Nowadays the accuracy of recommendation still plays an important role, but other factors also need to be considered while evaluating recommender systems and algorithms. From the user perspective the most important evaluation criteria are: the novelty of recommendations (the ability to show recommendations of items that a user did not know before), serendipity (a measure of how surprising recommendations are), and diversity, which results in recommendations that are not similar and, thus, ensures a wider range of items potentially not known before. Trust in the recommendation system is another crucial factor, since a more-reliable recommendation should build up user's trust and result in higher usage of recommendations. Privacy also plays vital role as users more often do not want to disclose their personal preferences. Other criteria used to evaluate recommender systems are: coverage, which refers to both item space coverage (measured as the percentage of all items in a catalog that can be recommended) and user space coverage (the proportion of all users for whom the system can provide personalized recommendations). More technical criteria of recommender systems evaluation include scalability, which shows the amount of resources the recommendation engine needs when the amount of data increases, and adaptivity, which represents the ability to generate recommendations when item collections change quickly [16]. The recommendation algorithm and therefore quality of generated recommendations is an important aspect, but the efficiency of a recommendation system goes far beyond that factor [17]. There are a lot fewer studies on the selection of best methods of presenting recommended products to the client than research in the area of recommendation methods and algorithms themselves. One can seek the most appropriate ways to make recommendations to users by observing human-computer interactions with recommending interfaces. People's behavior while interacting with webpages can be tracked by registering user's generated events inside web browsers [3] or by using gaze tracking solutions, and counting the number of times the user moved over or browsed a given element of the website, in order to learn user's preferences and generate a set of similar products constituting the basis for recommendations [18]. Studying visual aspects of recommending interfaces could bring benefits in terms of better integration of these interfaces with online stores, thus improving efficiency of recommendations [17; 19]. The layout and position of the recommendation zone, the number of recommended products, the presence of a carousel responsible for scrolling zone content, the size of related photos, the color and size of product titles, prices, etc., can be assessed for interface improvement [20]. In times of information overload and inundating marketing content, the habituation effect often appears, resulting in the phenomenon of so-called banner blindness. It is possible that best recommendations from the algorithmic point of view can have little impact if they are not optimally presented [21; 22; 23] i.e., well-positioned on the website, at the right time while shopping, and with the right intensity level [24; 25; 26]. This paper focuses on evaluating the effectiveness of horizontal vs. vertical recommending interface layouts for four major e-commerce sites and is based on an implicit feedback study performed via a specially developed browser add-on. The rest of the paper has the following structure: research assumptions and methodology are presented in Section 2, the experiment architecture and results are presented in Section 3, and conclusions and future study plans are presented in Section 4.