  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.
