Mining Stable Preferences: Adaptive Modality Decorrelation for
  Multimedia Recommendation
Jinghao Zhang Qiang Liu Shu Wu Liang Wang
Multimedia content is of predominance in the modern Web era. In real
scenarios, multiple modalities reveal different aspects of item attributes and
usually possess different importance to user purchase decisions. However, it is
difficult for models to figure out users' true preference towards different
modalities since there exists strong statistical correlation between
modalities. Even worse, the strong statistical correlation might mislead models
to learn the spurious preference towards inconsequential modalities. As a
result, when data (modal features) distribution shifts, the learned spurious
preference might not guarantee to be as effective on the inference set as on
the training set. We propose a novel MOdality DEcorrelating STable learning
framework, MODEST for brevity, to learn users' stable preference. Inspired by
sample re-weighting techniques, the proposed method aims to estimate a weight
for each item, such that the features from different modalities in the weighted
distribution are decorrelated. We adopt Hilbert Schmidt Independence Criterion
(HSIC) as independence testing measure which is a kernel-based method capable
of evaluating the correlation degree between two multi-dimensional and
non-linear variables. Our method could be served as a play-and-plug module for
existing multimedia recommendation backbones. Extensive experiments on four
public datasets and four state-of-the-art multimedia recommendation backbones
unequivocally show that our proposed method can improve the performances by a
large margin.