HybridSVD for feture selection

In this research, we propose a new hybrid algorithm for collaborative filtering that incorporates both user and item side information. Our approach extends the PureSVD method by utilizing a generalized formulation of singular value decomposition, allowing for increased flexibility and structure within the model's latent space. This approach retains the unique advantages of PureSVD, such as an efficient optimization procedure and simplified hyper-parameter tuning. Additionally, our algorithm enables quick recommendation generation, even in dynamic online environments. We evaluate the effectiveness of our approach on a diverse range of datasets and demonstrate its superiority over other similar hybrid models. Our algorithm also provides an efficient solution for the cold start scenario. Overall, our work presents a promising new approach for collaborative filtering with hybrid user and item side information, offering improved performance and versatility for recommendation systems.