Dynamic Modeling of User Preferences for Stable Recommendations

TL;DR

This paper introduces a dynamic, incremental learning approach for recommender systems that models evolving user preferences to enhance recommendation stability without losing accuracy.

Contribution

It extends the PureSVD method to time-aware settings using a differential equation-based dynamic low-rank approximation, improving stability in recommendations.

Findings

Significantly improves recommendation stability over time.

Maintains high accuracy in top-$n$ recommendation tasks.

Extends existing matrix factorization techniques to dynamic, time-dependent data.

Abstract

In domains where users tend to develop long-term preferences that do not change too frequently, the stability of recommendations is an important factor of the perceived quality of a recommender system. In such cases, unstable recommendations may lead to poor personalization experience and distrust, driving users away from a recommendation service. We propose an incremental learning scheme that mitigates such problems through the dynamic modeling approach. It incorporates a generalized matrix form of a partial differential equation integrator that yields a dynamic low-rank approximation of time-dependent matrices representing user preferences. The scheme allows extending the famous PureSVD approach to time-aware settings and significantly improves its stability without sacrificing the accuracy in standard top-$n$ recommendations tasks.