No Arabic abstract
Web 3.0 promises to have a significant effect in users and businesses. It will change how people work and play, how companies use information to market and sell their products, as well as operate their businesses. The basic shift occurring in Web 3.0 is from information-centric to knowledge-centric patterns of computing. Web 3.0 will enable people and machines to connect, evolve, share and use knowledge on an unprecedented scale and in new ways that make our experience of the Internet better. Additionally, semantic technologies have the potential to drive significant improvements in capabilities and life cycle economics through cost reductions, improved efficiencies, enhanced effectiveness, and new functionalities that were not possible or economically feasible before. In this paper we look to the semantic web and Web 3.0 technologies as enablers for the creation of value and appearance of new business models. For that, we analyze the role and impact of Web 3.0 in business and we identify nine potential business models, based in direct and undirected revenue sources, which have emerged with the appearance of semantic web technologies.
Customers trust in vendors reputation is a key factor that facilitates economic transactions in e-commerce platforms. Although the trust-sales relationship is assumed robust and consistent, its empirical evidence remains neglected for Latin American countries. This work aims to provide a data-driven comprehensive framework for extracting valuable knowledge from public data available in the leading Latin American e-commerce platform with commercial operations in 18 countries. Only Argentina, Brasil, Chile, Colombia, Ecuador, Mexico, Uruguay, and Venezuela showed the highest trust indexes among all nations analyzed. The trust-sales relationship was statistically inconsistent across nations but worked as the most important predictor of sales, followed by purchase intention and price.
Shared e-scooters have become a familiar sight in many cities around the world. Yet the role they play in the mobility space is still poorly understood. This paper presents a study of the use of Bird e-scooters in the city of Atlanta. Starting with raw data which contains the location of available Birds over time, the study identifies trips and leverages the Google Places API to associate each trip origin and destination with a Point of Interest (POI). The resulting trip data is then used to understand the role of e-scooters in mobility by clustering trips using 10 collections of POIs, including business, food and recreation, parking, transit, health, and residential. The trips between these POI clusters reveal some surprising, albeit sensible, findings about the role of e-scooters in mobility, as well as the time of the day where they are most popular.
Article about objective laws of formation of social and economic institutes in system of electronic commerce. Rapid development of Internet technologies became the reason of deep institutional transformation of economic relations. The author analyzes value transaction costs as motive power of formation of new economic institutes in network economy.
E-commerce is gradually transformed from a version of trading activity to independent branch of global network economy which cannot be ignored. The Russian Federation is in the lead in the CIS on development of e-commerce, but lags behind world leaders in institutionalization of e-commerce. Problems of state regulation of e-commerce in Russia are analyzed in article, ways of their decision are offered.
Showing items that do not match search query intent degrades customer experience in e-commerce. These mismatches result from counterfactual biases of the ranking algorithms toward noisy behavioral signals such as clicks and purchases in the search logs. Mitigating the problem requires a large labeled dataset, which is expensive and time-consuming to obtain. In this paper, we develop a deep, end-to-end model that learns to effectively classify mismatches and to generate hard mismatched examples to improve the classifier. We train the model end-to-end by introducing a latent variable into the cross-entropy loss that alternates between using the real and generated samples. This not only makes the classifier more robust but also boosts the overall ranking performance. Our model achieves a relative gain compared to baselines by over 26% in F-score, and over 17% in Area Under PR curve. On live search traffic, our model gains significant improvement in multiple countries.