Technological advances in the last two decades alone have allowed a large portion of the population the ability to easily access information on a global scale. While often overlooked, recommender systems play a big role in the distribution of information to users. Since these algorithms have such a significant impact on the information users see, it is important to look at the negative effects they may have on the network. In this project, simulations were done through the Netlogo platform to observe how much impact recommender systems have on users in regards to polarizing opinions, and how the network may be affected when presented with highly controversial items. Ultimately it was found that, although the user-based collaborative recommender was more resistant, the presence of controversial items in recommender systems impacted all recommenders in a way that increased the tendency towards polarization in the network. By further investigating the effects of the content that is being distributed within the network, and improving our understanding of the causes of polarization in networks, we can hopefully help to improve the recommender algorithms and mitigate the unnecessary and problematic division within these networks.