Comparing state-of-the-art collaborative filtering systems

Collaborative filtering aims at helping users find items they should appreciate from huge catalogues. In that field, we can distinguish user-based, item-based and model-based approaches. For each of them, many options play a crucial role for their performances, and in particular the similarity function defined between users or items, the number of neighbors considered for user- or item-based approaches, the number of clusters for model-based approaches using clustering, and the prediction function used. In this paper, we review the main collaborative filtering methods proposed in the litterature and compare them on the same widely used real dataset called MovieLens, and using the same widely used performance measure called Mean Absolute Error (MAE). This study thus allows us to highlight the advantages and drawbacks of each approach, and to propose some default options that we think should be used when using a given approach or designing a new one.