Clustering-based factorized collaborative filtering

Factorized collaborative models show a promising accuracy and scalability in recommendation systems. They employ the latent collaborative information of users and items to achieve higher accuracy of recommendation. In this paper, we propose a new approach to improve the accuracy of two well-known, highly scalable factorized models: SVD++ and Asymmetric-SVD++. These are cutting-edge factorized models that have played a key role in the Netflix prize winner's solution. We first employ collaborative information to categorize the users and items. We then discover the shared interests between these categories. Including this new information, we extend these cutting-edge models regarding two main goals: 1) to improve their recommendation accuracies; 2) to keep the extended models still scalable. Finally, we evaluate our proposed models on two recommendation datasets: MovieLens100k, and Netflix. Our experiment shows that adding the shared interests among categories into these models improves their accuracy while maintaining scalability.