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1
Zhao, Xue Song, and Kai Fan Ji. "Research on the Web Mining Algorithm Application in Tourism E-Commerce." Applied Mechanics and Materials 380-384 (August 2013): 1133–36. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1133.
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Web mining algorithms are widely used in e-commerce. Tourism e-commerce develops fast in recent years in China but the application of web mining algorithms stays in low level compared with some developed countries. This paper first discusses two major web mining algorithms: the Association Rules algorithm and Clustering Analysis, and then analyzes the application of web mining algorithm in tourism e-commerce. It concludes that web mining algorithms can help tourism e-commerce to improve web design, increase online sales and provide better personalized services for web users.
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Shalannanda, Wervyan, Rafi Falih Mulia, Arief Insanu Muttaqien, Naufal Rafi Hibatullah, and Annisabelia Firdaus. "Singular value decomposition model application for e-commerce recommendation system." JITEL (Jurnal Ilmiah Telekomunikasi, Elektronika, dan Listrik Tenaga) 2, no. 2 (2022): 103–10. http://dx.doi.org/10.35313/jitel.v2.i2.2022.103-110.
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A recommendation system is one of the most important things in today’s technology. It can suggest products that match the user’s preferences. Many fields utilize this system, including e-commerce, using various algorithms. This paper used the matrix factorization-based algorithm, singular value decomposition (SVD), to make a recommendation system based on users’ similarities. Afterward, we implement the model against the ModCloth Amazon dataset. The results imply that the SVD algorithm yields the best accuracy compared to other matrix factorization-based algorithms with root mean square error (RMSE) of 1.055586. Then, we optimized the SVD algorithm by changing the hyperparameters of the algorithm to generate better accuracy and yield a model with an RMSE value of 1.041784.
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Xiao, Bing. "Mining Algorithm for the Core Node in the Complex Network of E-Commerce." Applied Mechanics and Materials 556-562 (May 2014): 4577–81. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4577.
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Through digging out the core suppliers and core customers from the numerous suppliers and customers in the complex network of E-commerce, it contributes to reducing the adverse selection for the consumers and moral hazards for the operators caused by information asymmetry. Meanwhile, it is very meaningful for the credit risk protection in the complex network of E-commerce. On the basis of the references to the White and Smyth algorithms, in this paper, improvements from the White and Smyth algorithms are made herein, combining several features of the E-commerce complex network such as competitiveness, incomplete information and unsymmetrical information. In addition, an algorithm for mining the key nodes in E-commerce complex network is put forward, and applications are explained by instances.
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Zhang, Yiman. "The application of e-commerce recommendation system in smart cities based on big data and cloud computing." Computer Science and Information Systems, no. 00 (2021): 26. http://dx.doi.org/10.2298/csis200917026z.
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In the era of big data, the amount of Internet data is growing explosively. How to quickly obtain valuable information from massive data has become a challenging task. To effectively solve the problems faced by recommendation technology, such as data sparsity, scalability, and real-time recommendation, a personalized recommendation algorithm for e-commerce based on Hadoop is designed aiming at the problems in collaborative filtering recommendation algorithm. Hadoop cloud computing platform has powerful computing and storage capabilities, which are used to improve the collaborative filtering recommendation algorithm based on project, and establish a comprehensive evaluation system. The effectiveness of the proposed personalized recommendation algorithm is further verified through the analysis and comparison with some traditional collaborative filtering algorithms. The experimental results show that the e-commerce system based on cloud computing technology effectively improves the support of various recommendation algorithms in the system environment; the algorithm has good scalability and recommendation efficiency in the distributed cluster, and the recommendation accuracy is also improved, which can improve the sparsity, scalability and real-time problems in e-commerce personalized recommendation. This study greatly improves the recommendation performance of e-commerce, effectively solves the shortcomings of the current recommendation algorithm, and further promotes the personalized development of e-commerce.
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Zhang, Zhijun, Gongwen Xu, and Pengfei Zhang. "Research on E-Commerce Platform-Based Personalized Recommendation Algorithm." Applied Computational Intelligence and Soft Computing 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/5160460.
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Aiming at data sparsity and timeliness in traditional E-commerce collaborative filtering recommendation algorithms, when constructing user-item rating matrix, this paper utilizes the feature that commodities in E-commerce system belong to different levels to fill in nonrated items by calculating RF/IRF of the commodity’s corresponding level. In the recommendation prediction stage, considering timeliness of the recommendation system, time weighted based recommendation prediction formula is adopted to design a personalized recommendation model by integrating level filling method and rating time. The experimental results on real dataset verify the feasibility and validity of the algorithm and it owns higher predicting accuracy compared with present recommendation algorithms.
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Micu, Adrian, Marius Geru, Alexandru Capatina, Avram Constantin, Robert Rusu, and Andrei Alexandru Panait. "Leveraging e-Commerce Performance through Machine Learning Algorithms." Annals of Dunarea de Jos University of Galati. Fascicle I. Economics and Applied Informatics 25, no. 2 (2019): 162–71. http://dx.doi.org/10.35219/eai1584040947.
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Sergeeva, Valeria S., Tatiana V. Bysova, Viktor A. Smirnov, and Alexander V. Ponachugin. "PROBLEMS OF E-COMMERCE DEVELOPMENT." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 10/1, no. 130 (2022): 94–98. http://dx.doi.org/10.36871/ek.up.p.r.2022.10.01.012.
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The main object of study and analysis was the field of e-commerce. This choice is due to the emergence of a new sector in the global economy, which is becoming increasingly important. The implementation of correctly constructed economic algorithms on the Internet makes the organization more profitable and efficient. The article discusses the problems that hinder the development of e-commerce. The authors' conclusion: Russia lacks experience in the commercialization of these technologies, which is a significant problem for the country.
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Gan, Lin. "XGBoost-Based E-Commerce Customer Loss Prediction." Computational Intelligence and Neuroscience 2022 (July 31, 2022): 1–10. http://dx.doi.org/10.1155/2022/1858300.
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In recent years, with the rapid development of mobile Internet, more and more industries have begun to adopt mobile Internet technology, provide diversified wireless services, and further expand user activity scenarios. The core of reducing customer loss is to identify potential customers. In order to solve the problem of how to accurately predict the loss of customers, this paper put forward an invented method to verify and compared the model with the customer data of an e-commerce enterprise in China. According to the research results, the improved XGBoost algorithm can effectively reduce the probability of class I errors and has higher accuracy, among which the accuracy has increased by 2.8%. The prediction effect of customer groups after segmentation was better than that before segmentation, in which the probability of the occurrence of class I errors in the prediction of core value customers decreases by 10.8% and the accuracy rate increases by 7.8%. Compared with other classification algorithms, the improved XGBoost algorithm had a significant improvement in AUC value accuracy rate and other indicators. This fully shows that the XGBoost algorithm can effectively predict the loss of e-commerce customers and then provide decision-making reference for the customer service strategy of e-commerce enterprises.
