Integrated label correction for e-commerce data: Boosting accuracy with baseline attention and enhanced Bayesian updating

The rise of e-commerce has seen a surge in personalized recommendation systems, greatly enhancing user experience and boosting sales.This paper explores the integration of multimodal data in e-commerce recommendations and the privacy challenges it brings.Multimodal data, combining different sensory inputs, offers a rich source for recommendations but also raises concerns about data fusion and privacy.To address these challenges, the paper suggests strategies like differential privacy, federated learning, encryption, access control, and anonymization.These approaches aim to maintain effective recommendations while safeguarding user privacy.Looking ahead, the paper discusses future trends in multimodal data privacy, including emerging technologies, standardization, and user education.

Automated sentimental analysis using heuristic-based CNN-BiLSTM for E-commerce dataset

With the rapid development of economy, Online shopping has been quickly favored by the vast number of consumers of all ages due to its convenience. But with online shopping business’s continuous growing, many companies are beginning to be faced with competitive pressure and slowed revenue growth.In the commercial competition, data analysis has become an effective mean to auxiliary operations.In this context, by building E-commerce data analysis platform Based on Hadoop, this model used the parallel computing capability of Hadoop platform to analyze and deal with E-commerce data. So as to give enterprises a clearer understanding of the customer's interest preferences,and then provide consumers with products or services which are more targeted and meet the actual needs of them better. Thus it will help promote products and enhance competitiveness in the new situation.The test results showed that this model could achieve E-commerce data processing, and analysis of consumers’ consume behavior.

Web search engines such as Google and Bing provide an easy and convenient way to find web pages that contain input keywords. This provides a user-friendly interface for non-technical users to explore the Web and find relevant data among thousands of Web pages. While numerous advancement has been made to store e-commerce data in the cloud, we have not seen great advancement in terms of search over such data. E-commerce data is usually stored as structured data in relational and graph databases. Thus, an answer to a query keyword is composed of different pieces of data stitched together. As of now, the main method to find answers over this structured data is through predefined search forms. However, these search forms are limited, and developing a new search form is time consuming and expensive. In this work, we present an easy way to explore structured e-commerce data for business users that eliminate the dependency to predefined forms. The new search system is similar to Google, in which the interface is essentially a text box, and non-technical business users enter input keywords into the system. The output is a portion of the data, that covers the input keywords. We propose a new ranking strategy based on machine learning to rank more relevant answers ahead of less relevant ones. Our experiments show this ranking strategy is successful in returning relevant answers.

Most existing recommender systems leverage user behavior data of one type only, such as the purchase behavior in E-commerce that is directly related to the business KPI (Key Performance Indicator) of conversion rate. Besides the key behavioral data, we argue that other forms of user behaviors also provide valuable signal, such as views, clicks, adding a product to shop carts and so on. They should be taken into account properly to provide quality recommendation for users. In this work, we contribute a new solution named NMTR (short for Neural Multi-Task Recommendation) for learning recommender systems from user multi-behavior data. We develop a neural network model to capture the complicated and multi-type interactions between users and items. In particular, our model accounts for the cascading relationship among different types of behaviors (e.g., a user must click on a product before purchasing it). To fully exploit the signal in the data of multiple types of behaviors, we perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task. Extensive experiments on two real-world datasets demonstrate that NMTR significantly outperforms state-of-the-art recommender systems that are designed to learn from both single-behavior data and multi-behavior data. Further analysis shows that modeling multiple behaviors is particularly useful for providing recommendation for sparse users that have very few interactions.

Purpose: This paper summarizes and compares the types and interpretations of the basic attributes necessary to enter product data in selected e-commerce platforms. Design/Methodology/Approach: The research methodology was based on an analysis and selection of a reference group of basic product attributes and identification of appropriate market representatives, platforms and tools commonly used in e-commerce. Furthermore, for each of the selected basic attributes of the product, an analysis was made in terms of the presence, mandatory field, and data input validators. The best practices indicated by the platform developers were also reviewed.

In the ever-changing e-commerce landscape, modern businesses are progressively utilising advanced technology to develop personalised marketing strategies that improve client engagement and satisfaction. This article investigates the utilisation of AI, the Internet of Things (IoT), and big data analytics by e-commerce platforms to develop personalised marketing strategies. Organisations can refine marketing initiatives, anticipate client preferences, and offer personalised recommendations by integrating AI-driven analytics and real-time data from IoT devices. This results in a more pleasurable purchasing experience and improved conversion rates. This article provides a comprehensive framework for companies that are seeking to enhance their marketing strategies by leveraging technological advancements. It does so by examining the current trends, challenges, and potential impacts of the convergence of AI and IoT on e-commerce.

<p>Both the level of science and technology and people’s economic level have been significantly improved in the present social background. With the application and popularization of computers and networks in people’s homes, online shopping, which is of high flexibility and convenience, has emerged and gradually become an important choice to shop. In e-commerce activities, the trust between the two parties often directly affects the results of online transactions, which is even a very critical influencing factor. Therefore, an effective trust evaluation model will make a positive impact on the healthy and sustainable development of e-commerce activities. Based on the trust evaluation model of reputation that has been applied in practice, this article will integrate D-S evidence fusion algorithm into the new evaluation model and try to analyze its unique value in application.</p>

