HybridSVD: When Collaborative Information is Not Enough

TL;DR

HybridSVD introduces a flexible hybrid collaborative filtering algorithm that integrates user and item side information, enhancing efficiency, cold start handling, and adaptability in dynamic environments.

Contribution

It extends PureSVD with a generalized SVD formulation, enabling incorporation of side information and efficient cold start solutions.

Findings

Outperforms similar hybrid models on diverse datasets.

Maintains high efficiency and quick recommendation generation.

Effectively handles cold start scenarios.

Abstract

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.