Online social media platforms fundamentally impact information transmission in our societies. In order to understand phenomena such as political polarization, misinformation spreading or even large-scale collective action, it is important to understand what drives the spread of information in online platforms. The impact of algorithmic recommendations in online information diffusion remains poorly understood. Here, we present a preliminary model to test how different forms of content recommendation might impact information diffusion patterns, in heterogeneous populations where groups might be connected with arbitrary homophily levels.We observe that content-based recommenders can increase the size of information cascades and affect the possibility that minority groups trigger large cascade events.