During the last years, mobile devices allow incorporating users’ location and movements into recommendations to potentially suggest most valuable information. In this context, this paper presents a hybrid recommender algorithm that combines users’ location and preferences and the content of the items located close to such users. This algorithm also includes a way of providing implicit ratings considering the users’ movements after receiving recommendations, aimed at measuring the users’ interest for the recommended items. Conducted experiments measure the effectiveness and the efficiency of our recommender algorithm, as well as the impact of implicit ratings.