  In production applications of recommender systems, a continuous data flow is
employed to update models in real-time. Many recommender models often require
complete retraining to adapt to new data. In this work, we introduce a novel
collaborative filtering model for sequential problems known as Tucker
Integrator Recommender - TIRecA. TIRecA efficiently updates its parameters
using only the new data segment, allowing incremental addition of new users and
items to the recommender system. To demonstrate the effectiveness of the
proposed model, we conducted experiments on four publicly available datasets:
MovieLens 20M, Amazon Beauty, Amazon Toys and Games, and Steam. Our comparison
with general matrix and tensor-based baselines in terms of prediction quality
and computational time reveals that TIRecA achieves comparable quality to the
baseline methods, while being 10-20 times faster in training time.
