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.

本文首次将变分信息瓶颈与度量学习模型相结合，提出了一种新的度量学习模型 VIB-DML（Variational Information Bottleneck Distance Metric Learning），用于评分预测，通过解耦潜在空间特征向量，限制潜在空间特征向量的互信息以提高模型的鲁棒性并满足欧几里得距离的假设。实验证明，VIB-DML 具有出色的泛化能力，与通用度量学习模型 MetricF 相比，预测误差降低了 7.29%。最后，本文通过实验证明了 VIBDML 的强鲁棒性。