Academic literature on the topic 'E-Commerce - Algorithms'

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.

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.

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.

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.