# HybridSVD: When Collaborative Information is Not Enough

arXiv: https://arxiv.org/abs/1802.06398 · HTML: https://mlsanity.com/paper/1802.06398/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2018.

## Built on by (most cited first, 8 of 8)

- [DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation](https://mlsanity.com/paper/2206.10848/) (2022)
- [Cold Item Integration in Deep Hybrid Recommenders via Tunable Stochastic Gates](https://mlsanity.com/paper/2112.07615/) (2021)
- [Unsupervised Proxy Selection for Session-based Recommender Systems](https://mlsanity.com/paper/2107.03564/) (2021)
- [Tensor-based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations](https://mlsanity.com/paper/2212.05720/) (2022)
- [Dynamic Modeling of User Preferences for Stable Recommendations](https://mlsanity.com/paper/2104.05047/) (2021)
- [Recommendation Is a Dish Better Served Warm](https://mlsanity.com/paper/2508.07856/) (2025)
- [Tensor-based Collaborative Filtering With Smooth Ratings Scale](https://mlsanity.com/paper/2205.05070/) (2022)
- [Online and Offline Evaluations of Collaborative Filtering and Content Based Recommender Systems](https://mlsanity.com/paper/2411.01354/) (2024)

## Abstract (arXiv)

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.