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Sinha, Manish, and Divyank Srivastava. "Impact of recommender algorithms on the sales of e-commerce websites." International Journal of Innovation Science 13, no. 2 (2021): 161–74. http://dx.doi.org/10.1108/ijis-09-2020-0155.
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Purpose With the current pandemic situation, the world is shifting to online buying and therefore the purpose of this study is to understand how the industry can improve sales based on the product recommendations shown on their online platforms. Design/methodology/approach This paper has studied content-based filtering using decision trees algorithm and collaborative filtering using K-nearest neighbour algorithm and measured their impact on sales of product of different genres on e-commerce websites and if their recommendation causes a difference in sales.This paper has conducted a field experiment to analyse the customer frequency, change in sales caused by different algorithms and also tried analysing the change in buying preferences of customers in post-pandemic situation and how this paper can improve on the search results by incorporating them in the already used algorithms. Findings This study indicates that different algorithms cause differences in sales and score over each other depending upon the category of the product sold. It also suggests that post-Covid, the buying frequency and the preferences of consumers have changed significantly. Research limitations/implications The study is limited to existing users of these sites, it also requires the sites to have a huge database of active users and products. Also, the preferences and likings of Indian subcontinent might not generally apply everywhere else. Originality/value This study enables better insight into consumer behaviour, thus enabling the data scientists to design better algorithms and help the companies improve their product sales.
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Liu, Yang. "A Novel E-Commerce Negotiation Optimization Model Based on Improved Genetic Algorithm." Advanced Engineering Forum 6-7 (September 2012): 566–70. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.566.
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Electronic commerce has rapidly become a major player in the business market .This paper proposes a new electronic commerce negotiation optimization model based on improved genetic algorithm which depends on not only price, but also other factors of commodity. The proposed model illustrates the relationship between the business components required to support the e-commerce processes with the value creation factor and the controlling complexity. The experiment results show that the proposed algorithm can gain the optimal negotiation result more efficiently than other three kinds of negotiation algorithms in competitive bilateral multi-issue negotiation.
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Al-Zubaidie, Mishall, and Ghanima Sabr Shyaa. "Applying Detection Leakage on Hybrid Cryptography to Secure Transaction Information in E-Commerce Apps." Future Internet 15, no. 8 (2023): 262. http://dx.doi.org/10.3390/fi15080262.
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Technology advancements have driven a boost in electronic commerce use in the present day due to an increase in demand processes, regardless of whether goods, products, services, or payments are being bought or sold. Various goods are purchased and sold online by merchants (M)s for large amounts of money. Nonetheless, during the transmission of information via electronic commerce, Ms’ information may be compromised or attacked. In order to enhance the security of e-commerce transaction data, particularly sensitive M information, we have devised a protocol that combines the Fernet (FER) algorithm with the ElGamal (ELG) algorithm. Additionally, we have integrated data leakage detection (DLD) technology to verify the integrity of keys, encryptions, and decryptions. The integration of these algorithms ensures that electronic-commerce transactions are both highly secure and efficiently processed. Our analysis of the protocol’s security and performance indicates that it outperforms the algorithms used in previous studies, providing superior levels of security and performance.
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Wang, Yaosheng. "Construction of E-commerce Personalized Information Recommendation System in the Era of Big Data." Journal of Physics: Conference Series 2074, no. 1 (2021): 012085. http://dx.doi.org/10.1088/1742-6596/2074/1/012085.
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Abstract With the continuous expansion of the scale of e-commerce, personalized recommendation technology has been widely used. However, the traditional recommendation system has been unable to meet the current needs of data processing, and good big data processing ability has become the basic requirement of the new personalized recommendation system. In addition, traditional recommendation systems are often limited to tangible goods recommendation, and pay less attention to e-commerce logistics service recommendation. In this paper, through the in-depth study of information personalized recommendation service in e-commerce environment, combined with the application background of big data: Taking the user dissimilarity matrix as the recommendation model, we propose IU usercf and UDB slope one recommendation algorithm. The two algorithms based on incremental update recommendation model have good scalability, can effectively deal with big data, and have high prediction accuracy. The proposed algorithm is applied to the actual system, taking e-commerce logistics service as the recommendation object and iu-usercf as the recommendation algorithm, the personalized recommendation system for e-commerce logistics service is constructed. The e-commerce logistics service recommendation system explores the application practice of recommendation algorithm under big data, and enriches the application scenarios of personalized recommendation technology.
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Guo, Lin, and Dongliang Zhang. "EC-Structure: Establishing Consumption Structure through Mining E-Commerce Data to Discover Consumption Upgrade." Complexity 2019 (March 12, 2019): 1–8. http://dx.doi.org/10.1155/2019/6543590.
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The traditional methods of analyzing consumption structure have many limitations, and data acquisition is difficult, so it is hard to scientifically verify the accuracy of algorithms. With the development of Internet economy, many scientific researchers focus on mining knowledge of consumer behavior using big data analysis technology. Because consumption decisions are influenced by not only personal characteristics but also social trends and environment, it is one-sided to analyze the impact of one single factor on the phenomenon of consumption. The authors of this paper combine the consumption structure analysis method and data processing technology using data from an e-commerce platform to extract the consumption structure of cities, compare the structural differences between different periods, and then discover consumption upgrading according to swarm intelligence. The experiments prove the efficacy of the algorithm proposed in this paper compared to other similar algorithms using several different datasets, which illustrates the algorithm’s efficacy and stable performance in consumption structure analysis.
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Jokonowo, Bambang, Miskah Alfiyyah Kulsum, and Nita Komala. "Perbandingan Model Proses Algoritma Alpha dan Alpha++ Pada Aplikasi E-commerce." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 1 (2022): 123–29. http://dx.doi.org/10.29207/resti.v6i1.3732.
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Utilization of information technology is currently growing rapidly in helping activities especially in storing an event log. The activity which is behavior of the user can be analyzed using process mining. The process mining purpose to extract information from event logs on business processes that working. Discovery technique is used in this research. The purpose of this study is to compare two algorithms applied by creating an e-commerce application that is aware of the processes. E-commerce applications require event logs to read the behavior of visitor activities against the application. This research method starts from understanding the business processes that working, then designing a website by creating the application used. Furthermore, data collection through applications that are promoted through social media. The application will be recorded user activity and formed an event log. The event log that formed then discovered using alpha and alpha++ algorithms by utilizing the ProM Lite 1.2 tools. The evaluation results show that the alpha algorithm has shortcomings, namely length one loop, length two loop and non-free choice. And the alpha++ algorithm fixed this deficiency.
