Recommendation Systems have become an important tool to cope with the information overload problem by acquiring information about the user behavior. However, the process of getting user personal data may vary in many different ways, and can be done implicitly (through actions) or explicitly (through rates). After tracing actions or getting rates of the user, Computational Recommendation Technologies use information filtering techniques to recommend items. In this paper we describe an approach to improve the recommendation quality in the first moments the user interacts with the system. The main idea is: (1) first of all, we describe the items with the general users opinion about them; and (2) after this, we use modal symbolic structures to save this content in the user profile. The proposed methodology outperforms, concerning the Find Good Items task measured by half-life utility metric, other approaches based on the following techniques: Cognitive Filtering, Social Filtering and hybrid methods.