# Improving User Experience with Recommender Systems by
## 1 Introduction  
With the massive increase in available data in recent years, many techniques and technologies have emerged to help businesses, workers, and customers process such data more efficiently. Various recommender systems (RSs) have been developed since the mid-1990s, and many of these systems have been applied to a variety of tasks [1; 2; 3]. RSs are interactive systems that must be designed according to human-centered principles. Human-computer interaction (HCI) is the science domain that informs and validates design with respect to user interaction with such a system. In the particular case of an RS, a key design objective is to assure users understand and accept its recommendations. A large corpus of design- and development-focused RS papers exists (c.f. _Recommender Systems Handbook_ by [PERSON], [PERSON], and [PERSON], 2015 [4]). Among RSs, we can distinguish two popular types of systems: content-based recommendation and collaborative recommendation. There is also an increasing number of hybrid recommender systems that combine different types of RSs. Other systems have appeared in previous years, such as demographic recommendation, utility-based recommendation, and knowledge-based recommendation, but they are less frequent. Differences between these RSs include the elicitation techniques (i.e., how to collect data and userpreferences), the recommendation generation algorithm, and the presentation of the recommendation (i.e., text message, image, video, sound, or a combination of these four items). In this study, we focused on an RS that produced recommendations in the form of a text message. While RSs have received significant attention in recent years, it is important to observe that they are just one of several 'advice-giving systems'. \"These include expert systems, knowledge-based systems, decision support systems, and recommender systems.\" [5] (p. 2). However, given the relative obscurity of the term 'advice-giving systems' and the much more frequently used term of'recommender systems'--even for systems that support user decisions where user preferences are not the core element in the enabling machine-learning algorithm--we use the latter term of recommender systems hereafter to represent systems that generate and present recommendation messages to users. Due to the considerable opportunities and challenges in many domains (e.g., business, government, education, and healthcare), numerous studies have been conducted on RSs [1; 2; 3], especially on the comprehension of their performance [6], their design implications [7; 8; 9], and recommendation techniques [3; 10]. Thus, prior research has significantly addressed design implications at the system level [8; 9]; however, researchers and scientists have mostly disregarded the design of the interface [11]. In the rare instances where the extant literature has focused on RS interface design, recommendations are produced and put forth following the collection of a user's interaction data and subsequently juxtaposed against the attributes of artifacts stored in large repositories. Yet, to date, the impact of recommendation message presentation on the user's perception, attitude, and behavior towards the recommendation has been significantly understudied. A system's design alone will not shape its users' perceptions of trust and the likelihood of them accepting a recommendation. The nature of the message content is also likely to play a significant role in affecting users' beliefs, attitudes, and behaviors, particularly in the context of managerial decision making [9]. Therefore, as content elements and interaction elements (e.g., buttons) jointly comprise the processing of recommendations by the user, a simultaneous and granular analysis of the effects of both design elements is required. Hence, this study aims to advance the contemporary understanding of RS designs by exploring different ways to optimize the information presentation and/or interaction layers of the user-RS interactions.