  Collaborative filtering (CF) has achieved great success in the field of
recommender systems. In recent years, many novel CF models, particularly those
based on deep learning or graph techniques, have been proposed for a variety of
recommendation tasks, such as rating prediction and item ranking. These newly
published models usually demonstrate their performance in comparison to
baselines or existing models in terms of accuracy improvements. However, others
have pointed out that many newly proposed models are not as strong as expected
and are outperformed by very simple baselines.
  This paper proposes a simple linear model based on Matrix Factorization (MF),
called UserReg, which regularizes users' latent representations with explicit
feedback information for rating prediction. We compare the effectiveness of
UserReg with three linear CF models that are widely-used as baselines, and with
a set of recently proposed complex models that are based on deep learning or
graph techniques. Experimental results show that UserReg achieves overall
better performance than the fine-tuned baselines considered and is highly
competitive when compared with other recently proposed models. We conclude that
UserReg can be used as a strong baseline for future CF research.
