Clustering recommendation systems: Performance and accuracy comparison

Nowadays, there is an increasing trend in movie creation and consumption through video-on-demand platforms like Netflix. It is vital for movie industry companies of this kind to adopt a recommendation-based marketing strategy, as improving customer experience directly contributes to increased profits. In our previous research work, we improved the efficiency of the standard k-NN (K-Nearest Neighbor)-based recommendation system in both accuracy and speed by first clustering customers based on their preferred genres. For the clustering step, we used the K-Means algorithm. In this research work, we expanded our experiments by using other than K-Means clustering algorithms to implement the clustering step of our proposed recommendation system. We used the DBSCAN Clustering algorithm, the Agglomerative Clustering, the Affinity Propagation, the Spectral Clustering, the Mean Shift, the Gaussian Mixture and the K-Means Split Merge algorithms. K-Means clustering is one of the most widely used partitional clustering algorithms in recommender systems due to its simplicity and low computational cost, despite some inherent limitations. Its popularity persists due to its simplicity and its minimal computational requirements. This research aims to improve K-Means-based recommender systems by experimenting with alternative clustering methods.