Metric Factorization: Recommendation beyond Matrix Factorization
This addresses a fundamental problem in recommendation systems for improving accuracy and expressiveness, though it is incremental as it builds on matrix factorization.
The paper tackles the limitation of matrix factorization in recommendation systems, where the dot product fails to satisfy inequality properties, by proposing Metric Factorization, which uses Euclidean distance to measure user-item closeness. Experiments on real-world datasets show it outperforms state-of-the-art methods by a large margin on rating prediction and item ranking tasks.
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.