Abstract
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.
Highlights
This study showed the influence of the recommending interface on user behavior in structured and real-world e-commerce stores
By combining evaluation based on two different methodologies—eye tracking and implicit behavior event tracking inside the browser, which is an original contribution of this paper—a large amount of data related to user activity and physical parameters of recommending interfaces were collected
Results were analyzed in order to compare the reliability and applicability of both methods, and the classification quality of popular modeling methods was verified with regard to recommending interfaces evaluation
Summary
The continuous growth of e-commerce, especially nowadays due to the coronavirus disease (COVID-19) pandemic, requires tools, recommendation systems that aim to help discover relevant products for individuals. While online shopping benefits generally exceed disadvantages, lack of personal touch, especially when a customer is overwhelmed by many alternative products is perceived as a major obstacle to shopping online. To alleviate this problem, online stores invest in personalization tools, recommendation systems being a very popular example of them. Recommendation adequacy plays a vital role in overall commercial success and customer satisfaction. The way of presenting a recommendation, and its positioning and usability seem to have an important role in the ultimate recommendation effectiveness
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