An intelligent approach to design of E-Commerce metasearch and ranking system using next-generation big data analytics

Malhotra, Dheeraj; Rishi, O. P.

doi:10.1016/j.jksuci.2018.02.015

Cited by 36 publications

(18 citation statements)

References 28 publications

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“…Replacing the market mechanism partly with the enterprise mechanism on the e-commerce platform can reduce the transaction cost. Malhotra and Rishi [7] e-commerce transactions are conducted based on computer networks, bringing great convenience to the majority of traders, confirming the identity information of both parties in the transaction and ensuring the security of the transaction. Bin Li built an electronic commerce transaction system based on PKI, analyzed its transaction process, and proposed information security thought based on Java2 system for data security and antifraud in online transaction.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Big Data‐Based E‐Commerce Transaction Information Collection Method

Yan

et al. 2021

Complexity

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With the rapid development of e-commerce industry, online shopping has become a craze. With the rapid growth of transaction volume on e-commerce platforms, a large amount of transaction data has been accumulated. From the transaction information of these users, a lot of very valuable information can be mined, such as the defects of products and the actual needs of users. In view of the existing e-commerce transaction information collection method is not mature, in this paper, the electric business platform system architecture planning and design increases the function management module. In this paper, a new Naive Bayes model is established by using HBase distributed database instead of traditional database. Based on the optimization and extraction of the important transaction information in the product, the dataset of e-commerce transaction information is updated. Through the efficiency test of the collection method, the information scalability ability test, and the accuracy test, the important context was sorted out after integration, the sources of trading information were sorted out, and the data analysis of the collected information was conducted to optimize the information collection method and verify the feasibility of the method.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Big Data‐Based E‐Commerce Transaction Information Collection Method

Yan

et al. 2021

Complexity

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“…• "Adaptive look-alike targeting in social networks advertising" [16] • "An affordance perspective of enterprise social media and organizational socialization" [17] • "An icon that everyone wants to click: How perceived aesthetic qualities predict app icon successfulness" [18] • "An intelligent approach to design of E-Commerce metasearch and ranking system using next-generation big data analytics" [19] A total of 54 full-text articles are assessed for eligibility in the following step. A total of 17 articles, which receive scientific findings linked to the research question of this paper, are tabulated with aim, outcomes, methods, context and, where appropriate, a link to the issue raised here, and included in the qualitative synthesis.…”

Section: Methodsmentioning

confidence: 99%

Consumer Acceptance of App Push Notifications: Systematic Review on the Influence of Frequency

Wohllebe

2020

Int. J. Interact. Mob. Technol.

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Using push notifications, companies can address their smartphone app users with advertising messages. Each of these notifications can mean added value for smartphone users in terms of content, but also disruption and interruption. It is therefore important for advertisers to understand how the frequency of message delivery influences consumer acceptance. The systematic review is based on the PRISMA method and initially identifies a total of 18,725 potentially relevant scientific papers. The 17 research articles included in the qualitative synthesis show that push notifications are suitable for encouraging users to use an app and establish new habits. App usage increases with frequency, with particularly active users tolerating higher frequencies. At the same time it is also shown that too high a frequency can be perceived as disturbing and users should therefore be given the opportunity to determine the frequency with which they receive notifications. A gap in research has been identified in particular in studies that look at real observed behaviour of app users instead of behaviour reported via questionnaires and examine the interaction of frequency, content and presentation of notifications.

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“…The comprehensive development of e-commerce and the expansion of establishing electronic markets for online shopping like Amazon, eBay, Walmart, Best Buy, Wish, etc., have created a competitive environment between companies to attract the highest number of customers. Enterprises seek to measure several factors like customer satisfaction, loyalty, and the success of marketing strategies by analyzing customer reviews through electronic platforms such as websites and social media [133][134][135][136]. But the analysis process must involve excluding fake reviews and negative comments released by competitors [137].…”

Section: Marketing and E-commercementioning

confidence: 99%

Big Data: Analytical Solutions, Research Challenges and Trends

Ali¹,

Novikov²

2020

Proceedings of ISP RAS

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The term Big Data refers to an extensive collections of digital data generating every second. Produced datasets come in structured, semi-structured, and unstructured formats throughout the world, which is difficult for the traditional database management systems to analyze. Recently, big data analytics emerges as an essential research area due to the popularity of the Internet and the advent of new Web technologies. This growing area of research represents a multidisciplinary that attracts researchers from various research fields. Interested researchers are invited to design, develop, and implement several tools, technologies, architecture, and platforms for analyzing these large volumes of data. This paper begins with a brief introduction to big data and related concepts, including the main characteristics of big data, followed by discussions of the most significant open research challenges and emerging trends. Next, we review a study of big data analytics, the advantages of using big data solutions, and the preliminary assessments required before migrating from traditional solutions. Finally, we present a review of the recent main applications to obtain a broad perspective of big data analytics.

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An intelligent approach to design of E-Commerce metasearch and ranking system using next-generation big data analytics

Cited by 36 publications

References 28 publications

[Retracted] Big Data‐Based E‐Commerce Transaction Information Collection Method

[Retracted] Big Data‐Based E‐Commerce Transaction Information Collection Method

Consumer Acceptance of App Push Notifications: Systematic Review on the Influence of Frequency

Big Data: Analytical Solutions, Research Challenges and Trends

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