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Wu, Zengyuan, Lingmin Jin, Jiali Zhao, Lizheng Jing, and Liang Chen. "Research on Segmenting E-Commerce Customer through an Improved K-Medoids Clustering Algorithm." Computational Intelligence and Neuroscience 2022 (June 18, 2022): 1–10. http://dx.doi.org/10.1155/2022/9930613.
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In view of the shortcomings of traditional clustering algorithms in feature selection and clustering effect, an improved Recency, Frequency, and Money (RFM) model is introduced, and an improved K-medoids algorithm is proposed. Above model and algorithm are employed to segment customers of e-commerce. First, traditional RFM model is improved by adding two features of customer consumption behavior. Second, in order to overcome the defect of setting K value artificially in traditional K-medoids algorithm, the Calinski–Harabasz (CH) index is introduced to determine the optimal number of clustering. Meanwhile, K-medoids algorithm is optimized by changing the selection of centroids to avoid the influence of noise and isolated points. Finally, empirical research is done using a dataset from an e-commerce platform. The results show that our improved K-medoids algorithm can improve the efficiency and accuracy of e-commerce customer segmentation.
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Mu, Wei, and HePing Ding. "E-Commerce Intelligent Logistics Data Based on Neural Network Model." Mobile Information Systems 2022 (September 15, 2022): 1–12. http://dx.doi.org/10.1155/2022/8993365.
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Not only e-commerce is developing rapidly, but also intelligent logistics technology is becoming more and more mature. In daily life, we can track the logistics information synchronously. On the smartphone app, the logistics information can be viewed at any time. However, the current processing algorithms are not enough for the exponentially increasing data volume and increasingly complex data types. This paper aims to analyze the massive data generated by e-commerce intelligent logistics. In this paper, a new classification algorithm is proposed, which is improved on the ordinary ladder classification algorithm, and artificial neural networks are added for automatic iterative update learning, which can automatically classify a large amount of data. The experimental results show that the classification error rate of the improved algorithm is less than 5%, and when the sample size is less than 30,000, the improved algorithm can significantly outperform the original algorithm.
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Deng, Xiaoyi. "An Efficient Hybrid Artificial Bee Colony Algorithm for Customer Segmentation in Mobile E-commerce." Journal of Electronic Commerce in Organizations 11, no. 2 (2013): 53–63. http://dx.doi.org/10.4018/jeco.2013040105.
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Customer segmentation can enable company administrators to establish good customer relations and refine their marketing strategies to match customer expectations. To achieve optimal segmentation, a hybrid Artificial Bee Colony algorithm (ABC) is proposed to classify customers in mobile e-commerce environment, which is named KP-ABC. KP-ABC is based on three famous algorithms: the K-means, Particle Swarm Optimization (PSO), and ABC. The author first applied five clustering algorithms to a mobile customer segmentation problem using data collected from a well established chain restaurant which has operations throughout Japan. The results from the clustering were compared to the existing company customer segmentation data for verifications. Based on the initial analysis, special characteristics from those three algorithms were extracted and modified in our KP-ABC method which performed extremely well with mobile e-commerce applications. The result shows that KP-ABC is at least 2% higher than that of other three algorithms.
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Jiang, Xuehui. "Application of E-Commerce Interactive Marketing Model Based on Distributed Algorithm of Mobile Ad Hoc Network." Wireless Communications and Mobile Computing 2021 (December 2, 2021): 1–9. http://dx.doi.org/10.1155/2021/9766214.
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With the development of the mobile Internet, e-commerce has become one of the important ways of daily consumption, but how to effectively use e-commerce for interactive marketing and increase sales is an important research direction. Mobile ad hoc distributed algorithms are introduced in this paper. Through sorting out the mode of e-commerce interaction influence, process marketing is performed from two-dimensional code, short message, business district, mobile search, Bluetooth, wireless network, and other methods, and interactive marketing is tried in various industries such as education, tourism, agriculture, catering, finance, and publishing, and simulation experiments are used to verify them. The simulation experiment results show that the mobile ad hoc distributed algorithms are effective and can support the e-commerce interactive marketing model.
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Duan, Qing, Jian Li, and Yu Wang. "The Application of Fuzzy Association Rule Mining in E-Commerce Information System Mining." Advanced Engineering Forum 6-7 (September 2012): 631–35. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.631.
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Data mining in e-commerce application is information into business knowledge in the process. First of all, the object of clear data mining to determine the theme of business applications; around the commercial main data collection source, and clean up the data conversion, integration processing technology, and selects the appropriate data mining algorithms to build data mining models. This paper presents the application of fuzzy association rule mining in E-commerce information system mining. Experimental data sets prove that the proposed algorithm is effective and reasonable.
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Oruganti, Ramkrishna, Saurabh Shah, Yohan Pavri, Neelansh Prasad, and Prathamesh Churi. "JSSecure: A Secured Encryption Strategy for Payment Gateways in E-Commerce." Circulation in Computer Science 2, no. 5 (2017): 13–17. http://dx.doi.org/10.22632/ccs-2017-252-17.
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JSSecure is a framework for online payment systems over e-commerce websites. Payments made online using debit/credit cards have become familiar, and the users are shifting to a higher comfort level with this method of payment. Nowadays for any online transactions, a payment gateway is used which is a service that is provided by an e-commerce or by any bank that authorizes the details of the user for the secure transaction. This paper presents a frame format of JSSecure. For any transaction, there has to be a way in which the user details needs to be protected. Cryptography is one of the methods which is used for converting the information from its standard form to encrypted form or unreadable for the attackers. Using JSSecure, each user detail is encrypted individually to provide extra security against attackers. There are umpteen number of payment gateway methods like 3D Secure, SET, and MSET Protocols. Various algorithms help user securely enter his/her card details, some of them are Jumbling Salting (JS), Data Encryption Standard (DES), Advanced Encryption Standard (AES), etc. which are used for the encrypting the details securely. All these algorithms are symmetric key. JSSecure uses double encryption strategy for more security. We will be providing a fair comparison of Data Encryption Standard (DES), Advanced Encryption Standard (AES) and Jumbling Salting (JS) algorithms. Since our major concern here is the performance of algorithms under different conditions, we will be comparing on the basis of speed, block size, and key size on the encryption time, decryption time, throughput and size of cipher text. This analysis will help in implementing the best-suited algorithm for the proposed payment gateway. It will be open source and hence it will be more cost efficient.
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Chen, Nie. "Research on E-Commerce Database Marketing Based on Machine Learning Algorithm." Computational Intelligence and Neuroscience 2022 (June 29, 2022): 1–13. http://dx.doi.org/10.1155/2022/7973446.
