Integrating E-Commerce and Data Mining: Architecture and Challenges

We show that the e-commerce domain can provide all the right ingredients for successful data mining and claim that it is a killer domain for data mining. We describe an integrated architecture, based on our expe-rience at Blue Martini Software, for supporting this integration. The architecture can dramatically reduce the pre-processing, cleaning, and data understanding effort often documented to take 80% of the time in knowledge discovery projects. We emphasize the need for data collection at the application server layer (not the web server) in order to support logging of data and metadata that is essential to the discovery process. We describe the data transformation bridges required from the transaction processing systems and customer event streams (e.g., clickstreams) to the data warehouse. We detail the mining workbench, which needs to provide multiple views of the data through reporting, data mining algorithms, visualization, and OLAP. We con-clude with a set of challenges.Comment: KDD workshop: WebKDD 200

A Study of Customer Behaviour Through Web Mining

Web mining is the extraction of interesting and potentially useful patterns and hidden information from web documents and web activities by applying data mining technology. The most important challenge of electronic commerce (E-commerce) is to understand as much as possible the customers ’ wants, desires, and buying patterns to ensure competitiveness in the E-commerce era. Nowadays, any information related to consumer behavior has an important value in the highly competitive nature of the E-commerce market. Therefore, web mining can be used to find those obvious data that have potential value to reduce competition and simultaneously increase business profit. This paper aims to study the classification of web mining to extract customer behavior in E-commerce, investigate customer behavior through the techniques and processes of the web data mining used, explore the application of web mining in E-commerce, and increase profit

Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation

The growing expanse of e-commerce and the widespread availability of online databases raise many fears regarding loss of privacy and many statistical challenges. Even with encryption and other nominal forms of protection for individual databases, we still need to protect against the violation of privacy through linkages across multiple databases. These issues parallel those that have arisen and received some attention in the context of homeland security. Following the events of September 11, 2001, there has been heightened attention in the United States and elsewhere to the use of multiple government and private databases for the identification of possible perpetrators of future attacks, as well as an unprecedented expansion of federal government data mining activities, many involving databases containing personal information. We present an overview of some proposals that have surfaced for the search of multiple databases which supposedly do not compromise possible pledges of confidentiality to the individuals whose data are included. We also explore their link to the related literature on privacy-preserving data mining. In particular, we focus on the matching problem across databases and the concept of ``selective revelation'' and their confidentiality implications.Comment: Published at http://dx.doi.org/10.1214/088342306000000240 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Intelligent Agent-Based Data Mining in Electronic Markets

The advent of web-based electronic commerce has brought a tremendous increase in the volume of “collectable data” that can be mined for valuable managerial knowledge. Utilizing intelligent agents can enhance the data mining procedures that are employed in this process. We focus on the role of data mining and intelligent agent technology in the B2C and B2B e- commerce models. By identifying the complex nature of information flows between the vast numbers of economic entities, we identify opportunities for applying data mining that can lead ultimately to knowledge discovery

Three Essays on Big Data Consumer Analytics in E-Commerce

Consumers are increasingly spending more time and money online. Business to consumer e-commerce is growing on average of 20 percent each year and has reached 1.5 trillion dollars globally in 2014. Given the scale and growth of consumer online purchase and usage data, firms\u27 ability to understand and utilize this data is becoming an essential competitive strategy. But, large-scale data analytics in e-commerce is still at its nascent stage and there is much to be learned in all aspects of e-commerce. Successful analytics on big data often require a combination of both data mining and econometrics: data mining to reduce or structure (from unstructured data such as text, photo, and video) large-scale data and econometric analyses to truly understand and assign causality to interesting patterns. In my dissertation, I study how firms can better utilize big data analytics and specific applications of machine learning techniques for improved e-commerce using theory-driven econometrical and experimental studies. I show that e-commerce managers can now formulate data-driven strategies for many aspect of business including cross-selling via recommenders on sales sites to increasing brand awareness and leads via social media content-engineered-marketing. These results are readily actionable with far-reaching economical consequences

Analisis Sentimen Ulasan pada Aplikasi E-Commerce dengan Menggunakan Algoritma Naïve Bayes

