Algorithms that recommend items to users are known as recommender systems and have become an important part of online ecosystems. These systems calculate the similarity between given users or items based on the ratings they have received. Such similarities can be modeled as a graph where users or items (nodes) have connections (edges) to other users or items if the ratings they possess are alike. Using a graphical representation allows for the use of graph regularisers. These techniques predict ratings for a given user or item by using the ratings of users or items that are connected to the target in the graph. For the Total Variation Regulariser, much is still unclear concerning its performance when applied to Item KNN Collaborative Filtering. This report will show why Item Total Variation was unable to out-perform more traditional recommender systems in the conducted experiments. The findings indicate how this poor performance could in part be attributed to the choice in dataset. Additional results also convey that Total Variation may be predisposed to performing worse for a certain type of metric.