The article is devoted to the analysis of the Big Data concept, consumer behavior analytics, and personalization in e-commerce, using case studies of transnational companies. The role of Big Data in shaping the competitive advantages of e-commerce is examined, in particular in enhancing the accuracy of marketing decisions, optimizing customer interaction, and increasing conversion rates. The key components of Big Data in e-commerce are identified, including behavioral, transactional, socio-demographic, and content data, as well as their significance for building segmentation models and demand forecasting. Approaches to consumer behavior analytics are considered in detail, ranging from descriptive and diagnostic to predictive and prescriptive, along with tools for data collection and processing, such as web analytics, cohort analysis, and customer relationship management systems. Architectures and algorithms for personalization are analyzed, including rule-based recommendation systems, collaborative filtering, and hybrid models using deep learning. Special attention is given to challenges and risks related to personal data privacy, algorithmic bias, and the transparency of recommendation systems. It is emphasized that the implementation of Big Data analytics and personalization in electronic commerce contributes not only to improving the operational and strategic efficiency of business processes but also to enhancing the quality of customer interaction, increasing user engagement, and fostering long-term customer loyalty. Practical recommendations are proposed for the development of a robust analytical infrastructure, the refinement and optimization of personalization algorithms to achieve higher accuracy and relevance, and the establishment of regulatory and organizational measures aimed at ensuring the ethical and transparent use of customer data. Ethical aspects of corporative marketing decisions are lightened. Personalization of commercials and marketing as a main modern tool of corporate development is lightened. Main risks of modern consumption under digitalization are described.

The Privileges for the machine learning algorithms are actuality to lead the online exchange world with their does and power to match the fully customizable plot involvement. From expectable actions to social media to perceptive ways to shop online, big data quickly suppers within and is used frequently. Unfortunately, rare data conditions, called algebraic data, are still essentially unnoticed due to the uncommon of analytics method designed to manage the static data sets and external "quickening". As a result, very important rare functioning condition are being mistreated and leads to high cost loss Over the past applies, a number of ways to address the issue raised ranging from data processing to changes of the present algorithms having appeared with changing success. Though, most of them having major downsides when there are used indifferent claims domains due to the different nature of the active data presence. In the big E-Commerce domain, we suggest an unabled at a management methods, which consists of diverse subdivision solution that mix multi-methods data systems and algorithms to create the right balance between forecasts, accuracy and precision. Our solution helps in improving data service ability, class accuracy and results in cost analysis of large data sets used to mark client cognizance in E-Commerce.

Internet is one of the media facilities that is not only used for communication, but is also used in the process of buying and selling or trading (e-commerce). Behind all the conveniences obtained, e-commerce also raises the issue of consumer concerns on the responsibility for personal data. This study aimed to examine and re-explain the urgency of protecting personal data of e-commerce consumers and focused on the challenges and legal certainty of protecting personal data of e-commerce consumers in Indonesia. The method used was a doctrinal research method. This study showed that the application of data in e-commerce provides many benefits. However, the challenge was the absence of binding laws to safeguard personal data submitted to e-commerce companies. Legal certainty for the protection of e-commerce consumers is currently contained in several laws and regulations. Therefore, it is necessary to improve the effectiveness in the implementation of personal data protection and to regulate the protection of personal data in a law. In Indonesia, the urgency of the ratification of the Personal Data Protection Bill may become a solution in providing legal certainty for the protection of e-commerce consumers on their personal data.

Association Analysis of Pet Foods with Diseases in dogs Using E-Commerce Data

The demand for real-time e-commerce data services has been increasing recently. In many e-commerce applications, it is essential to process user requests within their deadlines, i.e., before the market status changes, using fresh data reflecting the current market status. However, current data services are poor at processing user requests in a timely manner using fresh data. To address this problem, we present a differentiated real-time data service framework for e-commerce applications. User requests are classified into several service classes according to their importance, and they receive differentiated real-time performance guarantees in terms of deadline miss ratio. At the same time, a certain data freshness is guaranteed for all transactions that commit within their deadlines. A feedback-based approach is applied to differentiate the deadline miss ratio among service classes. Admission control and adaptable update schemes are applied to manage potential overload. A simulation study, which reflects the e-commerce data semantics, shows that our approach can achieve a significant performance improvement compared to baseline approaches. Our approach can support the specified per-class deadline miss ratios maintaining the required data freshness even in the presence of unpredictable workloads and data access patterns, whereas baseline approaches fail.

Recently, graph neural networks (GNNs) have been improved under the influence of various deep learning techniques, such as attention, autoencoders, and recurrent networks. However, real-world graphs may have multiple types of vertices and edges, such as graphs of social networks, citation networks, and e-commerce data. In these cases, most GNNs that consider a homogeneous graph as input data are not suitable because they ignore the heterogeneity. Meta-path-based methods have been researched to capture both heterogeneity and structural information of heterogeneous graphs. As a meta-path is a type of graph pattern, we extend the use of meta-paths to exploit graph patterns. In this study, we propose TP-HAN, a heterogeneous graph attention network for exploiting triangle patterns. In the experiments using DBLP and IMDB, we show that TP-HAN outperforms the state-of-the-art heterogeneous graph attention network.KeywordsGraph neural networksHeterogeneous graphTriangle

In this paper, we introduce a novel multimodal fashion search paradigm where e-commerce data is searched with a multimodal query composed of both an image and text. In this setting, the query image shows a fashion product that the user likes and the query text allows to change certain product attributes to fit the product to the user's desire. Multimodal search gives users the means to clearly express what they are looking for. This is in contrast to current e-commerce search mechanisms, which are cumbersome and often fail to grasp the customer's needs. Multimodal search requires intermodal representations of visual and textual fashion attributes which can be mixed and matched to form the user's desired product, and which have a mechanism to indicate when a visual and textual fashion attribute represent the same concept. With a neural network, we induce a common, multimodal space for visual and textual fashion attributes where their inner product measures their semantic similarity. We build a multimodal retrieval model which operates on the obtained intermodal representations and which ranks images based on their relevance to a multimodal query. We demonstrate that our model is able to retrieve images that both exhibit the necessary query image attributes and satisfy the query texts. Moreover, we show that our model substantially outperforms two state-of-the-art retrieval models adapted to multimodal fashion search.