HybridSVD: When Collaborative Information is Not Enough
This work addresses the cold start and dynamic recommendation challenges in recommender systems, but it is incremental as it builds on existing PureSVD methods.
The paper tackles the problem of incorporating user and item side information into collaborative filtering by proposing HybridSVD, a hybrid algorithm that extends PureSVD, and shows its superiority over similar hybrid models on diverse datasets.
We propose a new hybrid algorithm that allows incorporating both user and item side information within the standard collaborative filtering technique. One of its key features is that it naturally extends a simple PureSVD approach and inherits its unique advantages, such as highly efficient Lanczos-based optimization procedure, simplified hyper-parameter tuning and a quick folding-in computation for generating recommendations instantly even in highly dynamic online environments. The algorithm utilizes a generalized formulation of the singular value decomposition, which adds flexibility to the solution and allows imposing the desired structure on its latent space. Conveniently, the resulting model also admits an efficient and straightforward solution for the cold start scenario. We evaluate our approach on a diverse set of datasets and show its superiority over similar classes of hybrid models.