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From simple commercial relations to complex online transactions at this stage, it not only highlights the progress of science and technology, but also indirectly explains the diversified evolution of marketing methods and means. In marketing, database marketing has been favored by more marketers with its low cost and high efficiency and has become the “rookie” in marketing in recent years. However, as a kind of prediction and ferry, database marketing tends to be applied after simple data analysis in unpredictable market and in practice. In contrast, database marketing combined with machine learning algorithms has always been a depression in the marketing field. Therefore, this paper takes e-commerce as the research object and carries out database marketing research based on machine learning algorithm from four stages: theoretical preparation, status analysis, model construction, and results application. Firstly, the connotation, advantages, and specific operation procedures of database marketing are discussed. At the same time, four excellent machine learning algorithms including logistic regression, random forest, support vector machine, and gradient boosted decision tree (GBDT) are selected to explain the basic principles and algorithm introduction, respectively, laying a theoretical foundation for the model training chapter. Secondly, it analyzes the current situation of e-commerce from the distribution of marketing objects, the proportion of marketing channels, and the composition of marketing methods and finds new marketing ideas based on the main problems existing at the present stage of database marketing using machine learning algorithm. Thirdly, on the premise of marketing ideas, data acquisition, data processing, and positive and negative sample setting. At the same time, four machine learning algorithms are used to combine features from the perspectives of consumers, stores, and the relationship between consumers and stores. Finally, by substituting the predicted sample into the model for testing, the crowd whose predicted score is between 80 and 99 is selected to be put into the market as the model predicted crowd, and it is proposed that e-commerce should mainly adopt the database marketing method of model prediction. On the one hand, machine learning algorithm can solve the problem of uneven distribution of marketing objects, and on the other hand, it can effectively prevent the loss of potential consumers. In addition, the application strategy of optimizing other database marketing methods and assisting model prediction to improve marketing effect is also put forward.
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Liu, Wei. "Coordination and Compensation Model of E-Commerce Dual-Channel Supply Chain Based on Optimized Genetic Algorithm." International Journal of Circuits, Systems and Signal Processing 16 (January 13, 2022): 426–32. http://dx.doi.org/10.46300/9106.2022.16.52.
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Due to the conflict between traditional channels and electronic channels in the e-commerce dual-channel supply chain, retailers are threatened and need to be compensated in some way. Based on this, an e-commerce dual-channel supply chain coordination compensation model based on optimized genetic algorithm is designed. Based on the problem description and basic assumptions, analyze the manufacturer’s profit and the retailer’s maximum profit in the case of centralized decision-making and decentralized decision-making. The genetic algorithm is optimized by introducing a collaborative genetic operator, and the optimized genetic algorithm is used to obtain dual e-commerce channels. The maximum profit of the supply chain, so far, the model design is completed. Through comparative experiments, the optimized genetic algorithm used in the model is compared with two traditional algorithms. Experimental results show that the proposed algorithm takes shorter iteration time to solve the problem, its convergence is better, and it can effectively obtain a global optimal solution instead of a local optimal solution.
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Salehi, Sajjad, and Maryam Ghasdimanghootai. "Term Weighting Vs. Logistic Regression Performance on E-Commerce Data." International Journal of Engineering & Technology 7, no. 4.35 (2018): 234. http://dx.doi.org/10.14419/ijet.v7i4.35.22738.
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Text categorization can become a very difficult problem to solve in many cases. However many text categorization algorithms have been developed in the history of computer science, they are not always as accurate as we expect. Some of them are highly accurate in special cases while others perform well in different cases. In this work, we are comparing two famous methods in text categorization; the first one is the well-known term weighting algorithm and the second one is the logistic regression algorithm. All the dataset is got from our previous start-up named “Ume Market Network” which was an online peer-to-peer e-commerce system, and was synchronized with Facebook sales groups. Every offer in this dataset should be categorized as a sale/purchase offer; therefore, the problem is a classical binary categorization on a text dataset of formal as well as colloquial expressions in English, Italian, and German languages. After overcoming all the ambiguities the logistic regression algorithm outperformed the term weighting algorithm by around 25% in acuracy.
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Meiling, Hu. "Big Data Mining and Analysis of Agricultural Products Based on e-Commerce Platform." Wireless Communications and Mobile Computing 2022 (September 13, 2022): 1–9. http://dx.doi.org/10.1155/2022/1730934.
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Solve the problem of agricultural product big data mining based on e-commerce platform, meet the needs of e-commerce development to agricultural products, meet the diversified needs of e-commerce platforms, and improve people’s living standards and convenience. According to 1000 online questionnaires, 866 people believe that e-commerce can bring them convenience, and 134 people believe that the convenience is insufficient. Even agricultural products, as a traditional primary industry, have begun to be “involved” in the sales mode of e-commerce platforms. In the face of the increasingly huge online consumer demand market, the agricultural product economy has redisplayed a strong market vitality. Of course, the huge market base also makes the e-commerce model of agricultural products pay attention to big data mining and analysis. This paper focuses on how to carry out big data mining and analysis of agricultural products more efficiently from the technical level. Therefore, the agricultural product user data mining technology of e-commerce platform based on Hadoop is proposed. Through the intervention of association rule analysis and algorithm, the improvement of relevant algorithms and agricultural products user behavior analysis system under e-commerce platform based on Hadoop is proposed. The results show that the system can realize the analysis of commodity association degree under various agricultural products user behavior modes and can better help the e-commerce platform of agricultural products realize precision marketing.
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Tu, Fang, and Bo Tu. "Prediction and Evaluation Method of e-Commerce Service Satisfaction Based on Intelligent Computing Method." Computational Intelligence and Neuroscience 2022 (August 30, 2022): 1–9. http://dx.doi.org/10.1155/2022/2730660.
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Among the many service industries, e-commerce, which is based on the Internet and relies mainly on platforms and third-party transaction models, has developed rapidly. All localities have actively deployed their regional e-commerce development strategies to improve the core competitiveness of the regional economy. The rapid development of e-commerce provides a favorable development environment and construction environment for the spatial agglomeration of e-commerce service industry. We use the intelligent computing method to calculate the e-commerce service degree prediction experimental results that show that according to the curves of the three algorithms, we can also see that the curve values of the intelligent computing and fuzzy statistical algorithm models are very stable and the experimental results are also very stable. It shows that the performance of the intelligent computing algorithm is the most superior; the second-level indicators are the after-sales service of the merchant, the popularity of the merchant, and the attitude of the merchant’s customer service; in the establishment of the logistic satisfaction evaluation index system, we found that the logistic satisfaction is the first-level indicator; the secondary indicators are the speed of logistics, the safety of logistics, the service attitude of logistics, and the price of logistics; after running on the test set, the model accuracy rate of the fuzzy statistical algorithm is 89.12%, and the accuracy rate can reach 89.56%. The accuracy rate of the intelligent algorithm can reach 92.46%, and the accuracy rate can reach 93.27%, which is the one with the highest index value among the three experimental models. Among the many service industries, e-commerce, which is based on the Internet and relies on platforms and third-party transaction models, is developing rapidly. All localities have actively deployed their regional e-commerce development strategies to improve the core competitiveness of the regional economy. The rapid development of e-commerce provides a favorable development environment and construction environment for the spatial agglomeration of e-commerce service industry.
