Recommendation systems play an important role in e-commerce turnover by presenting personalized recommendations. Due to the vast amount of marketing content online, users are less susceptible to these suggestions. In addition to the accuracy of a recommendation, its presentation, layout, and other visual aspects can improve its effectiveness. This study evaluates the visual aspects of recommender interfaces. Vertical and horizontal recommendation layouts are tested, along with different visual intensity levels of item presentation, and conclusions obtained with a number of popular machine learning methods are discussed. Results from the implicit feedback study of the effectiveness of recommending interfaces for four major e-commerce websites are presented. Two different methods of observing user behavior were used, i.e., eye-tracking and document object model (DOM) implicit event tracking in the browser, which allowed collecting a large amount of data related to user activity and physical parameters of recommending interfaces. Results have been analyzed in order to compare the reliability and applicability of both methods. Observations made with eye tracking and event tracking led to similar results regarding recommendation interface evaluation. In general, vertical interfaces showed higher effectiveness compared to horizontal ones, with the first and second positions working best, and the worse performance of horizontal interfaces probably being connected with banner blindness. Neural networks provided the best modeling results of the recommendation-driven purchase (RDP) phenomenon.

Designing business process models plays a vital role in business process management. The acquisition of such models may consume up to 60% of the project time. This time can be shortened using methods for the automatic or semi-automatic generation of process models. In this paper, we present a user-friendly method of business process composition. It uses a set of predefined constraints to generate a synthetic log of the process based on a simplified, unordered specification, which describes activities to be performed. Such a log can be used to generate a correct BPMN model. To achieve this, we propose the use of one of the existing process discovery algorithms or executing the activity graph-based composition algorithm, which generates the process model directly from the input log file. The proposed approach allows process participants to take part in process modeling. Moreover, it can be a support for business analysts or process designers in visualizing the workflow without the necessity to design the model explicitly in a graphical editor. The BPMN diagram is generated as an interchangeable XML file, which allows its further modification and adjustment. The included comparative analysis shows that our method is capable of generating process models characterized by high flow complexity and can support BPMN constructs, which are sufficient for about 70% of business cases.

In recent years, blockchains systems have seen massive adoption in retail and enterprise environments. Cryptocurrencies become more widely adopted, and many online businesses have decided to add the most popular ones, like Bitcoin or Ethereum, next to Visa or Mastercard payments. Due to the decentralized nature of blockchain-based systems, there is no possible way to revert confirmed transactions. It may result in losses caused by human error or poor design of the user interface. We created a cryptocurrency wallet with a full on-chain solution for aliasing accounts and tokens to improve user experience and avoid unnecessary errors. The aliasing system consists of a number of smart contracts deployed on top of the blockchain network that give the ability to register aliases to accounts and tokens and use them instead of opaque addresses. Our solution shows how performant modern blockchains are and presents a way of building fully decentralized applications that can compete with centralized ones in terms of performance.