Deep learning-enhanced framework for performance evaluation of a recommending interface with varied recommendation position and intensity based on eye-tracking equipment data processing

The increasing amount of marketing content in e-commerce websites results in the limited attention of users. For recommender systems, the way recommended items are presented becomes as important as the underlying algorithms for product selection. In order to improve the effectiveness of content presentation, marketing experts experiment with the layout and other visual aspects of website elements to find the most suitable solution. This study investigates those aspects for a recommending interface. We propose a framework for performance evaluation of a recommending interface, which takes into consideration individual user characteristics and goals. At the heart of the proposed solution is a deep neutral network trained to predict the efficiency a particular recommendation presented in a selected position and with a chosen degree of intensity. The proposed Performance Evaluation of a Recommending Interface (PERI) framework can be used to automate an optimal recommending interface adjustment according to the characteristics of the user and their goals. The experimental results from the study are based on research-grade measurement electronics equipment Gazepoint GP3 eye-tracker data, together with synthetic data that were used to perform pre-assessment training of the neural network.