A Differentiable Ranking Metric Using Relaxed Sorting Operation for Top-K Recommender Systems

TL;DR

This paper introduces DRM, a differentiable ranking metric that enables end-to-end training of recommender systems by relaxing the non-differentiable sorting operation, leading to improved recommendation quality.

Contribution

It proposes a novel differentiable relaxation of ranking metrics, allowing seamless integration into model training and enhancing recommendation performance.

Findings

DRM improves recommendation accuracy over existing methods.

Joint learning with DRM significantly outperforms traditional ranking objectives.

Experiments on real datasets validate the effectiveness of the proposed approach.

Abstract

A recommender system generates personalized recommendations for a user by computing the preference score of items, sorting the items according to the score, and filtering top-K items with high scores. While sorting and ranking items are integral for this recommendation procedure, it is nontrivial to incorporate them in the process of end-to-end model training since sorting is nondifferentiable and hard to optimize with gradient descent. This incurs the inconsistency issue between existing learning objectives and ranking metrics of recommenders. In this work, we present DRM (differentiable ranking metric) that mitigates the inconsistency and improves recommendation performance by employing the differentiable relaxation of ranking metrics. Via experiments with several real-world datasets, we demonstrate that the joint learning of the DRM objective upon existing factor based recommenders significantly improves the quality of recommendations, in comparison with other state-of-the-art recommendation methods.