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Cucu Ika Agustyaningrum, Muhammad Haris, Riska Aryanti, and Titik Misriati. "Online Shopper Intention Analysis Using Conventional Machine Learning And Deep Neural Network Classification Algorithm." Jurnal Penelitian Pos dan Informatika 11, no. 1 (2021): 89–100. http://dx.doi.org/10.17933/jppi.v11i1.341.
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The use of e-commerce throughout the world in recent years is very rapid. The continuous increase in sales shows that e-commerce has huge market potential. Store profits are derived from the process of assessing data to identify and classify online shopper intentions. The process of assessing the data uses conventional machine learning algorithms and deep neural networks. Comparison of algorithms in this study using the python programming language by knowing the value of Accuracy, F1-Score, Precision, Recall, and ROC AUC. The test results show that the accuracy of the deep neural network algorithm is 98.48%, the F1 score is 95.06%, precision is 97.36%, recall is 96.81% and AUC is 96.81%. So, based on this research, deep neural network data mining techniques can be an effective algorithm for online shopper intention data sets with cross-validation folds of 10, six hidden layer decoder-encoder variations, relu-sigmoid activation function, adagrad optimizer, and learning rate of 0.01 and no dropout. The value of this deep neural network algorithm is quite dominant compared to conventional machine learning algorithms and related research.
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Huang, Zan, Daniel Zeng, and Hsinchun Chen. "A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce." IEEE Intelligent Systems 22, no. 5 (2007): 68–78. http://dx.doi.org/10.1109/mis.2007.4338497.
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Hu, Zhi-Hua, Xiang Li, Chen Wei, and Hong-Lei Zhou. "Examining collaborative filtering algorithms for clothing recommendation in e-commerce." Textile Research Journal 89, no. 14 (2018): 2821–35. http://dx.doi.org/10.1177/0040517518801200.
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Wang, Bo, Feiyue Ye, and Jialu Xu. "A Personalized Recommendation Algorithm Based on the User’s Implicit Feedback in E-Commerce." Future Internet 10, no. 12 (2018): 117. http://dx.doi.org/10.3390/fi10120117.
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A recommendation system can recommend items of interest to users. However, due to the scarcity of user rating data and the similarity of single ratings, the accuracy of traditional collaborative filtering algorithms (CF) is limited. Compared with user rating data, the user’s behavior log is easier to obtain and contains a large amount of implicit feedback information, such as the purchase behavior, comparison behavior, and sequences of items (item-sequences). In this paper, we proposed a personalized recommendation algorithm based on a user’s implicit feedback (BUIF). BUIF considers not only the user’s purchase behavior but also the user’s comparison behavior and item-sequences. We extracted the purchase behavior, comparison behavior, and item-sequences from the user’s behavior log; calculated the user’s similarity by purchase behavior and comparison behavior; and extended word-embedding to item-embedding to obtain the item’s similarity. Based on the above method, we built a secondary reordering model to generate the recommendation results for users. The results of the experiment on the JData dataset show that our algorithm shows better improvement in regard to recommendation accuracy over other CF algorithms.
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Lou, Feng. "E-Commerce Recommendation Technology Based on Collaborative Filtering Algorithm and Mobile Cloud Computing." Wireless Communications and Mobile Computing 2022 (March 18, 2022): 1–8. http://dx.doi.org/10.1155/2022/7321021.
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Personalized recommendation technology, as one of the core technologies of an E-commerce platform, has attracted a lot of attention with the rise of E-commerce in the Internet industry. Mobile cloud computing-based E-commerce has also exploded in popularity. You can buy whatever you want without leaving the house. Consumers are becoming increasingly receptive to online shopping as a result of this convenience; the E-commerce model demonstrates great modern business value. With its convenient and quick characteristics, online shopping has become fashionable and trendy; however, the popularity of the Internet and the rapid development of E-commerce has resulted in information overload, making it difficult for users to find the goods they require among a vast amount of product information. As a result, the E-commerce recommendation system was born. However, there is currently very little in-depth research on personalized recommendation technology in the field of o2o E-commerce, and most existing recommendation algorithms need to be improved in terms of accuracy and recommendation efficiency.
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Jing, Weijuan. "Construction of an E-Commerce System Based on 5G and Internet of Things Technology." International Journal of Information Systems and Supply Chain Management 15, no. 2 (2022): 1–19. http://dx.doi.org/10.4018/ijisscm.287630.
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In order to improve the comprehensive performance of the e-commerce system, this paper combines 5G communication technology and the Internet of Things technology to improve the e-commerce system, and conduct end-point analysis on the e-commerce client data analysis system and smart logistics system. Moreover, this paper uses 5G technology to improve machine learning algorithms to process e-commerce back-end data and improve the efficiency of e-commerce client data processing. In addition, this paper combines the Internet of Things to build an e-commerce smart logistics system model to improve the overall efficiency of the logistics system. Finally, this paper combines the demand analysis to construct the functional module structure of the e-commerce system, and verifies the practical functions of the system through experimental research. From the experimental research results, it can be seen that the e-commerce system based on 5G communication technology and Internet of Things technology constructed in this paper is very reliable.
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Kaewphet, Chanida, and Nawaporn Wisitpongpun. "Algorithm for extracting product feature from e-commerce comment." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (2021): 1199. http://dx.doi.org/10.11591/ijeecs.v22.i2.pp1199-1207.
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<span>Reviews of e-commerce play an important role in online purchasing decisions. Consumers are likely to read reviews and comments on products from other consumers. In addition to those opinions that reflect consumers' trust in products, it also provides each product's distinctive properties. Today, there are many online reviews, resulting in enormous comments and suggestions. However, as fully reading reviews is quite difficult, this article presents 3 algorithms for automatic extraction of product features hidden in e-commerce reviews: a traditional frequency-based product feature extraction (F-PFE), syntax analyzer system (SAS), and the hybrid approach called the frequency and syntax-based product feature extraction (FaS-PFE). The proposed algorithms were tested against 4 different types of products: shampoo, skincare, mobile phone, and tablet, using reviews from amazon.com. Based on the product review used in this study, it was found that the SAS can help improve the performance in terms of precision by 15% when compared with the traditional F-PEE approach. When considering both the word frequency and syntax, FaS-PFE clearly outperforms the other two approaches with 94.00% precision and 95.13% recall.</span>
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Su, Hang. "The power of platforms and apps-empowering and controlling the consumer." Media and Communication Research 3, no. 1 (2022): 17–23. http://dx.doi.org/10.23977/mediacr.2022.030103.
