Nowadays, Recommender Systems (RSs) have become the indispensable solution to the problem of information overload in many different fields (e-commerce, e-tourism, ...) because they offer their customers with more adapted and increasingly personalized services. In this context, collaborative filtering (CF) techniques are used by many RSs since they make it easier to provide recommendations of acceptable quality by leveraging the preferences of similar user communities. However, these types of techniques suffer from the problem of the sparsity of user evaluations, especially during the cold start phase. Indeed, the process of searching for similar neighbors may not be successful due to insufficient data in the matrix of user-item ratings (case of a new user or new item). To solve this kind of problem, we can find in the literature several solutions which allow to overcome the insufficiency of the data thanks to the social relations between the users. These solutions can provide good quality recommendations even when data is sparse because they permit for an estimation of the level of trust between users. This type of metric is often used in tourism domain to support the computation of similarity measures between users by producing valuable POI (point of interest) recommendations through a better trust-based neighborhood. However, the difficulty of obtaining explicit trust data from the social relationships between tourists leads researchers to infer this data implicitly from the user-item relationships (implicit trust). In this paper, we make a state of the art on CF techniques that can be utilized to reduce the data sparsity problem during the RSs cold start phase. Second, we propose a method that essentially relies on user trustworthiness inferred using scores computed from users’ ratings of items. Finally, we explain how these relationships deduced from existing social links between tourists might be employed as additional sources of information to minimize cold start problem.