A comparative study of item space visualizations for recommender systems. Issue 172 (April 2023) - Kunkel, Johannes; Ziegler, Jürgen
Abstract: Recommender systems aim at supporting users in their search and decision making process by selecting a small number of likely relevant items from a large set of options. Although automatically filtering unmanageably large item sets down to a few recommendations often produces results that match the user's interests well, it also prevents users from understanding and exploring items in their larger context. This may reduce users' perception of transparency and controllability of the system. Visualizations have been proposed as a means for overcoming this problem, with some visualizations providing a complete overview of the entire space of available items. However, thus far item space visualizations have rarely been investigated and compared in user studies. To address this, we developed and empirically compared three applications that present the user with personalized music recommendations embedded in a visualization of the entire item space. The three applications display the same item space as a list, as a treemap, and as a map, respectively. We compared these applications in an online user study and found, against our expectations, that they did not differ much in how the recommendations are perceived. Perception of transparency, recommendation quality, and degree of control over the recommendations received relatively high scores over all three applications. However, we did find a difference in hedonic user experience and perceived novelty of the recommendations. Both factors were perceived to be higher in the map condition. Backed up by a mediation analysis, we argue that a halo effect is the reason for the observed perceived novelty: participants transferred the novelty of the application to the novelty of the recommendations. Highlights: Three prototypes demonstrate visualization of a large item space of music artists. Recommender systems and information visualization are effectively combined. In a user study, recommendations were perceived as highly transparent. A map-based visualization resulted in the highest hedonic user experience. Hedonic user experience influenced as how novel recommendations were perceived.