A recommendation algorithm aims to predict the quality of a user's future interaction with certain items based on their previous interactions. As research progresses, these algorithms are becoming increasingly more complicated with the use of machine learning and neural networks. This paper looks into a more simple solution. The recommendation domain can be represented as a graph, meaning different graph regularization techniques can be used to solve the same problem. After running experiments comparing the Item-Item Tikhonov Regularizer and the Sobolev Regularizer to a baseline, the item-item standard Collaborative Filtering method, it is clear that the Graph Regularization techniques outperform the baseline. Given that it has been shown that Collaborative Filtering is a relatively competitive method in this field, outperforming it means that Graph Regularizers are a viable and potentially competitive method for solving the recommender problem.