# Cold-start recommendations in Collective Matrix Factorization

arXiv: https://arxiv.org/abs/1809.00366 · HTML: https://mlsanity.com/paper/1809.00366/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2018.

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

- [Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models](https://mlsanity.com/paper/2205.06130/) (2022)
- [Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations](https://mlsanity.com/paper/2204.00327/) (2022)
- [Private Matrix Factorization with Public Item Features](https://mlsanity.com/paper/2309.11516/) (2023)
- [A Scalable Recommendation Engine for New Users and Items](https://mlsanity.com/paper/2209.06128/) (2022)
- [PrivMVMF: Privacy-Preserving Multi-View Matrix Factorization for Recommender Systems](https://mlsanity.com/paper/2210.07775/) (2022)
- [Accelerated structured matrix factorization](https://mlsanity.com/paper/2212.06504/) (2022)
- [MFAI: A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Information](https://mlsanity.com/paper/2303.02566/) (2023)
- [Stochastic gradient descent estimation of generalized matrix factorization models with application to single-cell RNA sequencing data](https://mlsanity.com/paper/2412.20509/) (2024)

## Abstract (arXiv)

This work explores the ability of collective matrix factorization models in recommender systems to make predictions about users and items for which there is side information available but no feedback or interactions data, and proposes a new formulation with a faster cold-start prediction formula that can be used in real-time systems. While these cold-start recommendations are not as good as warm-start ones, they were found to be of better quality than non-personalized recommendations, and predictions about new users were found to be more reliable than those about new items. The formulation proposed here resulted in improved cold-start recommendations in many scenarios, at the expense of worse warm-start ones.