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This article discusses the new marketing model of e-commerce platforms in the Internet age. Merchants use e-commerce platforms as marketing methods to make profits. Customers, as Internet users, when they enjoy the benefits brought by e-commerce platforms, algorithms and data are controlling their thought and action. This article is mainly based on the academic foundation of three publications: the "platform capitalism" written by Nick in 2017, 'Appified -Culture in the Age of Apps' written by Jeremy Wade Morris and Sarah Murray in 2018, 'When Platform Capitalism Meets Petty Capitalism in China: Alibaba and an Integrated Approach to Platformization' written by LIN ZHANG in 2020, analyzes how the Chinese e-commerce platform, Taobao, influences the Internet consumers, and compares the Chinese e-commerce platforms and western e-commerce platforms.
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Li, Xiaofeng, and Dong Li. "An Improved Collaborative Filtering Recommendation Algorithm and Recommendation Strategy." Mobile Information Systems 2019 (May 7, 2019): 1–11. http://dx.doi.org/10.1155/2019/3560968.
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The e-commerce recommendation system mainly includes content recommendation technology, collaborative filtering recommendation technology, and hybrid recommendation technology. The collaborative filtering recommendation technology is a successful application of personalized recommendation technology. However, due to the sparse data and cold start problems of the collaborative recommendation technology and the continuous expansion of data scale in e-commerce, the e-commerce recommendation system also faces many challenges. This paper has conducted useful exploration and research on the collaborative recommendation technology. Firstly, this paper proposed an improved collaborative filtering algorithm. Secondly, the community detection algorithm is investigated, and two overlapping community detection algorithms based on the central node and k-based faction are proposed, which effectively mine the community in the network. Finally, we select a part of user communities from the user network projected by the user-item network as the candidate neighboring user set for the target user, thereby reducing calculation time and increasing recommendation speed and accuracy of the recommendation system. This paper has a perfect combination of social network technology and collaborative filtering technology, which can greatly increase recommendation system performance. This paper used the MovieLens dataset to test two performance indexes which include MAE and RMSE. The experimental results show that the improved collaborative filtering algorithm is superior to other two collaborative recommendation algorithms for MAE and RMSE performance.
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Yazdani, Reza, Mohammad Javad Taghipourian, Mohammad Mahdi Pourpasha, and Seyed Shamseddin Hosseini. "Attracting Potential Customers in E-Commerce Environments: A Comparative Study of Metaheuristic Algorithms." Processes 10, no. 2 (2022): 369. http://dx.doi.org/10.3390/pr10020369.
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Internet technology has provided an indescribable new way for businesses to attract new customers, track their behaviour, customise services, products, and advertising. Internet technology and the new trend of online shopping have resulted in the establishment of numerous websites to sell products on a daily basis. Products compete to be displayed on the limited pages of a website in online shopping because it has a significant impact on sales. Website designers carefully select which products to display on a page in order to influence the customers’ purchasing decisions. However, concerns regarding appropriate decision making have not been fully addressed. As a result, this study conducts a comprehensive comparative analysis of the performance of ten different metaheuristics. The ant lion optimiser (ALO), Dragonfly algorithm (DA), Grasshopper optimisation algorithm (GOA), Harris hawks optimisation (HHO), Moth-flame optimisation algorithm (MFO), Multi-verse optimiser (MVO), sine cosine algorithm (SCA), Salp Swarm Algorithm (SSA), The whale optimisation algorithm (WOA), and Grey wolf optimiser (GWO) are some of the recent algorithms that were chosen for this study. The results show that the MFO outperforms the other methods in all sizes. MFO has an average normalised objective function of 81%, while ALO has a normalised objective function of 77%. In contrast, HHO has the worst performance of 16%. The study’s findings add new theoretical and practical insights to the growing body of knowledge about e-commerce environments and have implications for planners, policymakers, and managers, particularly in companies where an unplanned advertisement wastes the budget.
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Wen, Jingwu. "A Layered Encryption Model PABB Based on User Privacy in E-commerce Platforms." Frontiers in Business, Economics and Management 9, no. 3 (2023): 10–14. http://dx.doi.org/10.54097/fbem.v9i3.9428.
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In the era of big data, e-commerce platforms have increasingly strict requirements for encryption systems to prevent user privacy breaches. Traditional encryption systems use a single encryption algorithm, which cannot achieve a balance between efficiency and security. The layered encryption model PABB classifies and layers user data of different security levels, using targeted different encryption algorithms for protection, balancing the requirements of security and efficiency.
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Teodorescu, Daniel, Kamer-Ainur Aivaz, Diane Paula Corine Vancea, Elena Condrea, Cristian Dragan, and Ana Cornelia Olteanu. "Consumer Trust in AI Algorithms Used in E-Commerce: A Case Study of College Students at a Romanian Public University." Sustainability 15, no. 15 (2023): 11925. http://dx.doi.org/10.3390/su151511925.
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The aim of this cross-sectional study was to investigate the factors associated with trust in AI algorithms used in the e-commerce industry in Romania. The motivation for conducting this analysis arose from the observation of a research gap in the Romanian context regarding this specific topic. The researchers utilized a non-probability convenience sample of 486 college students enrolled at a public university in Romania, who participated in a web-based survey focusing on their attitudes towards AI in e-commerce. The findings obtained from an ordinal logistic model indicated that trust in AI is significantly influenced by factors such as transparency, familiarity with other AI technologies, perceived usefulness of AI recommenders, and the students’ field of study. To ensure widespread acceptance and adoption by consumers, it is crucial for e-commerce companies to prioritize building trust in these new technologies. This study makes significant contributions to our understanding of how young consumers in Romania perceive and evaluate AI algorithms utilized in the e-commerce sector. The findings provide valuable guidance for e-commerce practitioners in Romania seeking to effectively leverage AI technologies while building trust among their target audience.
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Zhang, Qing, Abdul Rashid Abdullah, Choo Wei Chong, and Mass Hareeza Ali. "E-Commerce Information System Management Based on Data Mining and Neural Network Algorithms." Computational Intelligence and Neuroscience 2022 (April 11, 2022): 1–11. http://dx.doi.org/10.1155/2022/1499801.
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The rapid development of artificial intelligence technology has led to rapid development in various fields. It has many hidden related customer behavior information and future development trends in the e-commerce information system. The data mining technology can dig out useful information and promote the development of e-commerce. This research analyzes the significance and advantages of data mining technology in the application of e-commerce management systems and analyzes the related technologies of data mining and future trend prediction. This research has taken the advantages of clustering and naive Bayesian methods in data mining to classify product information and purchase preferences and other information and mine the associated data. Then, the nonlinear data processing advantages of neural networks are used to predict future purchasing power. The results show that data mining technology and neural networks have high accuracy in predicting future consumer purchasing power information. The correlation coefficient between real consumption data and predicted consumption data reached 0.9785, and the maximum relative average error was only 2.32%. It fully shows that data mining technology can obtain some unrecognizable related information and future consumption trends in e-commerce systems, and neural networks can also predict future consumption power and consumption patterns well.
