Abstract
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.
Highlights
Fast e-commerce development inspires increasing attention to sales-boosting solutions, especially recommending systems, which aim to replace salespeople from traditional shops
33% of participants responded that they felt their selection was influenced by the recommendation content (RC) areas of the site (6% felt strongly about it), while others claimed the opposite, including 52% who strongly felt they did not care about recommendations on the website
The last group did seem to show strong resistance to the recommendations—some of those participants, when shown the RC sections after the test, were surprised that they might have neglected most of them at all, treating them comparably to adverts, which confirms the prevalence of the habituation effect
Summary
Fast e-commerce development inspires increasing attention to sales-boosting solutions, especially recommending systems, which aim to replace salespeople from traditional shops. Shopping online offers the benefit of convenience, but on the other hand it is lacking the personal touch of salespeople, especially when a customer has to select from a very large number of alternatives. The optimization of user experience, including personalization and implementing recommending interfaces, has a crucial role in e-commerce website design. Recommender systems play a vital role in motivating purchase decisions and usually prove successful in enhancing sales [1]. A user model is usually created, constituting a description of a user, in order to facilitate interactions between the user and the system [2]. A digital representation of a user model is a user profile, which reflects their preferences, transactions, online behavior, etc. [3]
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