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.

我们提出了一种新的混合算法，可以在标准的协同过滤技术中加入用户和物品方面的信息。该算法利用奇异值分解的广义公式，增加了解的灵活性，并允许在其潜在空间上施加预期的结构。我们在各种数据集上评估了该方法，并展示了其在类似的混合模型中的优越性。