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GUAN, SHENG-UEI, WEN PIN TAN, and FEI LIU. "COGBROKER — A COGNITIVE APPROACH TO INTELLIGENT PRODUCT BROKERING FOR E-COMMERCE." International Journal of Computational Intelligence and Applications 07, no. 04 (2008): 401–27. http://dx.doi.org/10.1142/s1469026808002363.
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Researchers have proposed intelligent product-brokering applications to help facilitate the m-commerce shopping process. However, most algorithms require explicit, user-provided feedback to learn about user preference. In practical applications, users may not be motivated to provide unrewarded and time-consuming feedback. By adopting a cognitive approach, this paper investigates the possibility of replacing user feedback with user behavioral data analysis during product browsing. By means of evolutionary algorithms, the system is able to derive corresponding models that simulate the user's shopping behavior. User group profiling is also implemented to help identify the user's shopping patterns. Upon simulations of trial cases with consistent and rational shopping patterns, our experimental results confirm this approach being promising. The system shows high accuracy in detecting the preferences of the user. The algorithms are also portable and effective across different products.
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Zhao, Bin, WenYing Li, Qian Guo, and RongRong Song. "E-Commerce Picture Text Recognition Information System Based on Deep Learning." Computational Intelligence and Neuroscience 2022 (January 3, 2022): 1–11. http://dx.doi.org/10.1155/2022/9474245.
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For the accuracy requirements of commodity image detection and classification, the FPN network is improved by DPFM ablation and RFM, so as to improve the detection accuracy of commodities by the network. At the same time, in view of the narrowing of channels in the application of traditional MWI-DenseNet network, a new GTNet network is proposed to improve the classification accuracy of commodities.The results show that at different levels of evaluation indexes, the dpFPN-Netv2 algorithm improved by DPFM + RFM fusion has higher target detection accuracy than RetinaNet-50 algorithm and other algorithms. And the detection time is 52 ms, which is significantly lower than 90 ms required for RetinaNet-50 detection. In terms of target recognition, compared with the traditional MWI-DenseNet neural network, the computation amount of the improved MWI DenseNet neural network is significantly reduced under different shunt ratios, and the recognition accuracy is significantly improved. The innovation of this study lies in improving the algorithm from the perspective of target detection and recognition, so as to change the previous improvement that only can be made in a single way.
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Acosta-Vargas, Patricia, Belén Salvador-Acosta, Luis Salvador-Ullauri, and Janio Jadán-Guerrero. "Accessibility challenges of e-commerce websites." PeerJ Computer Science 8 (February 22, 2022): e891. http://dx.doi.org/10.7717/peerj-cs.891.
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Today, there are many e-commerce websites, but not all of them are accessible. Accessibility is a crucial element that can make a difference and determine the success or failure of a digital business. The study was applied to 50 e-commerce sites in the top rankings according to the classification proposed by ecommerceDB. In evaluating the web accessibility of e-commerce sites, we applied an automatic review method based on a modification of Website Accessibility Conformance Evaluation Methodology (WCAG-EM) 1.0. To evaluate accessibility, we used Web Accessibility Evaluation Tool (WAVE) with the extension for Google Chrome, which helps verify password-protected, locally stored, or highly dynamic pages. The study found that the correlation between the ranking of e-commerce websites and accessibility barriers is 0.329, indicating that the correlation is low positive according to Spearman’s Rho. According to the WAVE analysis, the research results reveal that the top 10 most accessible websites are Sainsbury’s Supermarkets, Walmart, Target Corporation, Macy’s, IKEA, H&M Hennes, Chewy, Kroger, QVC, and Nike. The most significant number of accessibility barriers relate to contrast errors that must be corrected for e-commerce websites to reach an acceptable level of accessibility. The most neglected accessibility principle is perceivable, representing 83.1%, followed by operable with 13.7%, in third place is robust with 1.7% and finally understandable with 1.5%. Future work suggests constructing a software tool that includes artificial intelligence algorithms that help the software identify accessibility barriers.
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Sachan, Rohit Kumar, and Dharmender Singh Kushwaha. "A Multi-Objective Anti-Predatory NIA for E-Commerce Logistics Optimization Problem." International Journal of Applied Metaheuristic Computing 12, no. 4 (2021): 1–27. http://dx.doi.org/10.4018/ijamc.2021100101.
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Nature-inspired algorithms (NIAs) have established their promising performance to solve both single-objective optimization problems (SOOPs) and multi-objective optimization problems (MOOPs). Anti-predatory NIA (APNIA) is one of the recently introduced single-objective algorithm based on the self-defense behavior of frogs. This paper extends APNIA as multi-objective algorithm and presents the first proposal of APNIA to solve MOOPs. The proposed algorithm is a posteriori version of APNIA, which is named as multi-objective anti-predatory NIA (MO-APNIA). It uses the concept of Pareto dominance to determine the non-dominated solutions. The performance of the MO-APNIA is established through the experimental evaluation and statistically verified using the Friedman rank test and Holm-Sidak test. MO-APNIA is also employed to solve a multi-objective variant of hub location problem (HLP) from the perspective of the e-commerce logistics. Results indicate that the MO-APNIA is also capable to finds the non-dominated solutions of HLP. This finds immense use in logistics industry.
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Wang, Zhe, and Hong Zhu. "Optimization of e-commerce logistics of marine economy by fuzzy algorithms." Journal of Intelligent & Fuzzy Systems 38, no. 4 (2020): 3813–21. http://dx.doi.org/10.3233/jifs-179604.
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AKINYEDE, Raphael Olufemi, Sulaiman Omolade ADEGBENRO, and Babatola Moses OMILODI. "A SECURITY MODEL FOR PREVENTING E-COMMERCE RELATED CRIMES." Applied Computer Science 16, no. 3 (2020): 30–41. http://dx.doi.org/10.35784/acs-2020-19.
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The major challenge being faced by the financial related institutions, such as e-Commerce has been insecurity. Therefore, there is urgent need to develop a scheme to protect transmitted financial information or messages from getting to the third party, intruder and/or unauthorized person(s). Such scheme will be based on Advanced Encryption Standard (AES) and Neural Data Security (NDS) Model. Based on this background, an AES using Time-based Dynamic Key Generation coupled with NDS model will be used to develop security model for preventing e-commerce related crimes. While AES will secure users’ details in the database server and ensures login authentications, NDS model will fragment or partition sensitive data into High and Low levels of confidentiality. The sensitivity of the data will determine, which category of confidentiality the data will fall into. The fragmented data are saved into two different databases, on two different servers and on the same datacenter. In addition, an exploratory survey was carried out using different performance metrics with different classifications of algorithms. Out of the four algorithms considered, Naive Bayes performs better as it shows, out of a total of 105 instances that were observed, 85.71% were correctly classified while 14.29% were misclassified.
