios given multiple user behaviors. In this paper, we propose a plug-and-play \textit{\textbf{P}arameter and \textbf{E}mbedding \textbf{P}ersonalized \textbf{Net}work (\textbf{PEPNet})} for multi-domain and multi-task recommendation. PEPNet takes personalized prior information as input and dynamically scales the bottom-level Embedding and top-level DNN hidden units through gate mechanisms. \textit{Embedding Personalized Network (EPNet)} performs personalized selection on Embedding to fuse features with different importance for different users in multiple domains. \textit{Parameter Personalized Network (PPNet)} executes personalized modification on DNN parameters to balance targets with different sparsity for different users in multiple tasks. We have made a series of special engineering optimizations combining the Kuaishou training framework and the online deployment environment. By infusing personalized selection of Embedding and personalized modification of DNN parameters, PEPNet tailored to the interests of each individual obtains significant performance gains, with online improvements exceeding 1\% in multiple task metrics across multiple domains. We have deployed PEPNet in Kuaishou apps, serving over 300 million users every day.Comment: Accepted by KDD 202

Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation

Sequential recommendation is one of the most important tasks in recommender systems, which aims to recommend the next interacted item with historical behaviors as input. Traditional sequential recommendation always mainly considers the collected positive feedback such as click, purchase, etc. However, in short-video platforms such as TikTok, video viewing behavior may not always represent positive feedback. Specifically, the videos are played automatically, and users passively receive the recommended videos. In this new scenario, users passively express negative feedback by skipping over videos they do not like, which provides valuable information about their preferences. Different from the negative feedback studied in traditional recommender systems, this passive-negative feedback can reflect users' interests and serve as an important supervision signal in extracting users' preferences. Therefore, it is essential to carefully design and utilize it in this novel recommendation scenario. In this work, we first conduct analyses based on a large-scale real-world short-video behavior dataset and illustrate the significance of leveraging passive feedback. We then propose a novel method that deploys the sub-interest encoder, which incorporates positive feedback and passive-negative feedback as supervision signals to learn the user's current active sub-interest. Moreover, we introduce an adaptive fusion layer to integrate various sub-interests effectively. To enhance the robustness of our model, we then introduce a multi-task learning module to simultaneously optimize two kinds of feedback -- passive-negative feedback and traditional randomly-sampled negative feedback. The experiments on two large-scale datasets verify that the proposed method can significantly outperform state-of-the-art approaches. The code is released at https://github.com/tsinghua-fib-lab/RecSys2023-SINE.Comment: Accepted by RecSys'2

Distribution and expression of SLC45A2 in the skin of sheep with different coat colors

Introduction. To investigate whether the membrane-associated transporter protein SLC45A2 is differentially expressed in the skin of sheep with different coat colors and to determine its correlation with coat color establishment in sheep. Material and methods. The expression of SLC45A2 in sheep skin samples with different coat colors was qualitatively and quantitatively analyzed by PCR amplification, RT-PCR, immunohistochemical staining and Western blotting. Results. A 193-bp SLC45A2 CDS sequence was successfully amplified from sheep skin samples with diverse coat colors. RT-PCR analysis revealed that SLC45A2 mRNA was expressed in all sheep skin samples tested, with relative expression levels of 512.74 ± 121.51 in black skin, 143.38 ± 119.31 and 1.36 ± 0.09 in black dots and white dots of piebald skin, respectively, and 1.02 ± 0.23 in white skin (p < 0.01**). Positive SLC45A2 protein bands were also detected in all skin samples by Western blot analysis, with relative expression levels of 0.85 ± ± 0.17** in black skin, 0.60 ± 0.05** and 0.34 ± 0.07 in black dots and white dots of piebald skin, respectively, and 0.20 ± 0.05 in white skin (p < 0.01**). Immunohistochemical assays revealed that SLC45A2 was expressed in the hair follicle matrix, the inner and outer root sheath, and the dermal papilla in the skin tissues with different coat colors. These patterns were quantified by optical density (OD) analysis, which yielded relative expression levels of 0.23 ± 0.11 in black skin, 0.19 ± 0.09 and 0.10 ± 0.03 in black dots and white dots of piebald skin, respectively, and 0.08 ± 0.01 in white skin (p < 0.05*). Conclusion. SLC45A2 is detectably expressed in sheep skin of all coat colors, though at significantly different levels. SLC45A2 may participate in the establishment of coat color by regulating the synthesis and trafficking of melanin.

TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou

Life-long user behavior modeling, i.e., extracting a user's hidden interests from rich historical behaviors in months or even years, plays a central role in modern CTR prediction systems. Conventional algorithms mostly follow two cascading stages: a simple General Search Unit (GSU) for fast and coarse search over tens of thousands of long-term behaviors and an Exact Search Unit (ESU) for effective Target Attention (TA) over the small number of finalists from GSU. Although efficient, existing algorithms mostly suffer from a crucial limitation: the \textit{inconsistent} target-behavior relevance metrics between GSU and ESU. As a result, their GSU usually misses highly relevant behaviors but retrieves ones considered irrelevant by ESU. In such case, the TA in ESU, no matter how attention is allocated, mostly deviates from the real user interests and thus degrades the overall CTR prediction accuracy. To address such inconsistency, we propose \textbf{TWo-stage Interest Network (TWIN)}, where our Consistency-Preserved GSU (CP-GSU) adopts the identical target-behavior relevance metric as the TA in ESU, making the two stages twins. Specifically, to break TA's computational bottleneck and extend it from ESU to GSU, or namely from behavior length $10^2$ to length $10^4-10^5$, we build a novel attention mechanism by behavior feature splitting. For the video inherent features of a behavior, we calculate their linear projection by efficient pre-computing \& caching strategies. And for the user-item cross features, we compress each into a one-dimentional bias term in the attention score calculation to save the computational cost. The consistency between two stages, together with the effective TA-based relevance metric in CP-GSU, contributes to significant performance gain in CTR prediction.Comment: Accepted by KDD 202

Mixed Attention Network for Cross-domain Sequential Recommendation

In modern recommender systems, sequential recommendation leverages chronological user behaviors to make effective next-item suggestions, which suffers from data sparsity issues, especially for new users. One promising line of work is the cross-domain recommendation, which trains models with data across multiple domains to improve the performance in data-scarce domains. Recent proposed cross-domain sequential recommendation models such as PiNet and DASL have a common drawback relying heavily on overlapped users in different domains, which limits their usage in practical recommender systems. In this paper, we propose a Mixed Attention Network (MAN) with local and global attention modules to extract the domain-specific and cross-domain information. Firstly, we propose a local/global encoding layer to capture the domain-specific/cross-domain sequential pattern. Then we propose a mixed attention layer with item similarity attention, sequence-fusion attention, and group-prototype attention to capture the local/global item similarity, fuse the local/global item sequence, and extract the user groups across different domains, respectively. Finally, we propose a local/global prediction layer to further evolve and combine the domain-specific and cross-domain interests. Experimental results on two real-world datasets (each with two domains) demonstrate the superiority of our proposed model. Further study also illustrates that our proposed method and components are model-agnostic and effective, respectively. The code and data are available at https://github.com/Guanyu-Lin/MAN.Comment: WSDM 202