# Metric Factorization: Recommendation beyond Matrix Factorization

arXiv: https://arxiv.org/abs/1802.04606 · HTML: https://mlsanity.com/paper/1802.04606/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2018.

## Built on by (most cited first, 5 of 5)

- [Recommender systems based on graph embedding techniques: A comprehensive review](https://mlsanity.com/paper/2109.09587/) (2021)
- [Hierarchical Latent Relation Modeling for Collaborative Metric Learning](https://mlsanity.com/paper/2108.04655/) (2021)
- [The Neural Metric Factorization for Computational Drug Repositioning](https://mlsanity.com/paper/2109.07690/) (2021)
- [SPR:Supervised Personalized Ranking Based on Prior Knowledge for Recommendation](https://mlsanity.com/paper/2207.03197/) (2022)
- [A Distance Metric Learning Model Based On Variational Information Bottleneck](https://mlsanity.com/paper/2403.02794/) (2024)

## Abstract (arXiv)

In the past decade, matrix factorization has been extensively researched and has become one of the most popular techniques for personalized recommendations. Nevertheless, the dot product adopted in matrix factorization based recommender models does not satisfy the inequality property, which may limit their expressiveness and lead to sub-optimal solutions. To overcome this problem, we propose a novel recommender technique dubbed as em Metric Factorization. We assume that users and items can be placed in a low dimensional space and their explicit closeness can be measured using Euclidean distance which satisfies the inequality property. To demonstrate its effectiveness, we further designed two variants of metric factorization with one for rating estimation and the other for personalized item ranking. Extensive experiments on a number of real-world datasets show that our approach outperforms existing state-of-the-art by a large margin on both rating prediction and item ranking tasks.