Recommender systems are mechanisms that filter information and predict a user’s preference to an item. Parallel implementations of recommender systems improve scalability issues and can be applied to internet-based companies having considerable impact on their profits. This paper implements a parallel version of the collaborative filtering algorithm Slope One, which has advantages such as its efficiency and the ability to update data dynamically. The presented version is parallely implemented with the use of the OpenMP API and its performance is evaluated on a multi-core system.