A Distance Metric Learning Model Based On Variational Information
  Bottleneck
YaoDan Zhang Zidong Wang Ru Jia Ru Li
In recent years, personalized recommendation technology has flourished and
become one of the hot research directions. The matrix factorization model and
the metric learning model which proposed successively have been widely studied
and applied. The latter uses the Euclidean distance instead of the dot product
used by the former to measure the latent space vector. While avoiding the
shortcomings of the dot product, the assumption of Euclidean distance is
neglected, resulting in limited recommendation quality of the model. In order
to solve this problem, this paper combines the Variationl Information
Bottleneck with metric learning model for the first time, and proposes a new
metric learning model VIB-DML (Variational Information Bottleneck Distance
Metric Learning) for rating prediction, which limits the mutual information of
the latent space feature vector to improve the robustness of the model and
satisfiy the assumption of Euclidean distance by decoupling the latent space
feature vector. In this paper, the experimental results are compared with the
root mean square error (RMSE) on the three public datasets. The results show
that the generalization ability of VIB-DML is excellent. Compared with the
general metric learning model MetricF, the prediction error is reduced by
7.29%. Finally, the paper proves the strong robustness of VIBDML through
experiments.