The rapid development of E-commerce has given rise to many marketplaces in Indonesia such as Tokopedia, Shopee, Lazada. Tokopedia, Shopee and Lazada applications are applications that help sellers and buyers to make sales and purchase transactions for goods and services. Until now, of the three major E-Commerce applications, around 100 million users have downloaded the three E-Commerce applications. With the launch of some of these applications, it has caused a lot of opinions and criticisms from the public. Based on this, a sentiment analysis of the Naïve Bayes algorithm was carried out to find out how the sentiment of users compares to the E-Commerce application on the Google Play Store. This research uses the Knowledge Discovery in Database (KDD) method which consists of 5 stages, namely data selection, preprocessing, transformation, data mining, and evaluation. The data used is a review of 500 E-Commerce applications per each application. At the data mining stage, it is carried out with 3 scenarios data sharing is 80:20, 70:30 and 60:40. The best results were obtained in scenario 1 (80:20) on the Shopee application using the Naïve Bayes algorithm which resulted in an accuracy of 92%, precision of 92.13%, recall of 98.8% and f1-score of 95.35%

WEB BASED PRODUCT E-COMMERCE RECOMMENDER SYSTEM USING A KNOWLEDGE CASE BASED ALGORITHM

Understanding customer needs is crucial to gaining and retaining customers in a web store, on online e-commerce applications. In order to ensure a versatile system for e-commerce, the pattern generated by customers when they click icons to select some particular products on an e-commerce website, based on their choice, ought to be studied, recorded and built-up into a database. Data mining techniques can then be applied in mining and analyzing information for this database in order to help wholesaler and retailers, improve sales, marketing strategies and product advertisement. In this paper, a conceptualized a framework for determining customers’ product choice and factors involved in choosing online commodities is proposed. The system is tailored for phone products

Systematic Literature Review

Mutemi, A., & Bação, F. (2023). E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review. Big Data Mining and Analytics, 1-27. https://doi.org/10.26599/BDMA.2023.9020023The e-commerce industry's rapid growth, accelerated by the COVID-19 pandemic, has led to an alarming increase in digital fraud and associated losses. To establish a healthy e-commerce ecosystem, robust cyber security and anti-fraud measures are crucial. However, research on fraud detection systems has struggled to keep pace due to limited real-world datasets. Advances in artificial intelligence, machine learning, and cloud computing have revitalized research and applications in this domain. While machine learning and data mining techniques are popular in fraud detection, specific reviews focusing on their application in ecommerce platforms like eBay and Facebook are lacking depth. Existing reviews provide broad overviews but fail to grasp the intricacies of machine learning algorithms in the e-commerce context. To bridge this gap, our study conducts a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology. We aim to explore the effectiveness of these techniques in fraud detection within digital marketplaces and the broader e-commerce landscape. Understanding the current state of the literature and emerging trends is crucial given the rising fraud incidents and associated costs. Through our investigation, we identify research opportunities and provide insights to industry stakeholders on key machine learning and data mining techniques for combating e-commerce fraud. Our paper examines the research on these techniques as published in the past decade. Employing the PRISMA approach, we conducted a content analysis of 101 publications, identifying research gaps, recent techniques, and highlighting the increasing utilization of artificial neural networks in fraud detection within the industry.publishersversionepub_ahead_of_prin

Marketing, Consumers and Technology: Perspectives for Enhancing Ethical Transactions

The advance of technology has influenced marketing in a number of ways that have ethical implications. Growth in use of the Internet and e-commerce has placed electronic cookies, spyware, spam, RFIDs, and data mining at the forefront of the ethical debate. Some marketers have minimized the significance of these trends. This overview paper examines these issues and introduces the two articles that follow. It is hoped that these entries will further the important marketing and technology ethical debate

Identifying e-Commerce in Enterprises by means of Text Mining and Classification Algorithms

Monitoring specific features of the enterprises, for example, the adoption of e-commerce, is an important and basic task for several economic activities. This type of information is usually obtained by means of surveys, which are costly due to the amount of personnel involved in the task. An automatic detection of this information would allow consistent savings. This can actually be performed by relying on computer engineering, since in general this information is publicly available on-line through the corporate websites. This work describes how to convert the detection of e-commerce into a supervised classification problem, where each record is obtained from the automatic analysis of one corporate website, and the class is the presence or the absence of e-commerce facilities. The automatic generation of similar data records requires the use of several Text Mining phases; in particular we compare six strategies based on the selection of best words and best n-grams. After this, we classify the obtained dataset by means of four classification algorithms: Support Vector Machines; Random Forest; Statistical and Logical Analysis of Data; Logistic Classifier. This turns out to be a difficult case of classification problem. However, after a careful design and set-up of the whole procedure, the results on a practical case of Italian enterprises are encouraging