Metric Factorization: Recommendation beyond Matrix Factorization

TL;DR

This paper introduces Metric Factorization, a new recommendation method that uses Euclidean distance in a low-dimensional space to improve expressiveness over traditional matrix factorization, leading to better prediction and ranking results.

Contribution

The paper proposes Metric Factorization, a novel approach that replaces dot product with Euclidean distance, enhancing the expressiveness of recommender models.

Findings

Outperforms state-of-the-art methods in rating prediction

Achieves superior results in personalized item ranking

Demonstrates effectiveness on multiple real-world datasets

Abstract

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.