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Lin, Yi, and Jian Bo Lu. "Refining the Location-Identity Split Using "Fuzzy" Algorithms." Applied Mechanics and Materials 701-702 (December 2014): 1116–20. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.1116.
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Unified encrypted technology has led to many key advances, including e-commerce and public-private key pairs. Despite the fact that it is never a key purpose, it fell in line with our expectations. In this position paper, we validate the development of Boolean logic, which embodies the significant principles of electrical engineering. CoolCoiner, our new algorithm for the construction of sensor networks, is the solution to all of these grand challenges. CoolCoiner verifies that e-commerce and fiber-optic cables can collaborate to realize this objective. To fix this challenge for the robust unification of SMPs and the location-identity split, we explored an analysis of hierarchical databases. As a result, the characteristics of our system, in relation to those of more acclaimed systems, are dubiously more confirmed.
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Gao, Lili, and Jianmin Li. "E-Commerce Personalized Recommendation Model Based on Semantic Sentiment." Mobile Information Systems 2022 (August 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/7246802.
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The real economy has moved to online electronic market transactions as a result of the rapid development of Internet technology. Online shopping makes up a growing portion of transactions in China’s e-commerce market, and the number of users who are aware of online payment transactions on mobile phones is rising. Online shopping platforms like Taobao and JD.com, which are all exemplary online shopping platforms, are constantly emerging. However, because there is so much product information available when shopping online, it can be challenging for users to locate the information they need. Recently developed personalized recommender systems have successfully addressed this issue. The system can predict the user’s preferences through extensive data analysis, and it then pushes the predicted information to the user interface, greatly increasing the user’s purchasing efficiency and the advantages of e-commerce. As a result, in the modern era, research on the personalized recommendation model in e-commerce has become increasingly popular. In this study, semantic sentiment analysis—which is improved on the traditional semantic sentiment analysis algorithm—is introduced in the research of a personalized recommendation system, and 1000 users are chosen for an experimental study. On the user’s personalized product recommendation, the improved semantic sentiment analysis and other widely used personalized recommendation algorithms are compared. According to the survey results, the average transaction success rate is 71.3 percent, and the maximum search time is 1.74 milliseconds when collaborative filtering recommendation algorithm is used. Semantic sentiment analysis has reduced search times to a maximum of 1.42 milliseconds and increased transaction success rates to 87.9 percent. After the addition of semantic sentiment analysis, it is clear that the personalized recommendation system model has a higher accuracy in recommending the products that users have expressed an interest in, which can have a greater positive impact on e-commerce transactions.
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Jamshed, Ammar. "BAYESIAN NETWORK DESIGN FOR A DECISION SUPPORT SYSTEM IN SOUTH ASIAN E-COMMERCE MANAGEMENT." International Journal For Research In Advanced Computer Science And Engineering 9, no. 4 (2023): 1–5. http://dx.doi.org/10.53555/cse.v9i4.2267.
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E-commerce is a thriving industry in Pakistan which is the a high population density country connected by air travel and road networks to India and China as well as central Asia thus using advanced computing algorithms allow for examination into effective business practices for managerial commerce via smart technology platforms and using Bayesian model for predictability of consumer purchasing based on behavioral variables in Pakistan serve as foundational support in expansion of smart management of E-commerce in Pakistan and other potential markets of Bangladesh and Sri Lanka with similar behavioral trends.
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Xu, Lijuan, and Xiaokun Sang. "E-Commerce Online Shopping Platform Recommendation Model Based on Integrated Personalized Recommendation." Scientific Programming 2022 (April 27, 2022): 1–9. http://dx.doi.org/10.1155/2022/4823828.
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With the continuous innovation of Internet technology and the substantial improvement of network basic conditions, e-commerce has developed rapidly. Online shopping has become the mainstream mode of e-commerce. In order to solve the problem of information overload and information loss in the selection of e-commerce online shopping platform, a personalized recommendation system using information filtering technology has come into being. An e-commerce online shopping platform recommendation model is proposed based on integrated multiple personalized recommendation algorithms: random forest, gradient boosting decision tree, and eXtreme gradient boosting. The proposed model is tested on the public data set. The experimental results of the separate model and mixed model are compared and analyzed. The results show that the proposed model reduces the recommendation sparsity and improves the recommendation accuracy.
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Li, Jiling, and Xiaheng Zhang. "The Export Operation Mode and Optimization Strategy of Crossborder e-Commerce Enterprises Integrating Data Mining Algorithms." Wireless Communications and Mobile Computing 2022 (July 4, 2022): 1–13. http://dx.doi.org/10.1155/2022/6231249.
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With the advent of the crossborder e-commerce (CE) boom, more and more traditional enterprises have transformed into the field of crossborder e-commerce. It has become a new outlet for enterprises in the international trade environment, and at the same time, it has some problems in the utilization of resources and the maximization of benefits. In response to this problem, it is very important to optimize the export operation model of crossborder e-commerce enterprises (CEE). With the development of intelligent algorithms, research on the application of intelligent algorithms to the economic field has gradually been carried out. Its characteristics and advantages are of great significance to the optimization of CEE export operation mode. The purpose of this paper is to study the CEE export operation mode and optimization strategy based on data mining algorithm (DMA). Through the analysis and research of DMA, it can be applied to the optimization of CEE export operation mode to cope with the world trade in the new environment. This paper explains the basic theory of DMA and CEE export operation mode. Its effect is experimentally analyzed, and the relevant theoretical formulas are used to explain. The results show that the comprehensive trade quality index of the CEE export operation mode integrating DMA is higher, and the adaptability is stronger. Compared with the traditional operating model, the difference between the two indices is 3.175 index points. It has guiding significance in terms of CEE export operation mode and optimization strategy.
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Sekhar Babu, B., P. Lakshmi Prasanna, and P. Vidyullatha. "Personalized web search on e-commerce using ontology based association mining." International Journal of Engineering & Technology 7, no. 1.1 (2017): 286. http://dx.doi.org/10.14419/ijet.v7i1.1.9487.
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In current days, World Wide Web has grown into a familiar medium to investigate the new information, Business trends, trading strategies so on. Several organizations and companies are also contracting the web in order to present their products or services across the world. E-commerce is a kind of business or saleable transaction that comprises the transfer of statistics across the web or internet. In this situation huge amount of data is obtained and dumped into the web services. This data overhead tends to arise difficulties in determining the accurate and valuable information, hence the web data mining is used as a tool to determine and mine the knowledge from the web. Web data mining technology can be applied by the E-commerce organizations to offer personalized E-commerce solutions and better meet the desires of customers. By using data mining algorithm such as ontology based association rule mining using apriori algorithms extracts the various useful information from the large data sets .We are implementing the above data mining technique in JAVA and data sets are dynamically generated while transaction is processing and extracting various patterns.