This paper proposes a method for creating a dashboard, assuming the task of the visual analysis on rating matrices used for information recommendation. Recommender systems have been developed to help people find relevant information. One of the most representative recommendation algorithms is collaborative filtering (CF), which utilizes rating matrices consisting of user-item interactions. However, CF does not explicitly take into account the type of person interacting with items. The proposed dashboard is composed of information that is expected to be useful for understanding user profiles, which are extracted from a rating matrix. It includes visualizations such as heatmaps and histograms that display information about users and items. By using it, analysts can find trends of user preferences and identify users with distinctive preferences. Furthermore, the developers of recommendation services can obtain valuable information for generating recommendation explanations and considering new systems. To show the effectiveness of the proposed method, this paper describes a case study assuming the users using the proposed dashboard. One of the authors uses the dashboard as if he were the user of the given rating history, and tries to find those with similar preferences by examining highly/poorly rated items and evaluation strictness. The result shows that similar users can be found by using the proposed dashboard, which demonstrates the effectiveness of the proposed method.